Research ArticleA Hybrid Multiple Criteria Group Decision-Making Approachfor Green Supplier Selection in the TFT-LCD Industry

Che-Wei Tsui and Ue-Pyng Wen

Department of Industrial Engineering and Engineering Management National Tsing Hua University 101 Section 2Kuang-Fu Road Hsinchu 30013 Taiwan

Correspondence should be addressed to Che-Wei Tsui d9734804oznthuedutw

Received 12 May 2014 Revised 19 July 2014 Accepted 20 July 2014 Published 12 August 2014

Academic Editor Ren-Jieh Kuo

Copyright copy 2014 C-W Tsui and U-P Wen This is an open access article distributed under the Creative Commons AttributionLicense which permits unrestricted use distribution and reproduction in any medium provided the original work is properlycited

The awareness of the need for environmental protection is increasing throughout the worldThe focuses of green supplier selectionare on considering environmental criteria and strengthening the competitiveness of the entire supply chain The purpose of thisstudy is to develop a green supplier selection procedure for the thin film transistor liquid crystal display (TFT-LCD) industry usingpolarizer suppliers as an example First a decision framework for green supplier selection is developed based on literatures and thesupplier audit forms provided by an anonymous flat panel display manufacturer in Taiwan Then a hybrid multiple criteria groupdecision-making (MCGDM) method is proposed based on analytic hierarchy process (AHP) entropy elimination and choiceexpressing the reality III (ELECTRE III) and the linear assignment method to assist the manufacturer in choosing among fourpolarizer suppliersThe final ranking results for green supplier selection and different opinions from each department are providedAn improvement report is suggested to enhance suppliersrsquo performance For the evaluation procedure most managers emphasizethe importance of current capability and the capability of research and development Furthermore we found that the subsidiarysupplier should improve quality control competence immediately to be considered as the potential candidate of primary supplier

1 Introduction

With the increasing concern about environmental protectionmany enterprises have taken more and more responsibilitiesfor their products to reduce pollution and damage in ourenvironment Another driver is the environment-protectionconsciousness from consumers who urge the companies toenhance their ability to make their products greener There-fore green supply chain management (GSCM) is consideredas a systematic and integrated approach for companies tomaintain their sustainability and competitiveness in themarket Among the issues of GSCM green supplier selectionis a crucial issue in improving environmental performanceTherefore this study focuses on how to develop an effectivegreen supplier selection procedure and facilitate the improve-ment of the suppliersrsquo performance

In Taiwan the optoelectronics industry is the core topromote economic growth and it is greatly influencedby international regulations especially those in Europe

The Photonics Industry and Technology Development Asso-ciation (PIDA) an organization established by entrepreneursand academics in Taiwan reported that the highest outputvalue of the optoelectronics industry was the thin filmtransistor liquid crystal display (TFT-LCD) industry Theoutput value of the TFT-LCD industry was approximatelyUS$355 billion in 2013 which was approximately 28 ofthe global output value [1] Hung [2] noted that in additionto the well-known competitors in Japan and Korea TFT-LCD industries in China have emerged due to governmentsupport cheap labor and an extensive market ThereforeTaiwanese manufacturers face challenges of making effortsto provide green products that conform to environmentalregulations and meet the customersrsquo requests Consequentlygreen supplier selection has emerged as an important issuefor companies to sustain their market

Many companies have continually integrated environ-mental criteria into the supplier audit forms in the pastfew years and they have typically used a simple additive

Hindawi Publishing CorporationMathematical Problems in EngineeringVolume 2014 Article ID 709872 13 pageshttpdxdoiorg1011552014709872

2 Mathematical Problems in Engineering

method to rank the suppliers However an effective methodis necessary to obtain more information for improving thesuppliersrsquo performance Therefore the purpose of this studyis to develop an effective and systematic evaluation procedureto support the green supplier selection process for theTFT-LCD industry We attempt to address three researchquestions First we construct a decision framework for greensupplier selection suitable for the TFT-LCD industry Secondwe design and propose an effective decision method forranking and selecting the suppliers for the case-studied TFT-LCD panel manufacturer Finally we investigate ways tocontinuously improve suppliersrsquo performance

In recent years within the extensive literatures on tradi-tional supplier evaluation and selection [3 4] issues relatedto the decision-making tools for green supplier selectionhave gained increasing interest among researchers [5ndash9] Leeet al [10] divided the green supplier selection model intotwo stages The first stage is to differentiate the traditionalcriteria from the green criteria and the second stage is toevaluate green suppliers for a high-tech industry using afuzzy-extended analytic hierarchy process (AHP) methodwhich is relatively easier Tuzkaya et al [11] proposed ahybrid fuzzy multicriteria decision approach integrating thefuzzy analytic network process (ANP) and a fuzzy outrankingmethod to evaluate suppliersrsquo environmental performanceKuo et al [12] integrated artificial neural networks and twomultiattribute decision analysis methods (data envelopmentanalysis and ANP) for green supplier selection Zhu et al[13] integrated an ANP method into a green suppliermanagement process model based on portfolio analysisBuyukozkan [14] applied fuzzy AHP and fuzzy axiomaticdesign with group decision-making method to select themost appropriate green supplier Buyukozkan and Cifci [15]combined a fuzzy decision-making trial and evaluation labo-ratory (DEMATEL) a fuzzy ANP and a fuzzy technique fororder preference by similarity to an ideal solution (TOPSIS)method to evaluate green suppliers for the case of FordOtosan Company Hsu et al [16] used a hybrid multiple cri-teria decision-making (MCDM) model to evaluate problemsrelated to recycled materials vendors The model proposedby Hsu et al [16] also considered environmental criteriaThe results showed the best vendor and revealed the criterianeeded for improvement Govindan et al [17] used linguisticscale to determine the expertsrsquo subjective weights and appliedfuzzy TOPSIS method for suppliersrsquo ranking Shen et al [18]proposed fuzzy multiple criteria approach on the basis of thefuzzy TOPSIS method for green supplier evaluation Yazdani[19] combined fuzzy AHP and fuzzy TOPSIS method forgreen supplier selection in the automobile manufacturingindustry Chen and Freeman [20] proposed an integratedMCDM approach combining AHP entropy and TOPSIS torank green suppliers Kannan et al [21] considered linguisticexpression for criteria weights and rank green suppliers usingfuzzy TOPSIS method Zhao and Guo [22] applied fuzzyentropy-TOPSIS approach to select green supplier of thermalpower equipment in China

According to the previous studies AHP or ANP is usuallyadopted to obtain the subjective weights of each criterionThe main difference between AHP and ANP is that AHP

assumes the independent hierarchies and elements in a deci-sion structure while ANP allows dependence and feedbackcharacteristics among hierarchies and elements In this studywe will develop a decision framework based on literaturereview and supplier audit forms of each department Inpractice each department usually designs relevant items ontheir supplier audit forms and separately evaluates greensuppliersrsquo performance Consequently the AHP method ismore suitable for green supplier selection in the TFT-LCDindustry at the current stage Chen and Freeman [20] appliedAHP with entropy weights to determine the compromisedweights of criteria for green supplier selection Based oninformation theory the entropymethod is a usefulmethod toexplore the objective weights determined by a decisionmaker(DM) and this can avoid overly subjective weightsThereforethis study considers both subjective weights and objectiveweights of criteria using the AHP-entropy method In thisstudy the combined criteria weights along with the surveydata for empirical analysis are more suitable for real-worldapplications

The issue of green supplier selection is a typical MCDMproblem [5] Few studies have been published about greensupplier selection using the MCDM method especially theoutranking method [6] The main feature of the existingstudies is the comparison of all feasible alternatives or actions[23] The goals of decision-making tools have shifted fromfinding the right solution to a problem to trying to providesupport to the DMs to allow them to advance in the decisionprocess [24] Therefore instead of providing a strictly rank-based result it is even more important to present a resultthat expresses the DMsrsquo opinion To accomplish this goal weadopted the elimination and choice expressing the reality III(ELECTRE III) method which takes complex informationfrom the traditional and environmental appraisal and ranksthe various project options considered The ELECTRE IIImethod has been shown to be useful when involving manyDMs and in the cases where the outcomes of the variousalternatives remain uncertain to some degree [25] It has thecharacteristics of considering the situations of incompara-bility and indifference which causes the ranking result totruly reflect the DMsrsquo opinions For green supplier selectionmany criteria either qualitative or quantitative need to betaken into consideration and the ELECTRE III method canalso address these criteria simultaneously Moreover greensupplier selection involves the consideration of multiplefeasible alternatives The ELECTRE III method provides notonly the ranking results but also assistance to suppliers forimprovement

A further point needs to be considered In real-worldapplications green supplier selection is generally a groupdecision-making process but most of the previous studieshave discussed green supplier selection from the viewpointof a single DM A common way to address group decision-making problems is to aggregate the weight of the criteriaof each DM using the geometric mean [29 30] Howeverfew studies have concentrated on aggregating the DMsrsquoevaluation results while their opinions are important anddifferent in practice Then the managers can find a cure forthe problemThus green supplier selection is further counted

Mathematical Problems in Engineering 3

as a multiple criteria group decision-making (MCGDM)problem Bernardo and Blin [31] developed the linear assign-ment method (LAM) for aggregating a set of ranking resultsLAM can aggregate the DMsrsquo ranking results and it alsoconsiders the DMsrsquo weights based on their authority Thisstudy uses LAM to aggregate the different ranking results ofeach DM based on the results of the ELECTRE III method

The rest of this paper is organized as follows Section 2is the review of recent researches on green supplier selectionand the development of a framework for the process ofgreen supplier selection based on the reviewed literaturesand supplier audit forms offered by an anonymous TFT-LCD manufacturer (the case-studied company in this paper)in Taiwan Section 3 is the introduction of the proposedmethod which combines AHP the entropy method ELEC-TRE III and LAM Section 4 is the application of theproposed method to a green supplier selection case and theresults Further discussions and managerial implications arepresented in Section 4 In Section 5 concluding remarks areprovided

2 Green Supplier Selection Criteria

Due to economic globalization green procurement canenhance firm competitiveness in GSCM and green supplierselection has an important role in green procurement In thissection we will introduce the proposed framework for greensupplier selection based on literatures and supplier auditforms fromY-TECH (for confidentiality a pseudonym is usedthroughout the study) awell-knownTFT-LCDmanufacturerin Taiwan and the main case studied in the present researchIn this section we review the published studies and comparethem with the supplier audit forms

According to supplier audit forms from each departmentthe proposed decision framework can be divided into threeaspects environmental factors (environment safety andhealth [ESH] department and green product management[GPM] department) enterprise operating (material manage-ment [MM] department and supplier quality management[SQM] department) and strategic technology and develop-ment (research and development [RD] department and SQMdepartment) Some published papers have considered theselection issue with only environmental performance [32ndash36] A few studies have focused on environmental perfor-mance for supplier selection [37 38] For developing thedecision framework we review the recent published studiesof green supplier selection [10 12 14 26ndash28] to check therelated criteria for each aspect

Lee et al [10] suggested that the framework of greensupplier selection for high-tech industry should includesix aspects and 23 criteria but they presented a limiteddiscussion of several important and traditional criteria suchas delivery price and financial stability Kuo et al [12]determined the green supplier selection criteria based onliteratures and a Delphi expert questionnaire presentingsix aspects and 24 criteria sent to ten experts Tseng andChiu [27] proposed 18 GSCM criteria through comprehen-sive discussion and literatures for a printed circuit board

Table 1 The criteria of the green supplier selection

Aspects Criteria Relevantreferences

Environmentalfactor

1198921 safety and health [17]1198922 strategic fit [10]

1198923 environmental control [14]

1198924 recovery [26]

Enterpriseoperating

1198925 price [12]

1198926 finance stability [10]

1198927 quality control [12]

1198928 out-of-control management [12]

1198929 delivery [14]

11989210 flexibility [27]

11989211 maintenance and support [27]

Strategictechnology anddevelopment

11989212 current capability [28]

11989213 RampD capability [10]

11989214 compatibility across levels [14]11989215 information share [27]

manufacturer Using the framework proposed by Tseng andChiursquos [27] we included supplier relation closeness in ourproposed framework to carry out the suppliersrsquo connectionto green supplier selection Supplier relation closeness is animportant criterion in the supplier audit forms It containsthe implication of communication channels informationsharing instant feedback and resource-sharing platformamong the suppliers of the supply chain Lin [26] examinedthe cause and effect relationships among the eight criteriato evaluate GSCM practices using fuzzy DEMATEL Becauseall of the criteria are related to GSCM practices we adoptedsome environmental criteria in our proposed frameworkBuyukozkan [14] described the framework of green supplierselection based on the literatures about automotive industryincluding three aspects and 12 criteria In sum most of thepublished studies have developed a framework for greensupplier selection based on previous studies and interviewspossibly because interviewing is the most convenient way tolink the reviewed studies with the real cases In this study wedevelop a suitable decision framework of three aspects and 15criteria for green supplier selection as shown in Table 1

3 The Proposed Hybrid MCGDM Method

To construct a systematic evaluation to support the processof green supplier selection we propose a hybrid MCGDMmethod of four stages The first stage evaluates the subjectivecriteria weights of each DM based on the AHP methodAt the second stage we consider entropy to evaluate theobjective criteria weights of each DM At the third stage weuse ELECTRE III to rank and improve the suppliers For thefourth stage LAM is used to integrate the ranking results ofeach DM based on the result of the third stage Suppose aset of alternatives is (119886

1 119886

119894 119886

119896 119886

119898) and the criteria

are defined as (1198921 119892

119895 119892

119899)There are a number of DMs

4 Mathematical Problems in Engineering

denoted as (1198631 119863

119905 119863

119897)The weights of each criterion

for DMs are 119908119905119895and 119909

119894119895 representing the performance value

of alternative 119886119894under criterion 119892

119895 The AHP method the

entropy method the ELECTRE III method and LAM aredescribed as follows

31 The AHP Method for Determining the Subjective WeightsSaaty [39 40] used AHP to determine the DMrsquos subjectiveweight in a hierarchical structure All decision problemscan be considered as having a hierarchical structure Theimportant advantage of AHP is that a decision problemcan be decomposed into a number of subsystems [41] anda complicated decision problem can be systematized to ahierarchical structure The hierarchical structure starts witha goal and then moves to the intermediate level containingaspects and criteria and the alternatives are at the bottom Inthis study the first level is the goal for the overall objectivesof the problem for green supplier selection The second levelis decomposed into three aspects and each aspect can alsobe decomposed into several criteria as shown in Table 1If the criteria at the low levels exist the criteria at thelower levels can be generated based on the same principleAfter constructing the hierarchical structure of the problemfor green supplier selection the next step is to survey thecomparative weights among the aspectscriteria with a 1ndash9 point scale ranging from equally important to extremelyimportant to build the comparison matrices The subjectiveweight of criterion 119895 can be found with respect to themaximum eigenvalue of the comparative matrix for DM 119905which can be expressed as 1199081015840

119905119895 Then we can further check

the consistency index (CI) and consistency ratio (CR) ofeach comparison matrix via the following equation

CI =120582max minus 119899

119899 minus 1

CR = CIRI

(1)

The CI and CR values should not be larger than 01 for aconfident result If CI or CR value is larger than 01 we willinterview manager or assistant manager for the inconsistentcomparative weight and further adjust the value

32 The Entropy Method for Determining Objective WeightsThe entropymethod is used to evaluate the objective weightswhich is an important concept in information theory [42]This concept measures the expected information content ofa specific message [43] If the entropy measure is larger theinformation contained is less Therefore we can decide theobjective weight of each criterion based on the informationcontained in the decision matrix First a decision matrixis a matrix whose elements express the performance of analternative with respect to a criterion Then the decisionmatrix can be normalized in a linear manner which can bedescribed as

119903119894119895=

119909119894119895

max119895119909119894119895

119894 = 1 2 119898 119895 = 1 2 119899 (2)

where

119909119894119895=

119909119894119895 if 119892

119895is a benefit criterion

1

119909119894119895

if 119892119895is a cost criterion

(3)

Then the degree of diversification of the normalized decisionmatrix can be considered using the following equation

120575119895= 1 + 119887

119898

sum

119894=1

119903119894119895ln 119903119894119895 119895 = 1 2 119899 (4)

where 119887 = 1 ln119898 and 119887 is a constantFinally the objective weight of criterion 119895 can be obtained

as follows

11990810158401015840

119895=

120575119895

sum119899

119894=1120575119895

119895 = 1 2 119899 (5)

In this study according to the subjective weights and objec-tive weights the compromised weights of criterion 119895 for DM119905 can be expressed as

119908119905119895=

1199081015840

11990511989511990810158401015840

119895

sum119899

119895=1119908101584011990511989511990810158401015840119895

119905 = 1 2 119897 119895 = 1 2 119899 (6)

33 The ELECTRE III Method to Evaluate the Performance ofSuppliers The ELECTRE III method proposed by Roy [44]was designed to address inaccurate imprecise uncertain orill-determined data such as qualitative data ELECTRE IIInot only evaluates the best choice but also presents a specificranking result and leaves the final selection to the DMsFor the issue of green supplier selection ELECTRE III ischosen as a suitable method and it involves aspects that areoften neglected by othermethods for yielding relatively stableresults [45] Before we illustrate ELECTRE III we definethree threshold values that establish the DMrsquos preferencefor each criterion 119895 including indifference preference andveto thresholds The indifference threshold (119902

119895) indicates a

gap between the evaluation scores that are still compatiblewith a situation of indifference The preference threshold(119901119895) expresses the minimum difference between the values

of criterion 119895 to which the DM attributes significance interms of strict preference The veto threshold (V

119895) expresses

the minimum difference between the values of criterion 119895

beyond which the DM believes the gap between the twoscores cannot be compensated by the good performance ofthe other criteria In this study these thresholds for eachcriterion were set by interviews with DMs Then outrankingdegree can be defined as that alternative 119894 outranks alternative119896 forDM 119905 which can be calculated by the following equation

119862119905(119886119894 119886119896) =

1

119908

119899

sum

119895=1

119908119905119895119888119895(119886119894 119886119896) (7)

Mathematical Problems in Engineering 5

where

119908 =

119899

sum

119895=1

119908119905119895

119888119895(119886119894 119886119896) =

1 if 119909119894119895+ 119902119895ge 119909119896119895

0 if 119909119894119895+ 119901119895le 119909119896119895

119901119895+ 119909119894119895minus 119909119896119895

119901119895minus 119902119895

otherwise

(8)

We then consider rejecting degree which means alternative 119894is dominated by alternative 119896 under criterion 119895 as follows

119889119895(119886119894 119886119896)

=

1 if 119892119895(119886119896) ge 119892119895(119886119894) + V119895

0 if 119892119895(119886119896) le 119892119895(119886119894) + 119901119895

119892119895(119886119896) minus 119892119895(119886119894) minus 119901119895

V119895minus 119901119895

otherwise

(9)

Therefore the overall outranking degree of DM 119905 can beconsidered with outranking degree and rejecting degreewhich is called overall outranking degree as indicated in thefollowing equation

119878119905(119886119894 119886119896)

=

119862119905(119886119894 119886119896) if 119889

119895(119886119894 119886119896)

le 119862119905(119886119894 119886119896) forall119895

119862119905(119886119894 119886119896)

times prod

119895isin119869(119886119894 119886119896)

1 minus 119889119895(119886119894 119886119896)

1 minus 119862119905(119886119894 119886119896) otherwise

(10)

where 119869(119886119894 119886119896) is the set of criteria such that 119889

119895(119886119894 119886119896) gt

119862119905(119886119894 119886119896)

The final step is to exploit the model and produce aranking result from the overall outranking degrees The gen-eral approach for exploitation is to construct two preorders1198851and 119885

2using the descending and ascending distillation

process and then combine these two to produce a partialpreorder 119885 = 119885

1cap 1198852 The descending process is to clas-

sify the alternatives from the best to the worst while theascending process is from the worst to the best [46ndash48]

34 The LAM for Integrating Ranking Results of DMs Ber-nardo and Blin [31] developed LAM to transform individualranking result into an overall ranking result Therefore LAMcan integrate the ranking result of each DM into an overallranking result We can apply this simple method to sum theranking score from each DM and rank the overall score fromthe lowest score to the highest score [43] If an alternativeis ranked as the first order the alternative gets one pointIf an alternative is ranked as the second it gets two pointsand so on On the other hand LAM also considers the

weights of the DMs to integrate the overall ranking resultIn our case study we consider the weights of DMs basedon their positions For example the RampD department hasapproximately 27 of the decision-making power for greensupplier selection so the weight of the RampD departmentis 027 in the procedure of green supplier selection LAMcan be illustrated as follows First an overall ranking matrixis established as Π = [120587

119894119904] where 120587

119894119904represents the

weighted frequency that 119886119894is ranked the 119904th with the different

weights of the DMs Then depending on the above overallranking matrix we formulate a linear programming modelto optimize the permutationThe linear programmingmodeluses the binary decision variables 120593

119894119904 The decision variable

120593119894119904

= 1 means 119886119894is assigned to the 119904th overall rank and

120593119894119904= 0 otherwise Obviously one alternative can be assigned

to only one rank and one rank can only be assigned by onealternative Therefore the linear programming model can bewritten as follows

max119898

sum

119894=1

119898

sum

119891=1

120587119894119904120593119894119904

subject to119898

sum

119894=1

120593119894119904= 1 119904 = 1 2 119898

119898

sum

119891=1

120593119894119904= 1 119894 = 1 2 119898

120593119894119904isin 0 1 119894 119904 = 1 2 119898

(11)

4 Application Case

Y-TECH is the first manufacturer in Taiwan to mass-produceTFT-LCD panels and it is also one of the top five TFT-LCD panel manufacturers in the world By utilizing thedata coming from Y-TECH we aimed at providing a usefulevaluation method with continuous improvement Becauseof the global competitive market the well-known TFT-LCD manufacturers hope to develop and invest in theirsuppliers especially for some of the important componentsfor TFT-LCD panel such as the color filter back-lightunit and polarizer Because good suppliers may affect themanufacturerrsquos performance and competitiveness and theentire supply chain in this study we demonstrate the supplierevaluation procedure and provide information about supplierassistance for TFT-LCD manufacturers

41 Study Background In the polarizer industry the polar-izer manufacturer of the highest market share is in SouthKorea while the Japanese polarizer manufacturer has thesecond highestmarket share In recent years Y-TECHmainlydepended on materials and technical support from Japanesepolarizer manufacturers Also the Korean polarizer manu-facturers have aggressively intervened in the supply chainof Taiwanese TFT-LCD industry Y-TECH has started toreconsider its supplier evaluation procedure and involvedthe group of GPM department in auditing and evaluatingits suppliers Figure 1 shows the new decision procedure for

6 Mathematical Problems in Engineering

Quarterly business reviewQuarterly quality review

Annual and audit

Qualified greensuppliers lists

MM material managementSQM supplier quality managementESH environment safety healthHR human resourceRD research and designGPM green product management

SQMESHHR

SQM (36)MM (27)RD (27)GPM (10)

SQMESHHR

1st tier2nd tierfreeze

New green suppliers

Survey and audit

MMSQM

Figure 1 The decision procedure of green supplier selection in the case study

green supplier selection at Y-TECH Before being a formalpolarizer supplier in Y-TECH the candidate companiesshould pass a two-stage assessment At the first stage anew polarizer supplier should accept a preliminary surveyby the teams of MM and SQM departments and then theteams of SQM ESH and HR departments further executethe preliminary audit for the polarizer suppliers A qualifiedpolarizer manufacturera should pass at least 70 of the itemson all supplier audit forms At the second stage the formalevaluation is conducted by the teams of SQM MM RD andGPM departments After the second-stage assessment thequalified polarizer suppliers will be ranked in the first tierif they pass more than 80 of the items on supplier auditforms and the suppliers passing 70ndash80 of the items willbe ranked in the second tier If the qualified polarizer supplierpasses fewer than 70 of the items it will be regarded asdisqualified All of the formal suppliers are reviewed regularlyby the teams of SQM ESH and HR departments Further-more IS9001 ISO14001 and OHSAS18001 certifications arenot necessary in the evaluation procedure because Y-TECHwill assist their formal suppliers to apply for environmentalcertifications within a limited timeThe first-stage assessmentis the preliminary survey and audit so we focus on applyingthe proposed MCGDM method to the formal evaluation forthe assessment at the second stage

In this case study although four qualified polarizer sup-pliers for Y-TECH outperformed in all other suppliers for thefirst-stage survey our focus will be on supplier 3 because it isa subsidiary company for Y-TECH In fact strategic purchaseis an important strategy to enhance competitiveness in theTFT-LCD industry Therefore the decision procedure is notonly to select the subsidiary into the supply chain but alsoto provide improvement reports for the subsidiary companyAdditionally it is expected that the subsidiary supplier willbe the primary one In addition to the three aspects andfifty criteria (as mentioned in Section 2) we also surveyed12 managers on the expert committee In practice the SQMdepartment holds 36 of the decision-making power theMM and RampD departments 27 and the GPM department10 to select suitable suppliers during the decision-makingprocess

42 Empirical Results First we designed a questionnaire for12 experts to measure the comparative weights between theaspects and criteria Table 2 presents the subjective weights ofeach criterion for DMs based on AHPThemanagers of MMRD GPM and SQM departments are 119863

1 1198632 1198633 1198634 1198635

1198636 1198637 1198638 1198639 and 119863

10 11986311 11986312 respectively Then we

focused on the criteria weights that were 15 more thanthe average weight (01) Notice that many managers think

Mathematical Problems in Engineering 7

Table 2 Subjective weights of each criterion for DMs

Dept MM RD GPM SQMCriteria 119863

11198632

1198633

1198634

1198635

1198636

1198637

1198638

1198639

11986310

11986311

11986312

1198921

0014 0192 0009 0177 0349 0007 0106 0111 0120 0006 0117 00221198922

0014 0021 0033 0421 0050 0007 0032 0111 0120 0042 0290 01191198923

0036 0043 0012 0083 0174 0037 0048 0056 0033 0016 0036 00441198924

0007 0006 0002 0028 0174 0007 0010 0056 0007 0002 0012 00061198925

0405 0006 0048 0016 0009 0131 0093 0093 0004 0019 0011 00171198926

0081 0001 0048 0005 0003 0026 0031 0019 0001 0004 0002 00171198927

0039 0043 0048 0071 0057 0131 0197 0056 0025 0161 0029 00071198928

0039 0009 0048 0010 0019 0026 0049 0056 0008 0032 0010 00021198929

0064 0009 0045 0006 0015 0071 0073 0079 0010 0007 0016 000211989210

0016 0009 0045 0003 0013 0071 0031 0020 0021 0026 0005 000211989211

0007 0002 0006 0001 0004 0014 0019 0013 0003 0044 0018 000111989212

0174 0480 0421 0026 0083 0196 0078 0243 0270 0013 0085 047611989213

0058 0096 0070 0130 0028 0196 0155 0049 0270 0093 0256 015911989214

0035 0069 0143 0019 0006 0020 0039 0021 0081 0089 0028 010611989215

0012 0014 0020 0004 0017 0059 0039 0021 0027 0445 0085 0021

highly of the current capability (11989212) and RampD capability

(11989213) The managers of MM department generally place

importance on the current capability (11989212) and the weights

of 1198631 1198632 and 119863

3are 0174 0480 and 0421 respectively

Moreover MM department is also concerned with the price(1198925) and the average weight is 0153 It is generally agreed

that the managers of RampD department put emphasis onenvironmental factors because of their relevance to productdesign Taking strategic fit (119892

2) for example suppliers should

review environment-related substance list regularly whendeveloping a green productThe average weights of safety andhealth (119892

1) strategic fit (119892

2) environmental control (119892

3) and

recovery (1198924) are 0263 0236 0129 and 0101 respectively

and the importance of environmental factors is over 70 ofthe total weight

In particular GPM department does not give moreweight to environmental factors and the average weight ofenvironmental factors is only 0054 The most likely reasonis that the GPM department holds less decision-makingpower GPM department is concerned with quality control(1198927) current capability (119892

12) and RampD capability (119892

13) and

the average weights are 0102 0196 and 0168 respectivelyOverall themanagers ofGPMdepartment are still concernedwith the current capability (119892

12) and theRampDcapability (119892

13)

Finally SQM department focuses on the strategic fit (1198922)

current capability (11989212) RampD capability (119892

13) and informa-

tion share (11989215) and the average weights of these factors

are 0150 0191 0169 and 0184 respectively Based on ourexpectation SQM department is not significantly concernedwith quality control (119892

7) or other criteria of the enterprise

operation and the average weight for quality control (1198927)

is only 0066 However SQM department emphasizes theimportance of information share (119892

15) Information share

brings the benefit of enhancing the competitiveness of theentire supply chain especially technology development Insum SQM department focuses on the criteria of strategic

Table 3 Objective weights of each criterion for individual DM

Criteria Objective weights1198921

00721198922

00761198923

00671198924

00661198925

00641198926

00751198927

00811198928

00781198929

008011989210

006411989211

004711989212

005011989213

007011989214

005611989215

0054

technology and developmentThus to avoid overly subjectiveweights we include the entropy method in the proposedevaluation procedure using (2) through (5) The objectiveweights of the criteria are shown in Table 3 while Table 4indicates the compromised weights of the criteria for eachDM by (6)

After the compromised criteria weights of the DMsare decided we further apply the criteria weights and theperformance to the ELECTRE III method (7) through (10)to evaluate the four polarizer suppliers For each DM theoutranking degree of any two alternatives can be calculatedby (7) and the results are shown in Table 5 For examplefor manager 119863

1 the outranking degree of alternative 1 to

alternative 3 is 0743 and the outranking degree of alternative3 to alternative 1 is 0493 Obviously manager 119863

1believes

8 Mathematical Problems in Engineering

Table 4 Compromised weights of each criterion for DMs

Dept MM RD GPM SQMCriteria 119863

11198632

1198633

1198634

1198635

1198636

1198637

1198638

1198639

11986310

11986311

11986312

1198921

0016 0230 0011 0176 0367 0008 0109 0121 0135 0007 0123 00271198922

0016 0027 0042 0441 0055 0008 0035 0127 0142 0051 0322 01521198923

0037 0048 0013 0077 0170 0038 0046 0057 0034 0017 0035 00501198924

0007 0007 0002 0025 0168 0007 0009 0056 0007 0002 0012 00071198925

0399 0006 0051 0014 0008 0128 0085 0090 0004 0020 0010 00181198926

0093 0001 0060 0005 0003 0030 0033 0022 0001 0005 0002 00211198927

0049 0058 0065 0079 0067 0162 0229 0068 0032 0209 0034 00101198928

0047 0012 0062 0011 0022 0031 0055 0066 0010 0040 0011 00031198929

0079 0012 0060 0007 0018 0087 0084 0095 0012 0009 0019 000311989210

0016 0010 0048 0003 0012 0069 0028 0019 0021 0027 0005 000211989211

0005 0002 0005 0001 0003 0010 0013 0009 0002 0033 0012 000111989212

0134 0400 0350 0018 0061 0149 0056 0183 0211 0010 0062 040111989213

0062 0112 0081 0126 0029 0209 0156 0052 0295 0104 0262 018711989214

0030 0064 0133 0015 0005 0017 0031 0018 0071 008 0023 010011989215

0010 0013 0018 0003 0013 0049 0030 0017 0023 0385 0067 0019

Table 5 Outranking degrees of each DM

1198631

1198632

1198633

1198861

1198862

1198863

1198864

1198861

1198862

1198863

1198864

1198861

1198862

1198863

1198864

1198861

0 0588 0743 0867 1198861

0 0771 0578 0868 1198861

0 0688 0547 08441198862

0521 0 0598 0633 1198862

0310 0 0374 0425 1198862

0409 0 0442 05291198863

0493 0538 0 0762 1198863

0715 0779 0 0803 1198863

0669 0719 0 07631198864

0591 0619 0479 0 1198864

0783 0787 0476 0 1198864

0771 0735 0488 01198634

1198635

1198636

a1 a2 a3 a4 a1 a2 a3 a4 a1 a2 a3 a41198861

0 0785 0839 0862 1198861

0 0790 0804 0858 1198861

0 0635 0685 07701198862

0306 0 0413 0440 1198862

0378 0 0444 0463 1198862

0477 0 0625 06441198863

0673 0750 0 0780 1198863

0650 0678 0 0703 1198863

0531 0632 0 07111198864

0740 0782 0694 0 1198864

0745 0832 0643 0 1198864

0665 0674 0565 01198637

1198638

1198639

1198861

1198862

1198863

1198864

1198861

1198862

1198863

1198864

1198861

1198862

1198863

1198864

1198861

0 0642 0762 0767 1198861

0 0736 0706 0853 1198861

0 0762 0664 08641198862

0473 0 0602 0634 1198862

0404 0 0492 0518 1198862

0320 0 0465 05201198863

0534 0604 0 0667 1198863

0608 0646 0 0712 1198863

0634 0788 0 08091198864

0687 0663 0628 0 1198864

0734 0777 0588 0 1198864

0702 0735 0532 011986310

11986311

11986312

1198861

1198862

1198863

1198864

1198861

1198862

1198863

1198864

1198861

1198862

1198863

1198864

1198861

0 0471 0557 0585 1198861

0 0762 0754 0841 1198861

0 0765 0562 08861198862

0607 0 0671 0661 1198862

0326 0 0491 0509 1198862

0313 0 0387 04551198863

0660 0704 0 0663 1198863

0630 0776 0 0791 1198863

0697 0798 0 08261198864

0780 0713 0706 0 1198864

0704 0761 0644 0 1198864

0749 0762 0453 0

that alternative 1 is superior to alternative 3 However acharacteristic of the ELECTRE III method is that a penaltyis set if the alternative performs the worst for a criterionThus we can use (9) to calculate the rejecting degree dueto the penalty and the overall outranking degree can beobtained by (10) as shown in Table 6 As another examplefor the 119863

6manager the outranking degree of alternative 3

to alternative 2 is 0632 but the overall outranking degreeof alternative 3 to alternative 2 is 0000 This means thatalternative 3 performs too poorly to be accepted based onsome criteria The results presented in Table 7 show thatalternative 3 performs too poorly on quality control (119892

7) and

delivery (1198929) The rejecting degrees for quality control (119892

7)

and delivery (1198929) are 1000 and 0778 respectively which

Mathematical Problems in Engineering 9

Table 6 Overall outranking degree of each DM

1198631

1198632

1198633

1198861

1198862

1198863

1198864

1198861

1198862

1198863

1198864

1198861

1198862

1198863

1198864

1198861

0 0588 0743 0867 1198861

0 0771 0564 0868 1198861

0 0688 0246 08441198862

0342 0 0331 0633 1198862

0026 0 0 0148 1198862

0115 0 0 04151198863

0273 0334 0 0762 1198863

0715 0779 0 0803 1198863

0669 0719 0 07631198864

0563 0 0384 0 1198864

0783 0787 0361 0 1198864

0771 0735 0207 01198634

1198635

1198636

1198861

1198862

1198863

1198864

1198861

1198862

1198863

1198864

1198861

1198862

1198863

1198864

1198861

0 0785 0839 0862 1198861

0 0790 0804 0858 1198861

0 0245 0483 07461198862

0 0 0 0 1198862

0 0 0 0037 1198862

0090 0 0 06441198863

0673 0750 0 0780 1198863

0413 0468 0 0526 1198863

0091 0 0 05461198864

0633 0782 0694 0 1198864

0745 0832 0643 0 1198864

0 0459 0541 01198637

1198638

1198639

1198861

1198862

1198863

1198864

1198861

1198862

1198863

1198864

1198861

1198862

1198863

1198864

1198861

0 0413 0762 0767 1198861

0 0736 0706 0853 1198861

0 0762 0664 08641198862

0054 0 0336 0634 1198862

0037 0 0 0163 1198862

0005 0 0 01661198863

0094 0 0 0445 1198863

0344 0406 0 0550 1198863

0372 0788 0 08091198864

0 0437 0628 0 1198864

0734 0774 0588 0 1198864

0 0735 0474 011986310

11986311

11986312

1198861

1198862

1198863

1198864

1198861

1198862

1198863

1198864

1198861

1198862

1198863

1198864

1198861

0 0072 0123 0 1198861

0 0762 0754 0841 1198861

0 0765 0509 08861198862

0607 0 0671 0661 1198862

0 0 0 0 1198862

0034 0 0 02611198863

0660 0529 0 0663 1198863

0365 0776 0 0791 1198863

0697 0798 0 08261198864

0780 0713 0706 0 1198864

0 0761 0644 0 1198864

0664 0762 0315 0

Table 7 Rejecting degree for alternative 3 to alternative 2 from 1198896manager

1198921

1198922

1198923

1198924

1198925

1198926

1198927

1198928

1198929

11989210

11989211

11989212

11989213

11989214

11989215

Reject degree 0000 0000 0000 0583 0407 0000 1000 0288 0778 0000 0000 0000 0000 0000 0000

are obviously larger than the outranking degree of 0632 Inthe next section we further discuss which criteria shouldbe improved immediately Accordingly we can establish thedescending and ascending distillation for each DM and theranking results are shown in Table 8

Finally LAM is applied to establish linear programmingfor integrating the opinions of each DM According tothe decision-making power of each department the overallranking matrix is shown as follows

Π =[[[

[

06850 01950 00000 01200

00400 01350 00850 07400

02350 04700 02825 00125

00400 02000 06325 01275

]]]

]

(12)

Therefore we can formulate a linear programming modelusing (11) and obtain the solution which indicates that theoverall ranking result is 119886

1≻ 1198863≻ 1198864≻ 1198862The best supplier is

supplier 1 and the subsidiary company of Y-TECH is secondonly to supplier 1 Supplier 4 is ranked third and supplier 2performs the worst in this evaluation

43 Discussion and Implications In this study we aim topropose a hybridMCGDMmethod to evaluate and assist sup-pliers At the first stage we use the AHP method to measurethe subjective weights of the criteria for each DM Based onthe results of the AHP method it is obvious that most ofthe managers value the importance of current capability andRampD capability and this suggests that Y-TECH places a greatemphasis on technology development In the FPD industrydue to the rapid development of new technology many well-known enterprises regularly release new products to marketevery year A challenge for Y-TECH includes the develop-ment of other technologies such as organic light-emittingdisplays (OLED) For example the OLED panel led to thereduction of the demand for small and medium TFT-LCDdisplays in the global market in 2010 Therefore to enhancecompetitiveness TFT-LCD manufactures connecting withthe suppliers in the entire supply chain should continuouslydevelop new technology Quality control is an importantcriterion secondary only to technological capability At theearly stages of applying a new technology to a new productthe yield rate of the new product is low Consequently it isimportant not only to develop the new technology but alsoto maintain a highly qualified product Currently because

10 Mathematical Problems in Engineering

Table 8 Initial evaluation of individual DM

Dept MM RD GPM SQMRanking 119889

11198892

1198893

1198894

1198895

1198896

1198897

1198898

1198899

11988910

11988911

11988912

1st 1198861

1198861 1198863

1198863

1198861

1198861

1198861

1198861

1198861

1198861

1198862 1198863 1198864

1198861

1198861 1198863

2nd 1198862 1198863

1198861

1198863

1198864

1198862

1198862

1198864

1198863

1198863 1198864

3rd 1198864

1198864

1198864

1198863

1198863

1198863 1198864

1198863

1198864

1198864

4th 1198864

1198862

1198862

1198862

1198862

1198864

1198862

1198862

1198861

1198862

1198862

Table 9 The worst performing criteria of each alternative

1198861

1198862

1198863

1198864

1198631

mdash mdash mdash 1198925

1198632

mdash 11989212

mdash mdash1198633

mdash 11989212

mdash mdash1198634

mdash 1198922

mdash mdash1198635

mdash 1198921

mdash mdash1198636

mdash 11989212

g7 11989213

1198637

mdash mdash g7 11989213

1198638

mdash 11989212

mdash mdash1198639

mdash 11989212

mdash 11989213

11986310

g15 mdash mdash mdash11986311

mdash 1198922

mdash 11989213

11986312

mdash 11989212

mdash mdash

GPM department processes less decision-making power forgreen supplier selection it is obvious that environmentalfactors are not significantly considered in the evaluationprocedure with the exception that RampD department shoulddesign new products and the related materials must be listedfor MM department Then MM department can place anorder and ask the suppliers to follow the list SQMdepartmentis concerned with technological capability as well as infor-mation share Vertical integration is an important enterprisepolicy for the TFT-LCD industry and strategic purchase isa method for the implementation of vertical integration Asmentioned before the key components of TFT-LCD panelsthat are produced in-house have gradually increased year byyear so information share is also an important criterion forcultivating the core capability of enterprise and establishinga competitive advantage Furthermore all of the criteriaplay important roles in the evaluation procedure for greensupplier selection If the proposed hybrid MCGDM methodonly adopts the AHP method it will most likely lead to theoversubjective weighting of the criteria Therefore we applythe entropy method to consider the weights of the criteriasimultaneously

ELECTRE III is applied to evaluate the suppliers Duringthe evaluation procedure we compare the outranking degreeand the overall outranking degree and then decide whichcriterion should be improved immediately for each alterna-tive as shown in Table 9 If the overall outranking degreeof a criterion decreases to zero (comparing to outrankingdegree) this criterion should be improved immediately Forthe best supplier supplier 1 (119886

1) the 119863

10manager suggests

that alternative 1 should strengthen communication with Y-TECH by taking actions such as improving cooperation forthe development of new products and instant market infor-mation share (119892

15) In this case study one of the most impor-

tant aspects is how to provide assistance to the subsidiarycompany (119886

3) of Y-TECH Table 9 indicates that if supplier

3 improves quality control (1198927) for polarizer production its

performance will be improved significantly Generally it iscontended that managers should be concerned about criteriarelated to their job description Due to limited enterpriseresources the order of executing enterprise improvementis also an important issue Following the useful proposedmethod the discovery of the criteria that need to be improvedwill become easy

Different from many previous studies this study doesnot integrate the weights of the criteria at the first stageIn fact green supplier selection is a group decision-makingproblem and integrating the weights would also lead tothe loss of information Therefore we further consider theranking results of each department as shown in Table 10Although these four polarizermanufacturers are the suppliersof Y-TECH there are some differences among the rankingresults of each department especially for suppliers 2 and 4 Asmentioned before RampD department placed great emphasison the ldquogreenrdquo criteria and supplier 4rsquos good performance onthese criteria is the reason for the different ranking resultsConsequently in this study we proposed a novel method andviewpoint for green supplier selection and we suggested thatthe weights of the criteria should not be integrated at thefirst stage because further discussions and communicationare necessary to include the different opinions from eachdepartment Additionally we compare the results of theproposed method with the ones implemented by Y-TECHthat is simple additive method in Table 10 Both results showthat supplier 1 has the best performance and is superior tosupplier 3 Supplier 4 is in the third place while supplier2 is the worst Furthermore within the extensive literatureon green supplier selection methods the TOPSIS methodwas widely applied to evaluate green suppliersrsquo performance[15 17ndash22] Thus we further compare the TOPSIS methodwith the proposedmethod and the comparison is establishedon the basis of AHP-entropy criteria weights In the TOPSISmethod with AHP-entropy weights supplier 3 is in the firstplace and superior to supplier 1 and supplier 4 and supplier2 have the worst performance in this evaluation Althoughthe ranking results among the proposed method and othertwo methods are similar the emphasis of this study is onhow to improve the suppliersrsquo performance in the TFT-LCD industry More importantly the results of the proposed

Mathematical Problems in Engineering 11

Table 10 Comparison of ranking results for the proposed method with other methods

Ranking MM RD GPM SQM The proposed method Current methodlowast AHP-entropy TOPSIS1st 119886

31198861

1198861

1198861

1198861

1198861

1198863

2nd 1198861

1198864

1198862

1198863

1198863

1198863

1198861

3rd 1198864

1198863

1198863

1198864

1198864

1198864

1198864

4th 1198862

1198862

1198864

1198862

1198862

1198862

1198862

lowast

The current evaluation method in Y-TECH is simple additive method

method show that each department has its different opinionsand viewpoints of improvement Each department of theTFT-LCDmanufacturers can assist their suppliers within thecontext of business perspectives

5 Conclusion

In this study we propose a hybrid MCGDM method forgreen supplier selection for Y-TECH Our method focuseson not only the selection or ranking but also the providenceof assistance to suppliers to strengthen their competitivenessThis method also emphasizes continuous improvement Dueto the challenges and difficulties the TFT-LCD industriesworldwide face we intend to establish a systematic selectionprocedure for TFT-LCD manufacturers We developed adecision framework for green supplier selection based onliteratures and the supplier audit forms from Y-TECH Weconsidered AHP and the entropy method to measure thecompromised weights of the criteria that involve subjectiveand objective opinions simultaneously After that the ELEC-TRE III method provides polarizer manufacturersrsquo rankingresults for executive managers in addition to improvementreports The acquisition of key components used for in-house production of TFT-LCD panels has gradually becomea strategy for cost reduction and the enhancement of corecapability Thus TFT-LCD manufacturers should strengthentheir cooperation with their suppliers

Based on the results of the ELECTRE III method wecan compare the differences between the outranking degreeand the overall outranking degree Then we further considerwhich criteria should be improved immediately especially forthe suppliers of strategic purchases The results of this studyprovide an important reference for subsidiary companiesthat are seeking to be primary polarizer suppliers Finallydiffering from many of the previous studies we do notintegrate the weights of the criteria with regard to a singleDM in this study In other worlds we integrate the rankingresults of eachDM instead of theweights of the criteria In theproposed evaluation procedure LAM places more emphasison the discussion and communication among a group ofDMs which is consistent with the practical case

In summary the result of this study can be regardedas a problem-solving process for green supplier selectionRegardless of the proposed method or analytic procedurewe aim at helping TFT-LCD manufacturers to establish asystematic and useful evaluation procedure via the currentstudy Because all of the supplier audit forms are in Microsoft

Excel 2010 spreadsheets the proposedmethod is easy to applyusing the same file Executive managers can consider theperformance of all suppliers and refer to the results of theproposed method Three main contributions are made inthis study First depending on the literatures and supplieraudit forms we develop a decision framework for the TFT-LCD industry Second we propose the useful systematicand flexible hybrid MCGDMmethod (based on the businessprocess) and improve the current evaluation procedureThird we place more emphasis on improving suppliers toenhance the competitiveness of entire supply chain especiallythe subsidiary company In practice green supplier selectionis evaluated by each department separately and all criteria areregarded as independent In recent years increasing attentionhas been given to the interdependent relationships amongthe criteria [15 16 26 49] Therefore future studies arenecessary to clarify the interrelations among the criteriaWe hope that more detailed information influencing therelationships between the criteria can be provided in futureresearch Consequently it would bemore efficient to improvethe supplier selection process for the entire supply chain inthe future work

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper

Acknowledgments

This research was partially supported by the National ScienceCouncil of the Republic of China (Grant NSC 102-2221-E-007-084) The authors would like to thank the editorand anonymous referees for their valuable comments andsuggestions

References

[1] Taiwan Stock Exchange ldquoIndustry analysis the flat paneldisplay Industryrdquo February 2013 httpwwwtwsecomtwchproductspublicationessayphp

[2] S Hung ldquoCompetitive strategies for Taiwanrsquos thin film tran-sistor-liquid crystal display (TFT-LCD) industryrdquoTechnology inSociety vol 28 no 3 pp 349ndash361 2006

[3] W Ho X Xu and P K Dey ldquoMulti-criteria decision makingapproaches for supplier evaluation and selection a literaturereviewrdquo European Journal of Operational Research vol 202 no1 pp 16ndash24 2010

12 Mathematical Problems in Engineering

[4] J Chai J N K Liu and E W T Ngai ldquoApplication of decision-making techniques in supplier selection a systematic review ofliteraturerdquo Expert Systems with Applications vol 40 no 10 pp3872ndash3885 2013

[5] K Govindan S Rajendran J Sarkis and P Murugesan ldquoMulticriteria decision making approaches for green supplier eval-uation and selection a literature reviewrdquo Journal of CleanerProduction 2013

[6] M Herva and E Roca ldquoReview of combined approaches andmulti-criteria analysis for corporate environmental evaluationrdquoJournal of Cleaner Production vol 39 pp 355ndash371 2013

[7] S Seuring ldquoA review of modeling approaches for sustainablesupply chain managementrdquo Decision Support Systems vol 54no 4 pp 1513ndash1520 2013

[8] M Igarashi L De Boer and A M Fet ldquoWhat is required forgreener supplier selection A literature review and conceptualmodel developmentrdquo Journal of Purchasing and SupplyManage-ment vol 19 no 4 pp 247ndash263 2013

[9] A Genovese S C Koh G Bruno and E Esposito ldquoGreenersupplier selection state of the art and some empirical evidencerdquoInternational Journal of Production Research vol 51 no 10 pp2868ndash2886 2013

[10] A H I Lee H Y Kang C F Hsu and H C Hung ldquoA greensupplier selectionmodel for high-tech industryrdquo Expert Systemswith Applications vol 36 no 4 pp 7917ndash7927 2009

[11] G Tuzkaya A Ozgen D Ozgen and U R Tuzkaya ldquoEnvi-ronmental performance evaluation of suppliers a hybrid fuzzymulti-criteria decision approachrdquo International Journal of Envi-ronmental Science and Technology vol 6 no 3 pp 477ndash4902009

[12] R J Kuo Y C Wang and F C Tien ldquoIntegration of artificialneural network and MADA methods for green supplier selec-tionrdquo Journal of Cleaner Production vol 18 no 12 pp 1161ndash11702010

[13] Q Zhu Y Dou and J Sarkis ldquoA portfolio-based analysis forgreen supplier management using the analytical network pro-cessrdquo Supply Chain Management vol 15 no 4 pp 306ndash3192010

[14] G Buyukozkan ldquoAn integrated fuzzy multi-criteria groupdecision-making approach for green supplier evaluationrdquo Inter-national Journal of Production Research vol 50 no 11 pp 2892ndash2909 2012

[15] G Buyukozkan and G Cifci ldquoA novel hybrid MCDM approachbased on fuzzy DEMATEL fuzzy ANP and fuzzy TOPSIS toevaluate green suppliersrdquo Expert Systems with Applications vol39 no 3 pp 3000ndash3011 2012

[16] C H Hsu F KWang and G H Tzeng ldquoThe best vendor selec-tion for conducting the recycled material based on a hybridMCDM model combining DANP with VIKORrdquo ResourcesConservation and Recycling vol 66 pp 95ndash111 2012

[17] K Govindan R Khodaverdi and A Jafarian ldquoA fuzzy multicriteria approach for measuring sustainability performance ofa supplier based on triple bottom line approachrdquo Journal ofCleaner Production vol 47 pp 345ndash354 2013

[18] L Shen L Olfat K Govindan R Khodaverdi and A DiabatldquoA fuzzy multi criteria approach for evaluating green supplierrsquosperformance in green supply chain with linguistic preferencesrdquoResources Conservation and Recycling vol 74 pp 170ndash179 2013

[19] M Yazdani ldquoAn integrated MCDM approach to green supplierselectionrdquo International Journal of Industrial Engineering Com-putations vol 5 no 3 pp 443ndash458 2014

[20] T Chen and J Freeman ldquoUsing AHP-Entropy weight and TOP-SIS methodology in green supplier selectionrdquo in Proceedings ofthe European Operations Management Association Conference(EurOMA rsquo14) Palermo Italy June 2014

[21] D Kannan A B L D S Jabbour andC J C Jabbour ldquoSelectinggreen suppliers based on GSCM practices using fuzzy TOPSISapplied to a Brazilian electronics companyrdquo European Journal ofOperational Research vol 233 no 2 pp 432ndash447 2014

[22] H Zhao and S Guo ldquoSelecting green supplier of thermalpower equipment by using a Hybrid MCDM Method forsustainabilityrdquo Sustainability vol 6 no 1 pp 217ndash235 2014

[23] J Figueira S Greco and M EhrgottMultiple Criteria DecisionAnalysis State of the Art Surveys Springer Science BostonMass USA 2005

[24] B Roy ldquoThe outranking approach and the foundations ofELECTREmethodsrdquoTheory and Decision vol 31 no 1 pp 49ndash73 1991

[25] J Hokkanen and P Salminen ldquoChoosing a solid waste man-agement systemusingmulticriteria decision analysisrdquoEuropeanJournal of Operational Research vol 98 no 1 pp 19ndash36 1997

[26] R J Lin ldquoUsing fuzzy DEMATEL to evaluate the green supplychain management practicesrdquo Journal of Cleaner Productionvol 40 pp 32ndash39 2013

[27] M L Tseng and A S F Chiu ldquoEvaluating firmrsquos green supplychain management in linguistic preferencesrdquo Journal of CleanerProduction vol 40 pp 22ndash31 2013

[28] G Buyukozkan and G Cifci ldquoA novel fuzzy mulati -criteriadecision framework for sustainable supplier selection withincomplete informationrdquo Computers in Industry vol 62 no 2pp 164ndash174 2011

[29] M T Escobar and J M Moreno-Jimenez ldquoAggregation of indi-vidual preference structures in AHP-group decision makingrdquoGroup Decision andNegotiation vol 16 no 4 pp 287ndash301 2007

[30] V Tsyganok ldquoInvestigation of the aggregation effectiveness ofexpert estimates obtained by the pairwise comparisonmethodrdquoMathematical and ComputerModelling vol 52 no 3-4 pp 538ndash544 2010

[31] J J Bernardo and J M Blin ldquoA programming model ofconsumer choice among multi-attributed brandsrdquo Journal ofConsumer Research vol 4 no 2 pp 111ndash118 1977

[32] P K Humphreys Y K Wong and F T S Chan ldquoIntegratingenvironmental criteria into the supplier selection processrdquoJournal of Materials Processing Technology vol 138 no 1ndash3 pp349ndash356 2003

[33] C Bai and J Sarkis ldquoIntegrating sustainability into supplierselection with grey system and rough set methodologiesrdquoInternational Journal of Production Economics vol 124 no 1 pp252ndash264 2010

[34] X Fu Q Zhu and J Sarkis ldquoEvaluating green supplier devel-opment programs at a telecommunications systems providerrdquoInternational Journal of Production Economics vol 140 no 1pp 357ndash367 2012

[35] A Amindoust S Ahmed A Saghafinia and A BahreininejadldquoSustainable supplier selection a ranking model based on fuzzyinference systemrdquoApplied Soft Computing Journal vol 12 no 6pp 1668ndash1677 2012

[36] K Shaw R Shankar S S Yadav and L S Thakur ldquoSupplierselection using fuzzy AHP and fuzzy multi-objective linearprogramming for developing low carbon supply chainrdquo ExpertSystems with Applications vol 39 no 9 pp 8182ndash8192 2012

Mathematical Problems in Engineering 13

[37] A Awasthi S S Chauhan and S K Goyal ldquoA fuzzy multi-criteria approach for evaluating environmental performance ofsuppliersrdquo International Journal of Production Economics vol126 no 2 pp 370ndash378 2010

[38] T Y Chiou H K Chan F Lettice and S H Chung ldquoThe influ-ence of greening the suppliers and green innovation on envi-ronmental performance and competitive advantage in TaiwanrdquoTransportation Research Part E Logistics and TransportationReview vol 47 no 6 pp 822ndash836 2011

[39] T L Saaty ldquoA scaling method for priorities in hierarchicalstructuresrdquo Journal of Mathematical Psychology vol 15 no 3pp 234ndash281 1977

[40] T L Saaty The Analytic Hierarchy Process McGraw-Hill NewYork NY USA 1980

[41] CMacharis J Springael K De Brucker andA Verbeke ldquoPRO-METHEE and AHP the design of operational synergies inmulticriteria analysis Strengthening PROMETHEE with ideasof AHPrdquo European Journal of Operational Research vol 153 no2 pp 307ndash317 2004

[42] M Zeleny Multiple Criteria Decision Making McGraw-HillNew York NY USA 1982

[43] C L Hwang and K Yoon Multiple Attribute Decision MakingMethods and Applications A State-of-the-Art Survey SpringerNew York NY USA 1981

[44] B Roy ldquoELECTRE III un algorithme de classements fondesur une repre sentation floue des preferences en preferences enpresence de criteres multiplesrdquo Cahiers du Centre drsquoEtudes deRecherche Operationnelle vol 20 no 1 pp 3ndash24 1978

[45] P Vincke Multiple Criteria Decision-Aid John Wiley amp SonsChichester UK 1992

[46] M Rogers and M Bruen ldquoUsing ELECTRE III to chooseroute for Dublin Port motorwayrdquo Journal of TransportationEngineering vol 126 no 4 pp 313ndash323 2000

[47] M Rogers and M Bruen ELECTRE and Decision SupportMethods and Applications in Engineering and InfrastructureInvestment Kluwer Academic Publishers Boston Mass USA2000

[48] V Belton and T J Stewart Multiple Criteria Decision AnalysisAn Integrated Approach Kluwer Academic Publishers BostonMass USA 2002

[49] B Chang C W Chang and C H Wu ldquoFuzzy DEMATELmethod for developing supplier selection criteriardquo Expert Sys-tems with Applications vol 38 no 3 pp 1850ndash1858 2011

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Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

MathematicsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Mathematical Problems in Engineering

Hindawi Publishing Corporationhttpwwwhindawicom

Differential EquationsInternational Journal of

Volume 2014

Applied MathematicsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Probability and StatisticsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Mathematical PhysicsAdvances in

Complex AnalysisJournal of

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2 Mathematical Problems in Engineering

method to rank the suppliers However an effective methodis necessary to obtain more information for improving thesuppliersrsquo performance Therefore the purpose of this studyis to develop an effective and systematic evaluation procedureto support the green supplier selection process for theTFT-LCD industry We attempt to address three researchquestions First we construct a decision framework for greensupplier selection suitable for the TFT-LCD industry Secondwe design and propose an effective decision method forranking and selecting the suppliers for the case-studied TFT-LCD panel manufacturer Finally we investigate ways tocontinuously improve suppliersrsquo performance

In recent years within the extensive literatures on tradi-tional supplier evaluation and selection [3 4] issues relatedto the decision-making tools for green supplier selectionhave gained increasing interest among researchers [5ndash9] Leeet al [10] divided the green supplier selection model intotwo stages The first stage is to differentiate the traditionalcriteria from the green criteria and the second stage is toevaluate green suppliers for a high-tech industry using afuzzy-extended analytic hierarchy process (AHP) methodwhich is relatively easier Tuzkaya et al [11] proposed ahybrid fuzzy multicriteria decision approach integrating thefuzzy analytic network process (ANP) and a fuzzy outrankingmethod to evaluate suppliersrsquo environmental performanceKuo et al [12] integrated artificial neural networks and twomultiattribute decision analysis methods (data envelopmentanalysis and ANP) for green supplier selection Zhu et al[13] integrated an ANP method into a green suppliermanagement process model based on portfolio analysisBuyukozkan [14] applied fuzzy AHP and fuzzy axiomaticdesign with group decision-making method to select themost appropriate green supplier Buyukozkan and Cifci [15]combined a fuzzy decision-making trial and evaluation labo-ratory (DEMATEL) a fuzzy ANP and a fuzzy technique fororder preference by similarity to an ideal solution (TOPSIS)method to evaluate green suppliers for the case of FordOtosan Company Hsu et al [16] used a hybrid multiple cri-teria decision-making (MCDM) model to evaluate problemsrelated to recycled materials vendors The model proposedby Hsu et al [16] also considered environmental criteriaThe results showed the best vendor and revealed the criterianeeded for improvement Govindan et al [17] used linguisticscale to determine the expertsrsquo subjective weights and appliedfuzzy TOPSIS method for suppliersrsquo ranking Shen et al [18]proposed fuzzy multiple criteria approach on the basis of thefuzzy TOPSIS method for green supplier evaluation Yazdani[19] combined fuzzy AHP and fuzzy TOPSIS method forgreen supplier selection in the automobile manufacturingindustry Chen and Freeman [20] proposed an integratedMCDM approach combining AHP entropy and TOPSIS torank green suppliers Kannan et al [21] considered linguisticexpression for criteria weights and rank green suppliers usingfuzzy TOPSIS method Zhao and Guo [22] applied fuzzyentropy-TOPSIS approach to select green supplier of thermalpower equipment in China

According to the previous studies AHP or ANP is usuallyadopted to obtain the subjective weights of each criterionThe main difference between AHP and ANP is that AHP

assumes the independent hierarchies and elements in a deci-sion structure while ANP allows dependence and feedbackcharacteristics among hierarchies and elements In this studywe will develop a decision framework based on literaturereview and supplier audit forms of each department Inpractice each department usually designs relevant items ontheir supplier audit forms and separately evaluates greensuppliersrsquo performance Consequently the AHP method ismore suitable for green supplier selection in the TFT-LCDindustry at the current stage Chen and Freeman [20] appliedAHP with entropy weights to determine the compromisedweights of criteria for green supplier selection Based oninformation theory the entropymethod is a usefulmethod toexplore the objective weights determined by a decisionmaker(DM) and this can avoid overly subjective weightsThereforethis study considers both subjective weights and objectiveweights of criteria using the AHP-entropy method In thisstudy the combined criteria weights along with the surveydata for empirical analysis are more suitable for real-worldapplications

The issue of green supplier selection is a typical MCDMproblem [5] Few studies have been published about greensupplier selection using the MCDM method especially theoutranking method [6] The main feature of the existingstudies is the comparison of all feasible alternatives or actions[23] The goals of decision-making tools have shifted fromfinding the right solution to a problem to trying to providesupport to the DMs to allow them to advance in the decisionprocess [24] Therefore instead of providing a strictly rank-based result it is even more important to present a resultthat expresses the DMsrsquo opinion To accomplish this goal weadopted the elimination and choice expressing the reality III(ELECTRE III) method which takes complex informationfrom the traditional and environmental appraisal and ranksthe various project options considered The ELECTRE IIImethod has been shown to be useful when involving manyDMs and in the cases where the outcomes of the variousalternatives remain uncertain to some degree [25] It has thecharacteristics of considering the situations of incompara-bility and indifference which causes the ranking result totruly reflect the DMsrsquo opinions For green supplier selectionmany criteria either qualitative or quantitative need to betaken into consideration and the ELECTRE III method canalso address these criteria simultaneously Moreover greensupplier selection involves the consideration of multiplefeasible alternatives The ELECTRE III method provides notonly the ranking results but also assistance to suppliers forimprovement

A further point needs to be considered In real-worldapplications green supplier selection is generally a groupdecision-making process but most of the previous studieshave discussed green supplier selection from the viewpointof a single DM A common way to address group decision-making problems is to aggregate the weight of the criteriaof each DM using the geometric mean [29 30] Howeverfew studies have concentrated on aggregating the DMsrsquoevaluation results while their opinions are important anddifferent in practice Then the managers can find a cure forthe problemThus green supplier selection is further counted

Mathematical Problems in Engineering 3

as a multiple criteria group decision-making (MCGDM)problem Bernardo and Blin [31] developed the linear assign-ment method (LAM) for aggregating a set of ranking resultsLAM can aggregate the DMsrsquo ranking results and it alsoconsiders the DMsrsquo weights based on their authority Thisstudy uses LAM to aggregate the different ranking results ofeach DM based on the results of the ELECTRE III method

The rest of this paper is organized as follows Section 2is the review of recent researches on green supplier selectionand the development of a framework for the process ofgreen supplier selection based on the reviewed literaturesand supplier audit forms offered by an anonymous TFT-LCD manufacturer (the case-studied company in this paper)in Taiwan Section 3 is the introduction of the proposedmethod which combines AHP the entropy method ELEC-TRE III and LAM Section 4 is the application of theproposed method to a green supplier selection case and theresults Further discussions and managerial implications arepresented in Section 4 In Section 5 concluding remarks areprovided

2 Green Supplier Selection Criteria

Due to economic globalization green procurement canenhance firm competitiveness in GSCM and green supplierselection has an important role in green procurement In thissection we will introduce the proposed framework for greensupplier selection based on literatures and supplier auditforms fromY-TECH (for confidentiality a pseudonym is usedthroughout the study) awell-knownTFT-LCDmanufacturerin Taiwan and the main case studied in the present researchIn this section we review the published studies and comparethem with the supplier audit forms

According to supplier audit forms from each departmentthe proposed decision framework can be divided into threeaspects environmental factors (environment safety andhealth [ESH] department and green product management[GPM] department) enterprise operating (material manage-ment [MM] department and supplier quality management[SQM] department) and strategic technology and develop-ment (research and development [RD] department and SQMdepartment) Some published papers have considered theselection issue with only environmental performance [32ndash36] A few studies have focused on environmental perfor-mance for supplier selection [37 38] For developing thedecision framework we review the recent published studiesof green supplier selection [10 12 14 26ndash28] to check therelated criteria for each aspect

Lee et al [10] suggested that the framework of greensupplier selection for high-tech industry should includesix aspects and 23 criteria but they presented a limiteddiscussion of several important and traditional criteria suchas delivery price and financial stability Kuo et al [12]determined the green supplier selection criteria based onliteratures and a Delphi expert questionnaire presentingsix aspects and 24 criteria sent to ten experts Tseng andChiu [27] proposed 18 GSCM criteria through comprehen-sive discussion and literatures for a printed circuit board

Table 1 The criteria of the green supplier selection

Aspects Criteria Relevantreferences

Environmentalfactor

1198921 safety and health [17]1198922 strategic fit [10]

1198923 environmental control [14]

1198924 recovery [26]

Enterpriseoperating

1198925 price [12]

1198926 finance stability [10]

1198927 quality control [12]

1198928 out-of-control management [12]

1198929 delivery [14]

11989210 flexibility [27]

11989211 maintenance and support [27]

Strategictechnology anddevelopment

11989212 current capability [28]

11989213 RampD capability [10]

11989214 compatibility across levels [14]11989215 information share [27]

manufacturer Using the framework proposed by Tseng andChiursquos [27] we included supplier relation closeness in ourproposed framework to carry out the suppliersrsquo connectionto green supplier selection Supplier relation closeness is animportant criterion in the supplier audit forms It containsthe implication of communication channels informationsharing instant feedback and resource-sharing platformamong the suppliers of the supply chain Lin [26] examinedthe cause and effect relationships among the eight criteriato evaluate GSCM practices using fuzzy DEMATEL Becauseall of the criteria are related to GSCM practices we adoptedsome environmental criteria in our proposed frameworkBuyukozkan [14] described the framework of green supplierselection based on the literatures about automotive industryincluding three aspects and 12 criteria In sum most of thepublished studies have developed a framework for greensupplier selection based on previous studies and interviewspossibly because interviewing is the most convenient way tolink the reviewed studies with the real cases In this study wedevelop a suitable decision framework of three aspects and 15criteria for green supplier selection as shown in Table 1

3 The Proposed Hybrid MCGDM Method

To construct a systematic evaluation to support the processof green supplier selection we propose a hybrid MCGDMmethod of four stages The first stage evaluates the subjectivecriteria weights of each DM based on the AHP methodAt the second stage we consider entropy to evaluate theobjective criteria weights of each DM At the third stage weuse ELECTRE III to rank and improve the suppliers For thefourth stage LAM is used to integrate the ranking results ofeach DM based on the result of the third stage Suppose aset of alternatives is (119886

1 119886

119894 119886

119896 119886

119898) and the criteria

are defined as (1198921 119892

119895 119892

119899)There are a number of DMs

4 Mathematical Problems in Engineering

denoted as (1198631 119863

119905 119863

119897)The weights of each criterion

for DMs are 119908119905119895and 119909

119894119895 representing the performance value

of alternative 119886119894under criterion 119892

119895 The AHP method the

entropy method the ELECTRE III method and LAM aredescribed as follows

31 The AHP Method for Determining the Subjective WeightsSaaty [39 40] used AHP to determine the DMrsquos subjectiveweight in a hierarchical structure All decision problemscan be considered as having a hierarchical structure Theimportant advantage of AHP is that a decision problemcan be decomposed into a number of subsystems [41] anda complicated decision problem can be systematized to ahierarchical structure The hierarchical structure starts witha goal and then moves to the intermediate level containingaspects and criteria and the alternatives are at the bottom Inthis study the first level is the goal for the overall objectivesof the problem for green supplier selection The second levelis decomposed into three aspects and each aspect can alsobe decomposed into several criteria as shown in Table 1If the criteria at the low levels exist the criteria at thelower levels can be generated based on the same principleAfter constructing the hierarchical structure of the problemfor green supplier selection the next step is to survey thecomparative weights among the aspectscriteria with a 1ndash9 point scale ranging from equally important to extremelyimportant to build the comparison matrices The subjectiveweight of criterion 119895 can be found with respect to themaximum eigenvalue of the comparative matrix for DM 119905which can be expressed as 1199081015840

119905119895 Then we can further check

the consistency index (CI) and consistency ratio (CR) ofeach comparison matrix via the following equation

CI =120582max minus 119899

119899 minus 1

CR = CIRI

(1)

The CI and CR values should not be larger than 01 for aconfident result If CI or CR value is larger than 01 we willinterview manager or assistant manager for the inconsistentcomparative weight and further adjust the value

32 The Entropy Method for Determining Objective WeightsThe entropymethod is used to evaluate the objective weightswhich is an important concept in information theory [42]This concept measures the expected information content ofa specific message [43] If the entropy measure is larger theinformation contained is less Therefore we can decide theobjective weight of each criterion based on the informationcontained in the decision matrix First a decision matrixis a matrix whose elements express the performance of analternative with respect to a criterion Then the decisionmatrix can be normalized in a linear manner which can bedescribed as

119903119894119895=

119909119894119895

max119895119909119894119895

119894 = 1 2 119898 119895 = 1 2 119899 (2)

where

119909119894119895=

119909119894119895 if 119892

119895is a benefit criterion

1

119909119894119895

if 119892119895is a cost criterion

(3)

Then the degree of diversification of the normalized decisionmatrix can be considered using the following equation

120575119895= 1 + 119887

119898

sum

119894=1

119903119894119895ln 119903119894119895 119895 = 1 2 119899 (4)

where 119887 = 1 ln119898 and 119887 is a constantFinally the objective weight of criterion 119895 can be obtained

as follows

11990810158401015840

119895=

120575119895

sum119899

119894=1120575119895

119895 = 1 2 119899 (5)

In this study according to the subjective weights and objec-tive weights the compromised weights of criterion 119895 for DM119905 can be expressed as

119908119905119895=

1199081015840

11990511989511990810158401015840

119895

sum119899

119895=1119908101584011990511989511990810158401015840119895

119905 = 1 2 119897 119895 = 1 2 119899 (6)

33 The ELECTRE III Method to Evaluate the Performance ofSuppliers The ELECTRE III method proposed by Roy [44]was designed to address inaccurate imprecise uncertain orill-determined data such as qualitative data ELECTRE IIInot only evaluates the best choice but also presents a specificranking result and leaves the final selection to the DMsFor the issue of green supplier selection ELECTRE III ischosen as a suitable method and it involves aspects that areoften neglected by othermethods for yielding relatively stableresults [45] Before we illustrate ELECTRE III we definethree threshold values that establish the DMrsquos preferencefor each criterion 119895 including indifference preference andveto thresholds The indifference threshold (119902

119895) indicates a

gap between the evaluation scores that are still compatiblewith a situation of indifference The preference threshold(119901119895) expresses the minimum difference between the values

of criterion 119895 to which the DM attributes significance interms of strict preference The veto threshold (V

119895) expresses

the minimum difference between the values of criterion 119895

beyond which the DM believes the gap between the twoscores cannot be compensated by the good performance ofthe other criteria In this study these thresholds for eachcriterion were set by interviews with DMs Then outrankingdegree can be defined as that alternative 119894 outranks alternative119896 forDM 119905 which can be calculated by the following equation

119862119905(119886119894 119886119896) =

1

119908

119899

sum

119895=1

119908119905119895119888119895(119886119894 119886119896) (7)

Mathematical Problems in Engineering 5

where

119908 =

119899

sum

119895=1

119908119905119895

119888119895(119886119894 119886119896) =

1 if 119909119894119895+ 119902119895ge 119909119896119895

0 if 119909119894119895+ 119901119895le 119909119896119895

119901119895+ 119909119894119895minus 119909119896119895

119901119895minus 119902119895

otherwise

(8)

We then consider rejecting degree which means alternative 119894is dominated by alternative 119896 under criterion 119895 as follows

119889119895(119886119894 119886119896)

=

1 if 119892119895(119886119896) ge 119892119895(119886119894) + V119895

0 if 119892119895(119886119896) le 119892119895(119886119894) + 119901119895

119892119895(119886119896) minus 119892119895(119886119894) minus 119901119895

V119895minus 119901119895

otherwise

(9)

Therefore the overall outranking degree of DM 119905 can beconsidered with outranking degree and rejecting degreewhich is called overall outranking degree as indicated in thefollowing equation

119878119905(119886119894 119886119896)

=

119862119905(119886119894 119886119896) if 119889

119895(119886119894 119886119896)

le 119862119905(119886119894 119886119896) forall119895

119862119905(119886119894 119886119896)

times prod

119895isin119869(119886119894 119886119896)

1 minus 119889119895(119886119894 119886119896)

1 minus 119862119905(119886119894 119886119896) otherwise

(10)

where 119869(119886119894 119886119896) is the set of criteria such that 119889

119895(119886119894 119886119896) gt

119862119905(119886119894 119886119896)

The final step is to exploit the model and produce aranking result from the overall outranking degrees The gen-eral approach for exploitation is to construct two preorders1198851and 119885

2using the descending and ascending distillation

process and then combine these two to produce a partialpreorder 119885 = 119885

1cap 1198852 The descending process is to clas-

sify the alternatives from the best to the worst while theascending process is from the worst to the best [46ndash48]

34 The LAM for Integrating Ranking Results of DMs Ber-nardo and Blin [31] developed LAM to transform individualranking result into an overall ranking result Therefore LAMcan integrate the ranking result of each DM into an overallranking result We can apply this simple method to sum theranking score from each DM and rank the overall score fromthe lowest score to the highest score [43] If an alternativeis ranked as the first order the alternative gets one pointIf an alternative is ranked as the second it gets two pointsand so on On the other hand LAM also considers the

weights of the DMs to integrate the overall ranking resultIn our case study we consider the weights of DMs basedon their positions For example the RampD department hasapproximately 27 of the decision-making power for greensupplier selection so the weight of the RampD departmentis 027 in the procedure of green supplier selection LAMcan be illustrated as follows First an overall ranking matrixis established as Π = [120587

119894119904] where 120587

119894119904represents the

weighted frequency that 119886119894is ranked the 119904th with the different

weights of the DMs Then depending on the above overallranking matrix we formulate a linear programming modelto optimize the permutationThe linear programmingmodeluses the binary decision variables 120593

119894119904 The decision variable

120593119894119904

= 1 means 119886119894is assigned to the 119904th overall rank and

120593119894119904= 0 otherwise Obviously one alternative can be assigned

to only one rank and one rank can only be assigned by onealternative Therefore the linear programming model can bewritten as follows

max119898

sum

119894=1

119898

sum

119891=1

120587119894119904120593119894119904

subject to119898

sum

119894=1

120593119894119904= 1 119904 = 1 2 119898

119898

sum

119891=1

120593119894119904= 1 119894 = 1 2 119898

120593119894119904isin 0 1 119894 119904 = 1 2 119898

(11)

4 Application Case

Y-TECH is the first manufacturer in Taiwan to mass-produceTFT-LCD panels and it is also one of the top five TFT-LCD panel manufacturers in the world By utilizing thedata coming from Y-TECH we aimed at providing a usefulevaluation method with continuous improvement Becauseof the global competitive market the well-known TFT-LCD manufacturers hope to develop and invest in theirsuppliers especially for some of the important componentsfor TFT-LCD panel such as the color filter back-lightunit and polarizer Because good suppliers may affect themanufacturerrsquos performance and competitiveness and theentire supply chain in this study we demonstrate the supplierevaluation procedure and provide information about supplierassistance for TFT-LCD manufacturers

41 Study Background In the polarizer industry the polar-izer manufacturer of the highest market share is in SouthKorea while the Japanese polarizer manufacturer has thesecond highestmarket share In recent years Y-TECHmainlydepended on materials and technical support from Japanesepolarizer manufacturers Also the Korean polarizer manu-facturers have aggressively intervened in the supply chainof Taiwanese TFT-LCD industry Y-TECH has started toreconsider its supplier evaluation procedure and involvedthe group of GPM department in auditing and evaluatingits suppliers Figure 1 shows the new decision procedure for

6 Mathematical Problems in Engineering

Quarterly business reviewQuarterly quality review

Annual and audit

Qualified greensuppliers lists

MM material managementSQM supplier quality managementESH environment safety healthHR human resourceRD research and designGPM green product management

SQMESHHR

SQM (36)MM (27)RD (27)GPM (10)

SQMESHHR

1st tier2nd tierfreeze

New green suppliers

Survey and audit

MMSQM

Figure 1 The decision procedure of green supplier selection in the case study

green supplier selection at Y-TECH Before being a formalpolarizer supplier in Y-TECH the candidate companiesshould pass a two-stage assessment At the first stage anew polarizer supplier should accept a preliminary surveyby the teams of MM and SQM departments and then theteams of SQM ESH and HR departments further executethe preliminary audit for the polarizer suppliers A qualifiedpolarizer manufacturera should pass at least 70 of the itemson all supplier audit forms At the second stage the formalevaluation is conducted by the teams of SQM MM RD andGPM departments After the second-stage assessment thequalified polarizer suppliers will be ranked in the first tierif they pass more than 80 of the items on supplier auditforms and the suppliers passing 70ndash80 of the items willbe ranked in the second tier If the qualified polarizer supplierpasses fewer than 70 of the items it will be regarded asdisqualified All of the formal suppliers are reviewed regularlyby the teams of SQM ESH and HR departments Further-more IS9001 ISO14001 and OHSAS18001 certifications arenot necessary in the evaluation procedure because Y-TECHwill assist their formal suppliers to apply for environmentalcertifications within a limited timeThe first-stage assessmentis the preliminary survey and audit so we focus on applyingthe proposed MCGDM method to the formal evaluation forthe assessment at the second stage

In this case study although four qualified polarizer sup-pliers for Y-TECH outperformed in all other suppliers for thefirst-stage survey our focus will be on supplier 3 because it isa subsidiary company for Y-TECH In fact strategic purchaseis an important strategy to enhance competitiveness in theTFT-LCD industry Therefore the decision procedure is notonly to select the subsidiary into the supply chain but alsoto provide improvement reports for the subsidiary companyAdditionally it is expected that the subsidiary supplier willbe the primary one In addition to the three aspects andfifty criteria (as mentioned in Section 2) we also surveyed12 managers on the expert committee In practice the SQMdepartment holds 36 of the decision-making power theMM and RampD departments 27 and the GPM department10 to select suitable suppliers during the decision-makingprocess

42 Empirical Results First we designed a questionnaire for12 experts to measure the comparative weights between theaspects and criteria Table 2 presents the subjective weights ofeach criterion for DMs based on AHPThemanagers of MMRD GPM and SQM departments are 119863

1 1198632 1198633 1198634 1198635

1198636 1198637 1198638 1198639 and 119863

10 11986311 11986312 respectively Then we

focused on the criteria weights that were 15 more thanthe average weight (01) Notice that many managers think

Mathematical Problems in Engineering 7

Table 2 Subjective weights of each criterion for DMs

Dept MM RD GPM SQMCriteria 119863

11198632

1198633

1198634

1198635

1198636

1198637

1198638

1198639

11986310

11986311

11986312

1198921

0014 0192 0009 0177 0349 0007 0106 0111 0120 0006 0117 00221198922

0014 0021 0033 0421 0050 0007 0032 0111 0120 0042 0290 01191198923

0036 0043 0012 0083 0174 0037 0048 0056 0033 0016 0036 00441198924

0007 0006 0002 0028 0174 0007 0010 0056 0007 0002 0012 00061198925

0405 0006 0048 0016 0009 0131 0093 0093 0004 0019 0011 00171198926

0081 0001 0048 0005 0003 0026 0031 0019 0001 0004 0002 00171198927

0039 0043 0048 0071 0057 0131 0197 0056 0025 0161 0029 00071198928

0039 0009 0048 0010 0019 0026 0049 0056 0008 0032 0010 00021198929

0064 0009 0045 0006 0015 0071 0073 0079 0010 0007 0016 000211989210

0016 0009 0045 0003 0013 0071 0031 0020 0021 0026 0005 000211989211

0007 0002 0006 0001 0004 0014 0019 0013 0003 0044 0018 000111989212

0174 0480 0421 0026 0083 0196 0078 0243 0270 0013 0085 047611989213

0058 0096 0070 0130 0028 0196 0155 0049 0270 0093 0256 015911989214

0035 0069 0143 0019 0006 0020 0039 0021 0081 0089 0028 010611989215

0012 0014 0020 0004 0017 0059 0039 0021 0027 0445 0085 0021

highly of the current capability (11989212) and RampD capability

(11989213) The managers of MM department generally place

importance on the current capability (11989212) and the weights

of 1198631 1198632 and 119863

3are 0174 0480 and 0421 respectively

Moreover MM department is also concerned with the price(1198925) and the average weight is 0153 It is generally agreed

that the managers of RampD department put emphasis onenvironmental factors because of their relevance to productdesign Taking strategic fit (119892

2) for example suppliers should

review environment-related substance list regularly whendeveloping a green productThe average weights of safety andhealth (119892

1) strategic fit (119892

2) environmental control (119892

3) and

recovery (1198924) are 0263 0236 0129 and 0101 respectively

and the importance of environmental factors is over 70 ofthe total weight

In particular GPM department does not give moreweight to environmental factors and the average weight ofenvironmental factors is only 0054 The most likely reasonis that the GPM department holds less decision-makingpower GPM department is concerned with quality control(1198927) current capability (119892

12) and RampD capability (119892

13) and

the average weights are 0102 0196 and 0168 respectivelyOverall themanagers ofGPMdepartment are still concernedwith the current capability (119892

12) and theRampDcapability (119892

13)

Finally SQM department focuses on the strategic fit (1198922)

current capability (11989212) RampD capability (119892

13) and informa-

tion share (11989215) and the average weights of these factors

are 0150 0191 0169 and 0184 respectively Based on ourexpectation SQM department is not significantly concernedwith quality control (119892

7) or other criteria of the enterprise

operation and the average weight for quality control (1198927)

is only 0066 However SQM department emphasizes theimportance of information share (119892

15) Information share

brings the benefit of enhancing the competitiveness of theentire supply chain especially technology development Insum SQM department focuses on the criteria of strategic

Table 3 Objective weights of each criterion for individual DM

Criteria Objective weights1198921

00721198922

00761198923

00671198924

00661198925

00641198926

00751198927

00811198928

00781198929

008011989210

006411989211

004711989212

005011989213

007011989214

005611989215

0054

technology and developmentThus to avoid overly subjectiveweights we include the entropy method in the proposedevaluation procedure using (2) through (5) The objectiveweights of the criteria are shown in Table 3 while Table 4indicates the compromised weights of the criteria for eachDM by (6)

After the compromised criteria weights of the DMsare decided we further apply the criteria weights and theperformance to the ELECTRE III method (7) through (10)to evaluate the four polarizer suppliers For each DM theoutranking degree of any two alternatives can be calculatedby (7) and the results are shown in Table 5 For examplefor manager 119863

1 the outranking degree of alternative 1 to

alternative 3 is 0743 and the outranking degree of alternative3 to alternative 1 is 0493 Obviously manager 119863

1believes

8 Mathematical Problems in Engineering

Table 4 Compromised weights of each criterion for DMs

Dept MM RD GPM SQMCriteria 119863

11198632

1198633

1198634

1198635

1198636

1198637

1198638

1198639

11986310

11986311

11986312

1198921

0016 0230 0011 0176 0367 0008 0109 0121 0135 0007 0123 00271198922

0016 0027 0042 0441 0055 0008 0035 0127 0142 0051 0322 01521198923

0037 0048 0013 0077 0170 0038 0046 0057 0034 0017 0035 00501198924

0007 0007 0002 0025 0168 0007 0009 0056 0007 0002 0012 00071198925

0399 0006 0051 0014 0008 0128 0085 0090 0004 0020 0010 00181198926

0093 0001 0060 0005 0003 0030 0033 0022 0001 0005 0002 00211198927

0049 0058 0065 0079 0067 0162 0229 0068 0032 0209 0034 00101198928

0047 0012 0062 0011 0022 0031 0055 0066 0010 0040 0011 00031198929

0079 0012 0060 0007 0018 0087 0084 0095 0012 0009 0019 000311989210

0016 0010 0048 0003 0012 0069 0028 0019 0021 0027 0005 000211989211

0005 0002 0005 0001 0003 0010 0013 0009 0002 0033 0012 000111989212

0134 0400 0350 0018 0061 0149 0056 0183 0211 0010 0062 040111989213

0062 0112 0081 0126 0029 0209 0156 0052 0295 0104 0262 018711989214

0030 0064 0133 0015 0005 0017 0031 0018 0071 008 0023 010011989215

0010 0013 0018 0003 0013 0049 0030 0017 0023 0385 0067 0019

Table 5 Outranking degrees of each DM

1198631

1198632

1198633

1198861

1198862

1198863

1198864

1198861

1198862

1198863

1198864

1198861

1198862

1198863

1198864

1198861

0 0588 0743 0867 1198861

0 0771 0578 0868 1198861

0 0688 0547 08441198862

0521 0 0598 0633 1198862

0310 0 0374 0425 1198862

0409 0 0442 05291198863

0493 0538 0 0762 1198863

0715 0779 0 0803 1198863

0669 0719 0 07631198864

0591 0619 0479 0 1198864

0783 0787 0476 0 1198864

0771 0735 0488 01198634

1198635

1198636

a1 a2 a3 a4 a1 a2 a3 a4 a1 a2 a3 a41198861

0 0785 0839 0862 1198861

0 0790 0804 0858 1198861

0 0635 0685 07701198862

0306 0 0413 0440 1198862

0378 0 0444 0463 1198862

0477 0 0625 06441198863

0673 0750 0 0780 1198863

0650 0678 0 0703 1198863

0531 0632 0 07111198864

0740 0782 0694 0 1198864

0745 0832 0643 0 1198864

0665 0674 0565 01198637

1198638

1198639

1198861

1198862

1198863

1198864

1198861

1198862

1198863

1198864

1198861

1198862

1198863

1198864

1198861

0 0642 0762 0767 1198861

0 0736 0706 0853 1198861

0 0762 0664 08641198862

0473 0 0602 0634 1198862

0404 0 0492 0518 1198862

0320 0 0465 05201198863

0534 0604 0 0667 1198863

0608 0646 0 0712 1198863

0634 0788 0 08091198864

0687 0663 0628 0 1198864

0734 0777 0588 0 1198864

0702 0735 0532 011986310

11986311

11986312

1198861

1198862

1198863

1198864

1198861

1198862

1198863

1198864

1198861

1198862

1198863

1198864

1198861

0 0471 0557 0585 1198861

0 0762 0754 0841 1198861

0 0765 0562 08861198862

0607 0 0671 0661 1198862

0326 0 0491 0509 1198862

0313 0 0387 04551198863

0660 0704 0 0663 1198863

0630 0776 0 0791 1198863

0697 0798 0 08261198864

0780 0713 0706 0 1198864

0704 0761 0644 0 1198864

0749 0762 0453 0

that alternative 1 is superior to alternative 3 However acharacteristic of the ELECTRE III method is that a penaltyis set if the alternative performs the worst for a criterionThus we can use (9) to calculate the rejecting degree dueto the penalty and the overall outranking degree can beobtained by (10) as shown in Table 6 As another examplefor the 119863

6manager the outranking degree of alternative 3

to alternative 2 is 0632 but the overall outranking degreeof alternative 3 to alternative 2 is 0000 This means thatalternative 3 performs too poorly to be accepted based onsome criteria The results presented in Table 7 show thatalternative 3 performs too poorly on quality control (119892

7) and

delivery (1198929) The rejecting degrees for quality control (119892

7)

and delivery (1198929) are 1000 and 0778 respectively which

Mathematical Problems in Engineering 9

Table 6 Overall outranking degree of each DM

1198631

1198632

1198633

1198861

1198862

1198863

1198864

1198861

1198862

1198863

1198864

1198861

1198862

1198863

1198864

1198861

0 0588 0743 0867 1198861

0 0771 0564 0868 1198861

0 0688 0246 08441198862

0342 0 0331 0633 1198862

0026 0 0 0148 1198862

0115 0 0 04151198863

0273 0334 0 0762 1198863

0715 0779 0 0803 1198863

0669 0719 0 07631198864

0563 0 0384 0 1198864

0783 0787 0361 0 1198864

0771 0735 0207 01198634

1198635

1198636

1198861

1198862

1198863

1198864

1198861

1198862

1198863

1198864

1198861

1198862

1198863

1198864

1198861

0 0785 0839 0862 1198861

0 0790 0804 0858 1198861

0 0245 0483 07461198862

0 0 0 0 1198862

0 0 0 0037 1198862

0090 0 0 06441198863

0673 0750 0 0780 1198863

0413 0468 0 0526 1198863

0091 0 0 05461198864

0633 0782 0694 0 1198864

0745 0832 0643 0 1198864

0 0459 0541 01198637

1198638

1198639

1198861

1198862

1198863

1198864

1198861

1198862

1198863

1198864

1198861

1198862

1198863

1198864

1198861

0 0413 0762 0767 1198861

0 0736 0706 0853 1198861

0 0762 0664 08641198862

0054 0 0336 0634 1198862

0037 0 0 0163 1198862

0005 0 0 01661198863

0094 0 0 0445 1198863

0344 0406 0 0550 1198863

0372 0788 0 08091198864

0 0437 0628 0 1198864

0734 0774 0588 0 1198864

0 0735 0474 011986310

11986311

11986312

1198861

1198862

1198863

1198864

1198861

1198862

1198863

1198864

1198861

1198862

1198863

1198864

1198861

0 0072 0123 0 1198861

0 0762 0754 0841 1198861

0 0765 0509 08861198862

0607 0 0671 0661 1198862

0 0 0 0 1198862

0034 0 0 02611198863

0660 0529 0 0663 1198863

0365 0776 0 0791 1198863

0697 0798 0 08261198864

0780 0713 0706 0 1198864

0 0761 0644 0 1198864

0664 0762 0315 0

Table 7 Rejecting degree for alternative 3 to alternative 2 from 1198896manager

1198921

1198922

1198923

1198924

1198925

1198926

1198927

1198928

1198929

11989210

11989211

11989212

11989213

11989214

11989215

Reject degree 0000 0000 0000 0583 0407 0000 1000 0288 0778 0000 0000 0000 0000 0000 0000

are obviously larger than the outranking degree of 0632 Inthe next section we further discuss which criteria shouldbe improved immediately Accordingly we can establish thedescending and ascending distillation for each DM and theranking results are shown in Table 8

Finally LAM is applied to establish linear programmingfor integrating the opinions of each DM According tothe decision-making power of each department the overallranking matrix is shown as follows

Π =[[[

[

06850 01950 00000 01200

00400 01350 00850 07400

02350 04700 02825 00125

00400 02000 06325 01275

]]]

]

(12)

Therefore we can formulate a linear programming modelusing (11) and obtain the solution which indicates that theoverall ranking result is 119886

1≻ 1198863≻ 1198864≻ 1198862The best supplier is

supplier 1 and the subsidiary company of Y-TECH is secondonly to supplier 1 Supplier 4 is ranked third and supplier 2performs the worst in this evaluation

43 Discussion and Implications In this study we aim topropose a hybridMCGDMmethod to evaluate and assist sup-pliers At the first stage we use the AHP method to measurethe subjective weights of the criteria for each DM Based onthe results of the AHP method it is obvious that most ofthe managers value the importance of current capability andRampD capability and this suggests that Y-TECH places a greatemphasis on technology development In the FPD industrydue to the rapid development of new technology many well-known enterprises regularly release new products to marketevery year A challenge for Y-TECH includes the develop-ment of other technologies such as organic light-emittingdisplays (OLED) For example the OLED panel led to thereduction of the demand for small and medium TFT-LCDdisplays in the global market in 2010 Therefore to enhancecompetitiveness TFT-LCD manufactures connecting withthe suppliers in the entire supply chain should continuouslydevelop new technology Quality control is an importantcriterion secondary only to technological capability At theearly stages of applying a new technology to a new productthe yield rate of the new product is low Consequently it isimportant not only to develop the new technology but alsoto maintain a highly qualified product Currently because

10 Mathematical Problems in Engineering

Table 8 Initial evaluation of individual DM

Dept MM RD GPM SQMRanking 119889

11198892

1198893

1198894

1198895

1198896

1198897

1198898

1198899

11988910

11988911

11988912

1st 1198861

1198861 1198863

1198863

1198861

1198861

1198861

1198861

1198861

1198861

1198862 1198863 1198864

1198861

1198861 1198863

2nd 1198862 1198863

1198861

1198863

1198864

1198862

1198862

1198864

1198863

1198863 1198864

3rd 1198864

1198864

1198864

1198863

1198863

1198863 1198864

1198863

1198864

1198864

4th 1198864

1198862

1198862

1198862

1198862

1198864

1198862

1198862

1198861

1198862

1198862

Table 9 The worst performing criteria of each alternative

1198861

1198862

1198863

1198864

1198631

mdash mdash mdash 1198925

1198632

mdash 11989212

mdash mdash1198633

mdash 11989212

mdash mdash1198634

mdash 1198922

mdash mdash1198635

mdash 1198921

mdash mdash1198636

mdash 11989212

g7 11989213

1198637

mdash mdash g7 11989213

1198638

mdash 11989212

mdash mdash1198639

mdash 11989212

mdash 11989213

11986310

g15 mdash mdash mdash11986311

mdash 1198922

mdash 11989213

11986312

mdash 11989212

mdash mdash

GPM department processes less decision-making power forgreen supplier selection it is obvious that environmentalfactors are not significantly considered in the evaluationprocedure with the exception that RampD department shoulddesign new products and the related materials must be listedfor MM department Then MM department can place anorder and ask the suppliers to follow the list SQMdepartmentis concerned with technological capability as well as infor-mation share Vertical integration is an important enterprisepolicy for the TFT-LCD industry and strategic purchase isa method for the implementation of vertical integration Asmentioned before the key components of TFT-LCD panelsthat are produced in-house have gradually increased year byyear so information share is also an important criterion forcultivating the core capability of enterprise and establishinga competitive advantage Furthermore all of the criteriaplay important roles in the evaluation procedure for greensupplier selection If the proposed hybrid MCGDM methodonly adopts the AHP method it will most likely lead to theoversubjective weighting of the criteria Therefore we applythe entropy method to consider the weights of the criteriasimultaneously

ELECTRE III is applied to evaluate the suppliers Duringthe evaluation procedure we compare the outranking degreeand the overall outranking degree and then decide whichcriterion should be improved immediately for each alterna-tive as shown in Table 9 If the overall outranking degreeof a criterion decreases to zero (comparing to outrankingdegree) this criterion should be improved immediately Forthe best supplier supplier 1 (119886

1) the 119863

10manager suggests

that alternative 1 should strengthen communication with Y-TECH by taking actions such as improving cooperation forthe development of new products and instant market infor-mation share (119892

15) In this case study one of the most impor-

tant aspects is how to provide assistance to the subsidiarycompany (119886

3) of Y-TECH Table 9 indicates that if supplier

3 improves quality control (1198927) for polarizer production its

performance will be improved significantly Generally it iscontended that managers should be concerned about criteriarelated to their job description Due to limited enterpriseresources the order of executing enterprise improvementis also an important issue Following the useful proposedmethod the discovery of the criteria that need to be improvedwill become easy

Different from many previous studies this study doesnot integrate the weights of the criteria at the first stageIn fact green supplier selection is a group decision-makingproblem and integrating the weights would also lead tothe loss of information Therefore we further consider theranking results of each department as shown in Table 10Although these four polarizermanufacturers are the suppliersof Y-TECH there are some differences among the rankingresults of each department especially for suppliers 2 and 4 Asmentioned before RampD department placed great emphasison the ldquogreenrdquo criteria and supplier 4rsquos good performance onthese criteria is the reason for the different ranking resultsConsequently in this study we proposed a novel method andviewpoint for green supplier selection and we suggested thatthe weights of the criteria should not be integrated at thefirst stage because further discussions and communicationare necessary to include the different opinions from eachdepartment Additionally we compare the results of theproposed method with the ones implemented by Y-TECHthat is simple additive method in Table 10 Both results showthat supplier 1 has the best performance and is superior tosupplier 3 Supplier 4 is in the third place while supplier2 is the worst Furthermore within the extensive literatureon green supplier selection methods the TOPSIS methodwas widely applied to evaluate green suppliersrsquo performance[15 17ndash22] Thus we further compare the TOPSIS methodwith the proposedmethod and the comparison is establishedon the basis of AHP-entropy criteria weights In the TOPSISmethod with AHP-entropy weights supplier 3 is in the firstplace and superior to supplier 1 and supplier 4 and supplier2 have the worst performance in this evaluation Althoughthe ranking results among the proposed method and othertwo methods are similar the emphasis of this study is onhow to improve the suppliersrsquo performance in the TFT-LCD industry More importantly the results of the proposed

Mathematical Problems in Engineering 11

Table 10 Comparison of ranking results for the proposed method with other methods

Ranking MM RD GPM SQM The proposed method Current methodlowast AHP-entropy TOPSIS1st 119886

31198861

1198861

1198861

1198861

1198861

1198863

2nd 1198861

1198864

1198862

1198863

1198863

1198863

1198861

3rd 1198864

1198863

1198863

1198864

1198864

1198864

1198864

4th 1198862

1198862

1198864

1198862

1198862

1198862

1198862

lowast

The current evaluation method in Y-TECH is simple additive method

method show that each department has its different opinionsand viewpoints of improvement Each department of theTFT-LCDmanufacturers can assist their suppliers within thecontext of business perspectives

5 Conclusion

In this study we propose a hybrid MCGDM method forgreen supplier selection for Y-TECH Our method focuseson not only the selection or ranking but also the providenceof assistance to suppliers to strengthen their competitivenessThis method also emphasizes continuous improvement Dueto the challenges and difficulties the TFT-LCD industriesworldwide face we intend to establish a systematic selectionprocedure for TFT-LCD manufacturers We developed adecision framework for green supplier selection based onliteratures and the supplier audit forms from Y-TECH Weconsidered AHP and the entropy method to measure thecompromised weights of the criteria that involve subjectiveand objective opinions simultaneously After that the ELEC-TRE III method provides polarizer manufacturersrsquo rankingresults for executive managers in addition to improvementreports The acquisition of key components used for in-house production of TFT-LCD panels has gradually becomea strategy for cost reduction and the enhancement of corecapability Thus TFT-LCD manufacturers should strengthentheir cooperation with their suppliers

Based on the results of the ELECTRE III method wecan compare the differences between the outranking degreeand the overall outranking degree Then we further considerwhich criteria should be improved immediately especially forthe suppliers of strategic purchases The results of this studyprovide an important reference for subsidiary companiesthat are seeking to be primary polarizer suppliers Finallydiffering from many of the previous studies we do notintegrate the weights of the criteria with regard to a singleDM in this study In other worlds we integrate the rankingresults of eachDM instead of theweights of the criteria In theproposed evaluation procedure LAM places more emphasison the discussion and communication among a group ofDMs which is consistent with the practical case

In summary the result of this study can be regardedas a problem-solving process for green supplier selectionRegardless of the proposed method or analytic procedurewe aim at helping TFT-LCD manufacturers to establish asystematic and useful evaluation procedure via the currentstudy Because all of the supplier audit forms are in Microsoft

Excel 2010 spreadsheets the proposedmethod is easy to applyusing the same file Executive managers can consider theperformance of all suppliers and refer to the results of theproposed method Three main contributions are made inthis study First depending on the literatures and supplieraudit forms we develop a decision framework for the TFT-LCD industry Second we propose the useful systematicand flexible hybrid MCGDMmethod (based on the businessprocess) and improve the current evaluation procedureThird we place more emphasis on improving suppliers toenhance the competitiveness of entire supply chain especiallythe subsidiary company In practice green supplier selectionis evaluated by each department separately and all criteria areregarded as independent In recent years increasing attentionhas been given to the interdependent relationships amongthe criteria [15 16 26 49] Therefore future studies arenecessary to clarify the interrelations among the criteriaWe hope that more detailed information influencing therelationships between the criteria can be provided in futureresearch Consequently it would bemore efficient to improvethe supplier selection process for the entire supply chain inthe future work

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper

Acknowledgments

This research was partially supported by the National ScienceCouncil of the Republic of China (Grant NSC 102-2221-E-007-084) The authors would like to thank the editorand anonymous referees for their valuable comments andsuggestions

References

[1] Taiwan Stock Exchange ldquoIndustry analysis the flat paneldisplay Industryrdquo February 2013 httpwwwtwsecomtwchproductspublicationessayphp

[2] S Hung ldquoCompetitive strategies for Taiwanrsquos thin film tran-sistor-liquid crystal display (TFT-LCD) industryrdquoTechnology inSociety vol 28 no 3 pp 349ndash361 2006

[3] W Ho X Xu and P K Dey ldquoMulti-criteria decision makingapproaches for supplier evaluation and selection a literaturereviewrdquo European Journal of Operational Research vol 202 no1 pp 16ndash24 2010

12 Mathematical Problems in Engineering

[4] J Chai J N K Liu and E W T Ngai ldquoApplication of decision-making techniques in supplier selection a systematic review ofliteraturerdquo Expert Systems with Applications vol 40 no 10 pp3872ndash3885 2013

[5] K Govindan S Rajendran J Sarkis and P Murugesan ldquoMulticriteria decision making approaches for green supplier eval-uation and selection a literature reviewrdquo Journal of CleanerProduction 2013

[6] M Herva and E Roca ldquoReview of combined approaches andmulti-criteria analysis for corporate environmental evaluationrdquoJournal of Cleaner Production vol 39 pp 355ndash371 2013

[7] S Seuring ldquoA review of modeling approaches for sustainablesupply chain managementrdquo Decision Support Systems vol 54no 4 pp 1513ndash1520 2013

[8] M Igarashi L De Boer and A M Fet ldquoWhat is required forgreener supplier selection A literature review and conceptualmodel developmentrdquo Journal of Purchasing and SupplyManage-ment vol 19 no 4 pp 247ndash263 2013

[9] A Genovese S C Koh G Bruno and E Esposito ldquoGreenersupplier selection state of the art and some empirical evidencerdquoInternational Journal of Production Research vol 51 no 10 pp2868ndash2886 2013

[10] A H I Lee H Y Kang C F Hsu and H C Hung ldquoA greensupplier selectionmodel for high-tech industryrdquo Expert Systemswith Applications vol 36 no 4 pp 7917ndash7927 2009

[11] G Tuzkaya A Ozgen D Ozgen and U R Tuzkaya ldquoEnvi-ronmental performance evaluation of suppliers a hybrid fuzzymulti-criteria decision approachrdquo International Journal of Envi-ronmental Science and Technology vol 6 no 3 pp 477ndash4902009

[12] R J Kuo Y C Wang and F C Tien ldquoIntegration of artificialneural network and MADA methods for green supplier selec-tionrdquo Journal of Cleaner Production vol 18 no 12 pp 1161ndash11702010

[13] Q Zhu Y Dou and J Sarkis ldquoA portfolio-based analysis forgreen supplier management using the analytical network pro-cessrdquo Supply Chain Management vol 15 no 4 pp 306ndash3192010

[14] G Buyukozkan ldquoAn integrated fuzzy multi-criteria groupdecision-making approach for green supplier evaluationrdquo Inter-national Journal of Production Research vol 50 no 11 pp 2892ndash2909 2012

[15] G Buyukozkan and G Cifci ldquoA novel hybrid MCDM approachbased on fuzzy DEMATEL fuzzy ANP and fuzzy TOPSIS toevaluate green suppliersrdquo Expert Systems with Applications vol39 no 3 pp 3000ndash3011 2012

[16] C H Hsu F KWang and G H Tzeng ldquoThe best vendor selec-tion for conducting the recycled material based on a hybridMCDM model combining DANP with VIKORrdquo ResourcesConservation and Recycling vol 66 pp 95ndash111 2012

[17] K Govindan R Khodaverdi and A Jafarian ldquoA fuzzy multicriteria approach for measuring sustainability performance ofa supplier based on triple bottom line approachrdquo Journal ofCleaner Production vol 47 pp 345ndash354 2013

[18] L Shen L Olfat K Govindan R Khodaverdi and A DiabatldquoA fuzzy multi criteria approach for evaluating green supplierrsquosperformance in green supply chain with linguistic preferencesrdquoResources Conservation and Recycling vol 74 pp 170ndash179 2013

[19] M Yazdani ldquoAn integrated MCDM approach to green supplierselectionrdquo International Journal of Industrial Engineering Com-putations vol 5 no 3 pp 443ndash458 2014

[20] T Chen and J Freeman ldquoUsing AHP-Entropy weight and TOP-SIS methodology in green supplier selectionrdquo in Proceedings ofthe European Operations Management Association Conference(EurOMA rsquo14) Palermo Italy June 2014

[21] D Kannan A B L D S Jabbour andC J C Jabbour ldquoSelectinggreen suppliers based on GSCM practices using fuzzy TOPSISapplied to a Brazilian electronics companyrdquo European Journal ofOperational Research vol 233 no 2 pp 432ndash447 2014

[22] H Zhao and S Guo ldquoSelecting green supplier of thermalpower equipment by using a Hybrid MCDM Method forsustainabilityrdquo Sustainability vol 6 no 1 pp 217ndash235 2014

[23] J Figueira S Greco and M EhrgottMultiple Criteria DecisionAnalysis State of the Art Surveys Springer Science BostonMass USA 2005

[24] B Roy ldquoThe outranking approach and the foundations ofELECTREmethodsrdquoTheory and Decision vol 31 no 1 pp 49ndash73 1991

[25] J Hokkanen and P Salminen ldquoChoosing a solid waste man-agement systemusingmulticriteria decision analysisrdquoEuropeanJournal of Operational Research vol 98 no 1 pp 19ndash36 1997

[26] R J Lin ldquoUsing fuzzy DEMATEL to evaluate the green supplychain management practicesrdquo Journal of Cleaner Productionvol 40 pp 32ndash39 2013

[27] M L Tseng and A S F Chiu ldquoEvaluating firmrsquos green supplychain management in linguistic preferencesrdquo Journal of CleanerProduction vol 40 pp 22ndash31 2013

[28] G Buyukozkan and G Cifci ldquoA novel fuzzy mulati -criteriadecision framework for sustainable supplier selection withincomplete informationrdquo Computers in Industry vol 62 no 2pp 164ndash174 2011

[29] M T Escobar and J M Moreno-Jimenez ldquoAggregation of indi-vidual preference structures in AHP-group decision makingrdquoGroup Decision andNegotiation vol 16 no 4 pp 287ndash301 2007

[30] V Tsyganok ldquoInvestigation of the aggregation effectiveness ofexpert estimates obtained by the pairwise comparisonmethodrdquoMathematical and ComputerModelling vol 52 no 3-4 pp 538ndash544 2010

[31] J J Bernardo and J M Blin ldquoA programming model ofconsumer choice among multi-attributed brandsrdquo Journal ofConsumer Research vol 4 no 2 pp 111ndash118 1977

[32] P K Humphreys Y K Wong and F T S Chan ldquoIntegratingenvironmental criteria into the supplier selection processrdquoJournal of Materials Processing Technology vol 138 no 1ndash3 pp349ndash356 2003

[33] C Bai and J Sarkis ldquoIntegrating sustainability into supplierselection with grey system and rough set methodologiesrdquoInternational Journal of Production Economics vol 124 no 1 pp252ndash264 2010

[34] X Fu Q Zhu and J Sarkis ldquoEvaluating green supplier devel-opment programs at a telecommunications systems providerrdquoInternational Journal of Production Economics vol 140 no 1pp 357ndash367 2012

[35] A Amindoust S Ahmed A Saghafinia and A BahreininejadldquoSustainable supplier selection a ranking model based on fuzzyinference systemrdquoApplied Soft Computing Journal vol 12 no 6pp 1668ndash1677 2012

[36] K Shaw R Shankar S S Yadav and L S Thakur ldquoSupplierselection using fuzzy AHP and fuzzy multi-objective linearprogramming for developing low carbon supply chainrdquo ExpertSystems with Applications vol 39 no 9 pp 8182ndash8192 2012

Mathematical Problems in Engineering 13

[37] A Awasthi S S Chauhan and S K Goyal ldquoA fuzzy multi-criteria approach for evaluating environmental performance ofsuppliersrdquo International Journal of Production Economics vol126 no 2 pp 370ndash378 2010

[38] T Y Chiou H K Chan F Lettice and S H Chung ldquoThe influ-ence of greening the suppliers and green innovation on envi-ronmental performance and competitive advantage in TaiwanrdquoTransportation Research Part E Logistics and TransportationReview vol 47 no 6 pp 822ndash836 2011

[39] T L Saaty ldquoA scaling method for priorities in hierarchicalstructuresrdquo Journal of Mathematical Psychology vol 15 no 3pp 234ndash281 1977

[40] T L Saaty The Analytic Hierarchy Process McGraw-Hill NewYork NY USA 1980

[41] CMacharis J Springael K De Brucker andA Verbeke ldquoPRO-METHEE and AHP the design of operational synergies inmulticriteria analysis Strengthening PROMETHEE with ideasof AHPrdquo European Journal of Operational Research vol 153 no2 pp 307ndash317 2004

[42] M Zeleny Multiple Criteria Decision Making McGraw-HillNew York NY USA 1982

[43] C L Hwang and K Yoon Multiple Attribute Decision MakingMethods and Applications A State-of-the-Art Survey SpringerNew York NY USA 1981

[44] B Roy ldquoELECTRE III un algorithme de classements fondesur une repre sentation floue des preferences en preferences enpresence de criteres multiplesrdquo Cahiers du Centre drsquoEtudes deRecherche Operationnelle vol 20 no 1 pp 3ndash24 1978

[45] P Vincke Multiple Criteria Decision-Aid John Wiley amp SonsChichester UK 1992

[46] M Rogers and M Bruen ldquoUsing ELECTRE III to chooseroute for Dublin Port motorwayrdquo Journal of TransportationEngineering vol 126 no 4 pp 313ndash323 2000

[47] M Rogers and M Bruen ELECTRE and Decision SupportMethods and Applications in Engineering and InfrastructureInvestment Kluwer Academic Publishers Boston Mass USA2000

[48] V Belton and T J Stewart Multiple Criteria Decision AnalysisAn Integrated Approach Kluwer Academic Publishers BostonMass USA 2002

[49] B Chang C W Chang and C H Wu ldquoFuzzy DEMATELmethod for developing supplier selection criteriardquo Expert Sys-tems with Applications vol 38 no 3 pp 1850ndash1858 2011

Submit your manuscripts athttpwwwhindawicom

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

MathematicsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Mathematical Problems in Engineering

Hindawi Publishing Corporationhttpwwwhindawicom

Differential EquationsInternational Journal of

Volume 2014

Applied MathematicsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Probability and StatisticsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Mathematical PhysicsAdvances in

Complex AnalysisJournal of

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OptimizationJournal of

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CombinatoricsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of

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Operations ResearchAdvances in

Journal of

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Function Spaces

Abstract and Applied AnalysisHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of Mathematics and Mathematical Sciences

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

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Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

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Discrete Dynamics in Nature and Society

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

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Decision SciencesAdvances in

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Volume 2014 Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Stochastic AnalysisInternational Journal of

Mathematical Problems in Engineering 3

as a multiple criteria group decision-making (MCGDM)problem Bernardo and Blin [31] developed the linear assign-ment method (LAM) for aggregating a set of ranking resultsLAM can aggregate the DMsrsquo ranking results and it alsoconsiders the DMsrsquo weights based on their authority Thisstudy uses LAM to aggregate the different ranking results ofeach DM based on the results of the ELECTRE III method

The rest of this paper is organized as follows Section 2is the review of recent researches on green supplier selectionand the development of a framework for the process ofgreen supplier selection based on the reviewed literaturesand supplier audit forms offered by an anonymous TFT-LCD manufacturer (the case-studied company in this paper)in Taiwan Section 3 is the introduction of the proposedmethod which combines AHP the entropy method ELEC-TRE III and LAM Section 4 is the application of theproposed method to a green supplier selection case and theresults Further discussions and managerial implications arepresented in Section 4 In Section 5 concluding remarks areprovided

2 Green Supplier Selection Criteria

Due to economic globalization green procurement canenhance firm competitiveness in GSCM and green supplierselection has an important role in green procurement In thissection we will introduce the proposed framework for greensupplier selection based on literatures and supplier auditforms fromY-TECH (for confidentiality a pseudonym is usedthroughout the study) awell-knownTFT-LCDmanufacturerin Taiwan and the main case studied in the present researchIn this section we review the published studies and comparethem with the supplier audit forms

According to supplier audit forms from each departmentthe proposed decision framework can be divided into threeaspects environmental factors (environment safety andhealth [ESH] department and green product management[GPM] department) enterprise operating (material manage-ment [MM] department and supplier quality management[SQM] department) and strategic technology and develop-ment (research and development [RD] department and SQMdepartment) Some published papers have considered theselection issue with only environmental performance [32ndash36] A few studies have focused on environmental perfor-mance for supplier selection [37 38] For developing thedecision framework we review the recent published studiesof green supplier selection [10 12 14 26ndash28] to check therelated criteria for each aspect

Lee et al [10] suggested that the framework of greensupplier selection for high-tech industry should includesix aspects and 23 criteria but they presented a limiteddiscussion of several important and traditional criteria suchas delivery price and financial stability Kuo et al [12]determined the green supplier selection criteria based onliteratures and a Delphi expert questionnaire presentingsix aspects and 24 criteria sent to ten experts Tseng andChiu [27] proposed 18 GSCM criteria through comprehen-sive discussion and literatures for a printed circuit board

Table 1 The criteria of the green supplier selection

Aspects Criteria Relevantreferences

Environmentalfactor

1198921 safety and health [17]1198922 strategic fit [10]

1198923 environmental control [14]

1198924 recovery [26]

Enterpriseoperating

1198925 price [12]

1198926 finance stability [10]

1198927 quality control [12]

1198928 out-of-control management [12]

1198929 delivery [14]

11989210 flexibility [27]

11989211 maintenance and support [27]

Strategictechnology anddevelopment

11989212 current capability [28]

11989213 RampD capability [10]

11989214 compatibility across levels [14]11989215 information share [27]

manufacturer Using the framework proposed by Tseng andChiursquos [27] we included supplier relation closeness in ourproposed framework to carry out the suppliersrsquo connectionto green supplier selection Supplier relation closeness is animportant criterion in the supplier audit forms It containsthe implication of communication channels informationsharing instant feedback and resource-sharing platformamong the suppliers of the supply chain Lin [26] examinedthe cause and effect relationships among the eight criteriato evaluate GSCM practices using fuzzy DEMATEL Becauseall of the criteria are related to GSCM practices we adoptedsome environmental criteria in our proposed frameworkBuyukozkan [14] described the framework of green supplierselection based on the literatures about automotive industryincluding three aspects and 12 criteria In sum most of thepublished studies have developed a framework for greensupplier selection based on previous studies and interviewspossibly because interviewing is the most convenient way tolink the reviewed studies with the real cases In this study wedevelop a suitable decision framework of three aspects and 15criteria for green supplier selection as shown in Table 1

3 The Proposed Hybrid MCGDM Method

To construct a systematic evaluation to support the processof green supplier selection we propose a hybrid MCGDMmethod of four stages The first stage evaluates the subjectivecriteria weights of each DM based on the AHP methodAt the second stage we consider entropy to evaluate theobjective criteria weights of each DM At the third stage weuse ELECTRE III to rank and improve the suppliers For thefourth stage LAM is used to integrate the ranking results ofeach DM based on the result of the third stage Suppose aset of alternatives is (119886

1 119886

119894 119886

119896 119886

119898) and the criteria

are defined as (1198921 119892

119895 119892

119899)There are a number of DMs

4 Mathematical Problems in Engineering

denoted as (1198631 119863

119905 119863

119897)The weights of each criterion

for DMs are 119908119905119895and 119909

119894119895 representing the performance value

of alternative 119886119894under criterion 119892

119895 The AHP method the

entropy method the ELECTRE III method and LAM aredescribed as follows

31 The AHP Method for Determining the Subjective WeightsSaaty [39 40] used AHP to determine the DMrsquos subjectiveweight in a hierarchical structure All decision problemscan be considered as having a hierarchical structure Theimportant advantage of AHP is that a decision problemcan be decomposed into a number of subsystems [41] anda complicated decision problem can be systematized to ahierarchical structure The hierarchical structure starts witha goal and then moves to the intermediate level containingaspects and criteria and the alternatives are at the bottom Inthis study the first level is the goal for the overall objectivesof the problem for green supplier selection The second levelis decomposed into three aspects and each aspect can alsobe decomposed into several criteria as shown in Table 1If the criteria at the low levels exist the criteria at thelower levels can be generated based on the same principleAfter constructing the hierarchical structure of the problemfor green supplier selection the next step is to survey thecomparative weights among the aspectscriteria with a 1ndash9 point scale ranging from equally important to extremelyimportant to build the comparison matrices The subjectiveweight of criterion 119895 can be found with respect to themaximum eigenvalue of the comparative matrix for DM 119905which can be expressed as 1199081015840

119905119895 Then we can further check

the consistency index (CI) and consistency ratio (CR) ofeach comparison matrix via the following equation

CI =120582max minus 119899

119899 minus 1

CR = CIRI

(1)

The CI and CR values should not be larger than 01 for aconfident result If CI or CR value is larger than 01 we willinterview manager or assistant manager for the inconsistentcomparative weight and further adjust the value

32 The Entropy Method for Determining Objective WeightsThe entropymethod is used to evaluate the objective weightswhich is an important concept in information theory [42]This concept measures the expected information content ofa specific message [43] If the entropy measure is larger theinformation contained is less Therefore we can decide theobjective weight of each criterion based on the informationcontained in the decision matrix First a decision matrixis a matrix whose elements express the performance of analternative with respect to a criterion Then the decisionmatrix can be normalized in a linear manner which can bedescribed as

119903119894119895=

119909119894119895

max119895119909119894119895

119894 = 1 2 119898 119895 = 1 2 119899 (2)

where

119909119894119895=

119909119894119895 if 119892

119895is a benefit criterion

1

119909119894119895

if 119892119895is a cost criterion

(3)

Then the degree of diversification of the normalized decisionmatrix can be considered using the following equation

120575119895= 1 + 119887

119898

sum

119894=1

119903119894119895ln 119903119894119895 119895 = 1 2 119899 (4)

where 119887 = 1 ln119898 and 119887 is a constantFinally the objective weight of criterion 119895 can be obtained

as follows

11990810158401015840

119895=

120575119895

sum119899

119894=1120575119895

119895 = 1 2 119899 (5)

In this study according to the subjective weights and objec-tive weights the compromised weights of criterion 119895 for DM119905 can be expressed as

119908119905119895=

1199081015840

11990511989511990810158401015840

119895

sum119899

119895=1119908101584011990511989511990810158401015840119895

119905 = 1 2 119897 119895 = 1 2 119899 (6)

33 The ELECTRE III Method to Evaluate the Performance ofSuppliers The ELECTRE III method proposed by Roy [44]was designed to address inaccurate imprecise uncertain orill-determined data such as qualitative data ELECTRE IIInot only evaluates the best choice but also presents a specificranking result and leaves the final selection to the DMsFor the issue of green supplier selection ELECTRE III ischosen as a suitable method and it involves aspects that areoften neglected by othermethods for yielding relatively stableresults [45] Before we illustrate ELECTRE III we definethree threshold values that establish the DMrsquos preferencefor each criterion 119895 including indifference preference andveto thresholds The indifference threshold (119902

119895) indicates a

gap between the evaluation scores that are still compatiblewith a situation of indifference The preference threshold(119901119895) expresses the minimum difference between the values

of criterion 119895 to which the DM attributes significance interms of strict preference The veto threshold (V

119895) expresses

the minimum difference between the values of criterion 119895

beyond which the DM believes the gap between the twoscores cannot be compensated by the good performance ofthe other criteria In this study these thresholds for eachcriterion were set by interviews with DMs Then outrankingdegree can be defined as that alternative 119894 outranks alternative119896 forDM 119905 which can be calculated by the following equation

119862119905(119886119894 119886119896) =

1

119908

119899

sum

119895=1

119908119905119895119888119895(119886119894 119886119896) (7)

Mathematical Problems in Engineering 5

where

119908 =

119899

sum

119895=1

119908119905119895

119888119895(119886119894 119886119896) =

1 if 119909119894119895+ 119902119895ge 119909119896119895

0 if 119909119894119895+ 119901119895le 119909119896119895

119901119895+ 119909119894119895minus 119909119896119895

119901119895minus 119902119895

otherwise

(8)

We then consider rejecting degree which means alternative 119894is dominated by alternative 119896 under criterion 119895 as follows

119889119895(119886119894 119886119896)

=

1 if 119892119895(119886119896) ge 119892119895(119886119894) + V119895

0 if 119892119895(119886119896) le 119892119895(119886119894) + 119901119895

119892119895(119886119896) minus 119892119895(119886119894) minus 119901119895

V119895minus 119901119895

otherwise

(9)

Therefore the overall outranking degree of DM 119905 can beconsidered with outranking degree and rejecting degreewhich is called overall outranking degree as indicated in thefollowing equation

119878119905(119886119894 119886119896)

=

119862119905(119886119894 119886119896) if 119889

119895(119886119894 119886119896)

le 119862119905(119886119894 119886119896) forall119895

119862119905(119886119894 119886119896)

times prod

119895isin119869(119886119894 119886119896)

1 minus 119889119895(119886119894 119886119896)

1 minus 119862119905(119886119894 119886119896) otherwise

(10)

where 119869(119886119894 119886119896) is the set of criteria such that 119889

119895(119886119894 119886119896) gt

119862119905(119886119894 119886119896)

The final step is to exploit the model and produce aranking result from the overall outranking degrees The gen-eral approach for exploitation is to construct two preorders1198851and 119885

2using the descending and ascending distillation

process and then combine these two to produce a partialpreorder 119885 = 119885

1cap 1198852 The descending process is to clas-

sify the alternatives from the best to the worst while theascending process is from the worst to the best [46ndash48]

34 The LAM for Integrating Ranking Results of DMs Ber-nardo and Blin [31] developed LAM to transform individualranking result into an overall ranking result Therefore LAMcan integrate the ranking result of each DM into an overallranking result We can apply this simple method to sum theranking score from each DM and rank the overall score fromthe lowest score to the highest score [43] If an alternativeis ranked as the first order the alternative gets one pointIf an alternative is ranked as the second it gets two pointsand so on On the other hand LAM also considers the

weights of the DMs to integrate the overall ranking resultIn our case study we consider the weights of DMs basedon their positions For example the RampD department hasapproximately 27 of the decision-making power for greensupplier selection so the weight of the RampD departmentis 027 in the procedure of green supplier selection LAMcan be illustrated as follows First an overall ranking matrixis established as Π = [120587

119894119904] where 120587

119894119904represents the

weighted frequency that 119886119894is ranked the 119904th with the different

weights of the DMs Then depending on the above overallranking matrix we formulate a linear programming modelto optimize the permutationThe linear programmingmodeluses the binary decision variables 120593

119894119904 The decision variable

120593119894119904

= 1 means 119886119894is assigned to the 119904th overall rank and

120593119894119904= 0 otherwise Obviously one alternative can be assigned

to only one rank and one rank can only be assigned by onealternative Therefore the linear programming model can bewritten as follows

max119898

sum

119894=1

119898

sum

119891=1

120587119894119904120593119894119904

subject to119898

sum

119894=1

120593119894119904= 1 119904 = 1 2 119898

119898

sum

119891=1

120593119894119904= 1 119894 = 1 2 119898

120593119894119904isin 0 1 119894 119904 = 1 2 119898

(11)

4 Application Case

Y-TECH is the first manufacturer in Taiwan to mass-produceTFT-LCD panels and it is also one of the top five TFT-LCD panel manufacturers in the world By utilizing thedata coming from Y-TECH we aimed at providing a usefulevaluation method with continuous improvement Becauseof the global competitive market the well-known TFT-LCD manufacturers hope to develop and invest in theirsuppliers especially for some of the important componentsfor TFT-LCD panel such as the color filter back-lightunit and polarizer Because good suppliers may affect themanufacturerrsquos performance and competitiveness and theentire supply chain in this study we demonstrate the supplierevaluation procedure and provide information about supplierassistance for TFT-LCD manufacturers

41 Study Background In the polarizer industry the polar-izer manufacturer of the highest market share is in SouthKorea while the Japanese polarizer manufacturer has thesecond highestmarket share In recent years Y-TECHmainlydepended on materials and technical support from Japanesepolarizer manufacturers Also the Korean polarizer manu-facturers have aggressively intervened in the supply chainof Taiwanese TFT-LCD industry Y-TECH has started toreconsider its supplier evaluation procedure and involvedthe group of GPM department in auditing and evaluatingits suppliers Figure 1 shows the new decision procedure for

6 Mathematical Problems in Engineering

Quarterly business reviewQuarterly quality review

Annual and audit

Qualified greensuppliers lists

MM material managementSQM supplier quality managementESH environment safety healthHR human resourceRD research and designGPM green product management

SQMESHHR

SQM (36)MM (27)RD (27)GPM (10)

SQMESHHR

1st tier2nd tierfreeze

New green suppliers

Survey and audit

MMSQM

Figure 1 The decision procedure of green supplier selection in the case study

green supplier selection at Y-TECH Before being a formalpolarizer supplier in Y-TECH the candidate companiesshould pass a two-stage assessment At the first stage anew polarizer supplier should accept a preliminary surveyby the teams of MM and SQM departments and then theteams of SQM ESH and HR departments further executethe preliminary audit for the polarizer suppliers A qualifiedpolarizer manufacturera should pass at least 70 of the itemson all supplier audit forms At the second stage the formalevaluation is conducted by the teams of SQM MM RD andGPM departments After the second-stage assessment thequalified polarizer suppliers will be ranked in the first tierif they pass more than 80 of the items on supplier auditforms and the suppliers passing 70ndash80 of the items willbe ranked in the second tier If the qualified polarizer supplierpasses fewer than 70 of the items it will be regarded asdisqualified All of the formal suppliers are reviewed regularlyby the teams of SQM ESH and HR departments Further-more IS9001 ISO14001 and OHSAS18001 certifications arenot necessary in the evaluation procedure because Y-TECHwill assist their formal suppliers to apply for environmentalcertifications within a limited timeThe first-stage assessmentis the preliminary survey and audit so we focus on applyingthe proposed MCGDM method to the formal evaluation forthe assessment at the second stage

In this case study although four qualified polarizer sup-pliers for Y-TECH outperformed in all other suppliers for thefirst-stage survey our focus will be on supplier 3 because it isa subsidiary company for Y-TECH In fact strategic purchaseis an important strategy to enhance competitiveness in theTFT-LCD industry Therefore the decision procedure is notonly to select the subsidiary into the supply chain but alsoto provide improvement reports for the subsidiary companyAdditionally it is expected that the subsidiary supplier willbe the primary one In addition to the three aspects andfifty criteria (as mentioned in Section 2) we also surveyed12 managers on the expert committee In practice the SQMdepartment holds 36 of the decision-making power theMM and RampD departments 27 and the GPM department10 to select suitable suppliers during the decision-makingprocess

42 Empirical Results First we designed a questionnaire for12 experts to measure the comparative weights between theaspects and criteria Table 2 presents the subjective weights ofeach criterion for DMs based on AHPThemanagers of MMRD GPM and SQM departments are 119863

1 1198632 1198633 1198634 1198635

1198636 1198637 1198638 1198639 and 119863

10 11986311 11986312 respectively Then we

focused on the criteria weights that were 15 more thanthe average weight (01) Notice that many managers think

Mathematical Problems in Engineering 7

Table 2 Subjective weights of each criterion for DMs

Dept MM RD GPM SQMCriteria 119863

11198632

1198633

1198634

1198635

1198636

1198637

1198638

1198639

11986310

11986311

11986312

1198921

0014 0192 0009 0177 0349 0007 0106 0111 0120 0006 0117 00221198922

0014 0021 0033 0421 0050 0007 0032 0111 0120 0042 0290 01191198923

0036 0043 0012 0083 0174 0037 0048 0056 0033 0016 0036 00441198924

0007 0006 0002 0028 0174 0007 0010 0056 0007 0002 0012 00061198925

0405 0006 0048 0016 0009 0131 0093 0093 0004 0019 0011 00171198926

0081 0001 0048 0005 0003 0026 0031 0019 0001 0004 0002 00171198927

0039 0043 0048 0071 0057 0131 0197 0056 0025 0161 0029 00071198928

0039 0009 0048 0010 0019 0026 0049 0056 0008 0032 0010 00021198929

0064 0009 0045 0006 0015 0071 0073 0079 0010 0007 0016 000211989210

0016 0009 0045 0003 0013 0071 0031 0020 0021 0026 0005 000211989211

0007 0002 0006 0001 0004 0014 0019 0013 0003 0044 0018 000111989212

0174 0480 0421 0026 0083 0196 0078 0243 0270 0013 0085 047611989213

0058 0096 0070 0130 0028 0196 0155 0049 0270 0093 0256 015911989214

0035 0069 0143 0019 0006 0020 0039 0021 0081 0089 0028 010611989215

0012 0014 0020 0004 0017 0059 0039 0021 0027 0445 0085 0021

highly of the current capability (11989212) and RampD capability

(11989213) The managers of MM department generally place

importance on the current capability (11989212) and the weights

of 1198631 1198632 and 119863

3are 0174 0480 and 0421 respectively

Moreover MM department is also concerned with the price(1198925) and the average weight is 0153 It is generally agreed

that the managers of RampD department put emphasis onenvironmental factors because of their relevance to productdesign Taking strategic fit (119892

2) for example suppliers should

review environment-related substance list regularly whendeveloping a green productThe average weights of safety andhealth (119892

1) strategic fit (119892

2) environmental control (119892

3) and

recovery (1198924) are 0263 0236 0129 and 0101 respectively

and the importance of environmental factors is over 70 ofthe total weight

In particular GPM department does not give moreweight to environmental factors and the average weight ofenvironmental factors is only 0054 The most likely reasonis that the GPM department holds less decision-makingpower GPM department is concerned with quality control(1198927) current capability (119892

12) and RampD capability (119892

13) and

the average weights are 0102 0196 and 0168 respectivelyOverall themanagers ofGPMdepartment are still concernedwith the current capability (119892

12) and theRampDcapability (119892

13)

Finally SQM department focuses on the strategic fit (1198922)

current capability (11989212) RampD capability (119892

13) and informa-

tion share (11989215) and the average weights of these factors

are 0150 0191 0169 and 0184 respectively Based on ourexpectation SQM department is not significantly concernedwith quality control (119892

7) or other criteria of the enterprise

operation and the average weight for quality control (1198927)

is only 0066 However SQM department emphasizes theimportance of information share (119892

15) Information share

brings the benefit of enhancing the competitiveness of theentire supply chain especially technology development Insum SQM department focuses on the criteria of strategic

Table 3 Objective weights of each criterion for individual DM

Criteria Objective weights1198921

00721198922

00761198923

00671198924

00661198925

00641198926

00751198927

00811198928

00781198929

008011989210

006411989211

004711989212

005011989213

007011989214

005611989215

0054

technology and developmentThus to avoid overly subjectiveweights we include the entropy method in the proposedevaluation procedure using (2) through (5) The objectiveweights of the criteria are shown in Table 3 while Table 4indicates the compromised weights of the criteria for eachDM by (6)

After the compromised criteria weights of the DMsare decided we further apply the criteria weights and theperformance to the ELECTRE III method (7) through (10)to evaluate the four polarizer suppliers For each DM theoutranking degree of any two alternatives can be calculatedby (7) and the results are shown in Table 5 For examplefor manager 119863

1 the outranking degree of alternative 1 to

alternative 3 is 0743 and the outranking degree of alternative3 to alternative 1 is 0493 Obviously manager 119863

1believes

8 Mathematical Problems in Engineering

Table 4 Compromised weights of each criterion for DMs

Dept MM RD GPM SQMCriteria 119863

11198632

1198633

1198634

1198635

1198636

1198637

1198638

1198639

11986310

11986311

11986312

1198921

0016 0230 0011 0176 0367 0008 0109 0121 0135 0007 0123 00271198922

0016 0027 0042 0441 0055 0008 0035 0127 0142 0051 0322 01521198923

0037 0048 0013 0077 0170 0038 0046 0057 0034 0017 0035 00501198924

0007 0007 0002 0025 0168 0007 0009 0056 0007 0002 0012 00071198925

0399 0006 0051 0014 0008 0128 0085 0090 0004 0020 0010 00181198926

0093 0001 0060 0005 0003 0030 0033 0022 0001 0005 0002 00211198927

0049 0058 0065 0079 0067 0162 0229 0068 0032 0209 0034 00101198928

0047 0012 0062 0011 0022 0031 0055 0066 0010 0040 0011 00031198929

0079 0012 0060 0007 0018 0087 0084 0095 0012 0009 0019 000311989210

0016 0010 0048 0003 0012 0069 0028 0019 0021 0027 0005 000211989211

0005 0002 0005 0001 0003 0010 0013 0009 0002 0033 0012 000111989212

0134 0400 0350 0018 0061 0149 0056 0183 0211 0010 0062 040111989213

0062 0112 0081 0126 0029 0209 0156 0052 0295 0104 0262 018711989214

0030 0064 0133 0015 0005 0017 0031 0018 0071 008 0023 010011989215

0010 0013 0018 0003 0013 0049 0030 0017 0023 0385 0067 0019

Table 5 Outranking degrees of each DM

1198631

1198632

1198633

1198861

1198862

1198863

1198864

1198861

1198862

1198863

1198864

1198861

1198862

1198863

1198864

1198861

0 0588 0743 0867 1198861

0 0771 0578 0868 1198861

0 0688 0547 08441198862

0521 0 0598 0633 1198862

0310 0 0374 0425 1198862

0409 0 0442 05291198863

0493 0538 0 0762 1198863

0715 0779 0 0803 1198863

0669 0719 0 07631198864

0591 0619 0479 0 1198864

0783 0787 0476 0 1198864

0771 0735 0488 01198634

1198635

1198636

a1 a2 a3 a4 a1 a2 a3 a4 a1 a2 a3 a41198861

0 0785 0839 0862 1198861

0 0790 0804 0858 1198861

0 0635 0685 07701198862

0306 0 0413 0440 1198862

0378 0 0444 0463 1198862

0477 0 0625 06441198863

0673 0750 0 0780 1198863

0650 0678 0 0703 1198863

0531 0632 0 07111198864

0740 0782 0694 0 1198864

0745 0832 0643 0 1198864

0665 0674 0565 01198637

1198638

1198639

1198861

1198862

1198863

1198864

1198861

1198862

1198863

1198864

1198861

1198862

1198863

1198864

1198861

0 0642 0762 0767 1198861

0 0736 0706 0853 1198861

0 0762 0664 08641198862

0473 0 0602 0634 1198862

0404 0 0492 0518 1198862

0320 0 0465 05201198863

0534 0604 0 0667 1198863

0608 0646 0 0712 1198863

0634 0788 0 08091198864

0687 0663 0628 0 1198864

0734 0777 0588 0 1198864

0702 0735 0532 011986310

11986311

11986312

1198861

1198862

1198863

1198864

1198861

1198862

1198863

1198864

1198861

1198862

1198863

1198864

1198861

0 0471 0557 0585 1198861

0 0762 0754 0841 1198861

0 0765 0562 08861198862

0607 0 0671 0661 1198862

0326 0 0491 0509 1198862

0313 0 0387 04551198863

0660 0704 0 0663 1198863

0630 0776 0 0791 1198863

0697 0798 0 08261198864

0780 0713 0706 0 1198864

0704 0761 0644 0 1198864

0749 0762 0453 0

that alternative 1 is superior to alternative 3 However acharacteristic of the ELECTRE III method is that a penaltyis set if the alternative performs the worst for a criterionThus we can use (9) to calculate the rejecting degree dueto the penalty and the overall outranking degree can beobtained by (10) as shown in Table 6 As another examplefor the 119863

6manager the outranking degree of alternative 3

to alternative 2 is 0632 but the overall outranking degreeof alternative 3 to alternative 2 is 0000 This means thatalternative 3 performs too poorly to be accepted based onsome criteria The results presented in Table 7 show thatalternative 3 performs too poorly on quality control (119892

7) and

delivery (1198929) The rejecting degrees for quality control (119892

7)

and delivery (1198929) are 1000 and 0778 respectively which

Mathematical Problems in Engineering 9

Table 6 Overall outranking degree of each DM

1198631

1198632

1198633

1198861

1198862

1198863

1198864

1198861

1198862

1198863

1198864

1198861

1198862

1198863

1198864

1198861

0 0588 0743 0867 1198861

0 0771 0564 0868 1198861

0 0688 0246 08441198862

0342 0 0331 0633 1198862

0026 0 0 0148 1198862

0115 0 0 04151198863

0273 0334 0 0762 1198863

0715 0779 0 0803 1198863

0669 0719 0 07631198864

0563 0 0384 0 1198864

0783 0787 0361 0 1198864

0771 0735 0207 01198634

1198635

1198636

1198861

1198862

1198863

1198864

1198861

1198862

1198863

1198864

1198861

1198862

1198863

1198864

1198861

0 0785 0839 0862 1198861

0 0790 0804 0858 1198861

0 0245 0483 07461198862

0 0 0 0 1198862

0 0 0 0037 1198862

0090 0 0 06441198863

0673 0750 0 0780 1198863

0413 0468 0 0526 1198863

0091 0 0 05461198864

0633 0782 0694 0 1198864

0745 0832 0643 0 1198864

0 0459 0541 01198637

1198638

1198639

1198861

1198862

1198863

1198864

1198861

1198862

1198863

1198864

1198861

1198862

1198863

1198864

1198861

0 0413 0762 0767 1198861

0 0736 0706 0853 1198861

0 0762 0664 08641198862

0054 0 0336 0634 1198862

0037 0 0 0163 1198862

0005 0 0 01661198863

0094 0 0 0445 1198863

0344 0406 0 0550 1198863

0372 0788 0 08091198864

0 0437 0628 0 1198864

0734 0774 0588 0 1198864

0 0735 0474 011986310

11986311

11986312

1198861

1198862

1198863

1198864

1198861

1198862

1198863

1198864

1198861

1198862

1198863

1198864

1198861

0 0072 0123 0 1198861

0 0762 0754 0841 1198861

0 0765 0509 08861198862

0607 0 0671 0661 1198862

0 0 0 0 1198862

0034 0 0 02611198863

0660 0529 0 0663 1198863

0365 0776 0 0791 1198863

0697 0798 0 08261198864

0780 0713 0706 0 1198864

0 0761 0644 0 1198864

0664 0762 0315 0

Table 7 Rejecting degree for alternative 3 to alternative 2 from 1198896manager

1198921

1198922

1198923

1198924

1198925

1198926

1198927

1198928

1198929

11989210

11989211

11989212

11989213

11989214

11989215

Reject degree 0000 0000 0000 0583 0407 0000 1000 0288 0778 0000 0000 0000 0000 0000 0000

are obviously larger than the outranking degree of 0632 Inthe next section we further discuss which criteria shouldbe improved immediately Accordingly we can establish thedescending and ascending distillation for each DM and theranking results are shown in Table 8

Finally LAM is applied to establish linear programmingfor integrating the opinions of each DM According tothe decision-making power of each department the overallranking matrix is shown as follows

Π =[[[

[

06850 01950 00000 01200

00400 01350 00850 07400

02350 04700 02825 00125

00400 02000 06325 01275

]]]

]

(12)

Therefore we can formulate a linear programming modelusing (11) and obtain the solution which indicates that theoverall ranking result is 119886

1≻ 1198863≻ 1198864≻ 1198862The best supplier is

supplier 1 and the subsidiary company of Y-TECH is secondonly to supplier 1 Supplier 4 is ranked third and supplier 2performs the worst in this evaluation

43 Discussion and Implications In this study we aim topropose a hybridMCGDMmethod to evaluate and assist sup-pliers At the first stage we use the AHP method to measurethe subjective weights of the criteria for each DM Based onthe results of the AHP method it is obvious that most ofthe managers value the importance of current capability andRampD capability and this suggests that Y-TECH places a greatemphasis on technology development In the FPD industrydue to the rapid development of new technology many well-known enterprises regularly release new products to marketevery year A challenge for Y-TECH includes the develop-ment of other technologies such as organic light-emittingdisplays (OLED) For example the OLED panel led to thereduction of the demand for small and medium TFT-LCDdisplays in the global market in 2010 Therefore to enhancecompetitiveness TFT-LCD manufactures connecting withthe suppliers in the entire supply chain should continuouslydevelop new technology Quality control is an importantcriterion secondary only to technological capability At theearly stages of applying a new technology to a new productthe yield rate of the new product is low Consequently it isimportant not only to develop the new technology but alsoto maintain a highly qualified product Currently because

10 Mathematical Problems in Engineering

Table 8 Initial evaluation of individual DM

Dept MM RD GPM SQMRanking 119889

11198892

1198893

1198894

1198895

1198896

1198897

1198898

1198899

11988910

11988911

11988912

1st 1198861

1198861 1198863

1198863

1198861

1198861

1198861

1198861

1198861

1198861

1198862 1198863 1198864

1198861

1198861 1198863

2nd 1198862 1198863

1198861

1198863

1198864

1198862

1198862

1198864

1198863

1198863 1198864

3rd 1198864

1198864

1198864

1198863

1198863

1198863 1198864

1198863

1198864

1198864

4th 1198864

1198862

1198862

1198862

1198862

1198864

1198862

1198862

1198861

1198862

1198862

Table 9 The worst performing criteria of each alternative

1198861

1198862

1198863

1198864

1198631

mdash mdash mdash 1198925

1198632

mdash 11989212

mdash mdash1198633

mdash 11989212

mdash mdash1198634

mdash 1198922

mdash mdash1198635

mdash 1198921

mdash mdash1198636

mdash 11989212

g7 11989213

1198637

mdash mdash g7 11989213

1198638

mdash 11989212

mdash mdash1198639

mdash 11989212

mdash 11989213

11986310

g15 mdash mdash mdash11986311

mdash 1198922

mdash 11989213

11986312

mdash 11989212

mdash mdash

GPM department processes less decision-making power forgreen supplier selection it is obvious that environmentalfactors are not significantly considered in the evaluationprocedure with the exception that RampD department shoulddesign new products and the related materials must be listedfor MM department Then MM department can place anorder and ask the suppliers to follow the list SQMdepartmentis concerned with technological capability as well as infor-mation share Vertical integration is an important enterprisepolicy for the TFT-LCD industry and strategic purchase isa method for the implementation of vertical integration Asmentioned before the key components of TFT-LCD panelsthat are produced in-house have gradually increased year byyear so information share is also an important criterion forcultivating the core capability of enterprise and establishinga competitive advantage Furthermore all of the criteriaplay important roles in the evaluation procedure for greensupplier selection If the proposed hybrid MCGDM methodonly adopts the AHP method it will most likely lead to theoversubjective weighting of the criteria Therefore we applythe entropy method to consider the weights of the criteriasimultaneously

ELECTRE III is applied to evaluate the suppliers Duringthe evaluation procedure we compare the outranking degreeand the overall outranking degree and then decide whichcriterion should be improved immediately for each alterna-tive as shown in Table 9 If the overall outranking degreeof a criterion decreases to zero (comparing to outrankingdegree) this criterion should be improved immediately Forthe best supplier supplier 1 (119886

1) the 119863

10manager suggests

that alternative 1 should strengthen communication with Y-TECH by taking actions such as improving cooperation forthe development of new products and instant market infor-mation share (119892

15) In this case study one of the most impor-

tant aspects is how to provide assistance to the subsidiarycompany (119886

3) of Y-TECH Table 9 indicates that if supplier

3 improves quality control (1198927) for polarizer production its

performance will be improved significantly Generally it iscontended that managers should be concerned about criteriarelated to their job description Due to limited enterpriseresources the order of executing enterprise improvementis also an important issue Following the useful proposedmethod the discovery of the criteria that need to be improvedwill become easy

Different from many previous studies this study doesnot integrate the weights of the criteria at the first stageIn fact green supplier selection is a group decision-makingproblem and integrating the weights would also lead tothe loss of information Therefore we further consider theranking results of each department as shown in Table 10Although these four polarizermanufacturers are the suppliersof Y-TECH there are some differences among the rankingresults of each department especially for suppliers 2 and 4 Asmentioned before RampD department placed great emphasison the ldquogreenrdquo criteria and supplier 4rsquos good performance onthese criteria is the reason for the different ranking resultsConsequently in this study we proposed a novel method andviewpoint for green supplier selection and we suggested thatthe weights of the criteria should not be integrated at thefirst stage because further discussions and communicationare necessary to include the different opinions from eachdepartment Additionally we compare the results of theproposed method with the ones implemented by Y-TECHthat is simple additive method in Table 10 Both results showthat supplier 1 has the best performance and is superior tosupplier 3 Supplier 4 is in the third place while supplier2 is the worst Furthermore within the extensive literatureon green supplier selection methods the TOPSIS methodwas widely applied to evaluate green suppliersrsquo performance[15 17ndash22] Thus we further compare the TOPSIS methodwith the proposedmethod and the comparison is establishedon the basis of AHP-entropy criteria weights In the TOPSISmethod with AHP-entropy weights supplier 3 is in the firstplace and superior to supplier 1 and supplier 4 and supplier2 have the worst performance in this evaluation Althoughthe ranking results among the proposed method and othertwo methods are similar the emphasis of this study is onhow to improve the suppliersrsquo performance in the TFT-LCD industry More importantly the results of the proposed

Mathematical Problems in Engineering 11

Table 10 Comparison of ranking results for the proposed method with other methods

Ranking MM RD GPM SQM The proposed method Current methodlowast AHP-entropy TOPSIS1st 119886

31198861

1198861

1198861

1198861

1198861

1198863

2nd 1198861

1198864

1198862

1198863

1198863

1198863

1198861

3rd 1198864

1198863

1198863

1198864

1198864

1198864

1198864

4th 1198862

1198862

1198864

1198862

1198862

1198862

1198862

lowast

The current evaluation method in Y-TECH is simple additive method

method show that each department has its different opinionsand viewpoints of improvement Each department of theTFT-LCDmanufacturers can assist their suppliers within thecontext of business perspectives

5 Conclusion

In this study we propose a hybrid MCGDM method forgreen supplier selection for Y-TECH Our method focuseson not only the selection or ranking but also the providenceof assistance to suppliers to strengthen their competitivenessThis method also emphasizes continuous improvement Dueto the challenges and difficulties the TFT-LCD industriesworldwide face we intend to establish a systematic selectionprocedure for TFT-LCD manufacturers We developed adecision framework for green supplier selection based onliteratures and the supplier audit forms from Y-TECH Weconsidered AHP and the entropy method to measure thecompromised weights of the criteria that involve subjectiveand objective opinions simultaneously After that the ELEC-TRE III method provides polarizer manufacturersrsquo rankingresults for executive managers in addition to improvementreports The acquisition of key components used for in-house production of TFT-LCD panels has gradually becomea strategy for cost reduction and the enhancement of corecapability Thus TFT-LCD manufacturers should strengthentheir cooperation with their suppliers

Based on the results of the ELECTRE III method wecan compare the differences between the outranking degreeand the overall outranking degree Then we further considerwhich criteria should be improved immediately especially forthe suppliers of strategic purchases The results of this studyprovide an important reference for subsidiary companiesthat are seeking to be primary polarizer suppliers Finallydiffering from many of the previous studies we do notintegrate the weights of the criteria with regard to a singleDM in this study In other worlds we integrate the rankingresults of eachDM instead of theweights of the criteria In theproposed evaluation procedure LAM places more emphasison the discussion and communication among a group ofDMs which is consistent with the practical case

In summary the result of this study can be regardedas a problem-solving process for green supplier selectionRegardless of the proposed method or analytic procedurewe aim at helping TFT-LCD manufacturers to establish asystematic and useful evaluation procedure via the currentstudy Because all of the supplier audit forms are in Microsoft

Excel 2010 spreadsheets the proposedmethod is easy to applyusing the same file Executive managers can consider theperformance of all suppliers and refer to the results of theproposed method Three main contributions are made inthis study First depending on the literatures and supplieraudit forms we develop a decision framework for the TFT-LCD industry Second we propose the useful systematicand flexible hybrid MCGDMmethod (based on the businessprocess) and improve the current evaluation procedureThird we place more emphasis on improving suppliers toenhance the competitiveness of entire supply chain especiallythe subsidiary company In practice green supplier selectionis evaluated by each department separately and all criteria areregarded as independent In recent years increasing attentionhas been given to the interdependent relationships amongthe criteria [15 16 26 49] Therefore future studies arenecessary to clarify the interrelations among the criteriaWe hope that more detailed information influencing therelationships between the criteria can be provided in futureresearch Consequently it would bemore efficient to improvethe supplier selection process for the entire supply chain inthe future work

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper

Acknowledgments

This research was partially supported by the National ScienceCouncil of the Republic of China (Grant NSC 102-2221-E-007-084) The authors would like to thank the editorand anonymous referees for their valuable comments andsuggestions

References

[1] Taiwan Stock Exchange ldquoIndustry analysis the flat paneldisplay Industryrdquo February 2013 httpwwwtwsecomtwchproductspublicationessayphp

[2] S Hung ldquoCompetitive strategies for Taiwanrsquos thin film tran-sistor-liquid crystal display (TFT-LCD) industryrdquoTechnology inSociety vol 28 no 3 pp 349ndash361 2006

[3] W Ho X Xu and P K Dey ldquoMulti-criteria decision makingapproaches for supplier evaluation and selection a literaturereviewrdquo European Journal of Operational Research vol 202 no1 pp 16ndash24 2010

12 Mathematical Problems in Engineering

[4] J Chai J N K Liu and E W T Ngai ldquoApplication of decision-making techniques in supplier selection a systematic review ofliteraturerdquo Expert Systems with Applications vol 40 no 10 pp3872ndash3885 2013

[5] K Govindan S Rajendran J Sarkis and P Murugesan ldquoMulticriteria decision making approaches for green supplier eval-uation and selection a literature reviewrdquo Journal of CleanerProduction 2013

[6] M Herva and E Roca ldquoReview of combined approaches andmulti-criteria analysis for corporate environmental evaluationrdquoJournal of Cleaner Production vol 39 pp 355ndash371 2013

[7] S Seuring ldquoA review of modeling approaches for sustainablesupply chain managementrdquo Decision Support Systems vol 54no 4 pp 1513ndash1520 2013

[8] M Igarashi L De Boer and A M Fet ldquoWhat is required forgreener supplier selection A literature review and conceptualmodel developmentrdquo Journal of Purchasing and SupplyManage-ment vol 19 no 4 pp 247ndash263 2013

[9] A Genovese S C Koh G Bruno and E Esposito ldquoGreenersupplier selection state of the art and some empirical evidencerdquoInternational Journal of Production Research vol 51 no 10 pp2868ndash2886 2013

[10] A H I Lee H Y Kang C F Hsu and H C Hung ldquoA greensupplier selectionmodel for high-tech industryrdquo Expert Systemswith Applications vol 36 no 4 pp 7917ndash7927 2009

[11] G Tuzkaya A Ozgen D Ozgen and U R Tuzkaya ldquoEnvi-ronmental performance evaluation of suppliers a hybrid fuzzymulti-criteria decision approachrdquo International Journal of Envi-ronmental Science and Technology vol 6 no 3 pp 477ndash4902009

[12] R J Kuo Y C Wang and F C Tien ldquoIntegration of artificialneural network and MADA methods for green supplier selec-tionrdquo Journal of Cleaner Production vol 18 no 12 pp 1161ndash11702010

[13] Q Zhu Y Dou and J Sarkis ldquoA portfolio-based analysis forgreen supplier management using the analytical network pro-cessrdquo Supply Chain Management vol 15 no 4 pp 306ndash3192010

[14] G Buyukozkan ldquoAn integrated fuzzy multi-criteria groupdecision-making approach for green supplier evaluationrdquo Inter-national Journal of Production Research vol 50 no 11 pp 2892ndash2909 2012

[15] G Buyukozkan and G Cifci ldquoA novel hybrid MCDM approachbased on fuzzy DEMATEL fuzzy ANP and fuzzy TOPSIS toevaluate green suppliersrdquo Expert Systems with Applications vol39 no 3 pp 3000ndash3011 2012

[16] C H Hsu F KWang and G H Tzeng ldquoThe best vendor selec-tion for conducting the recycled material based on a hybridMCDM model combining DANP with VIKORrdquo ResourcesConservation and Recycling vol 66 pp 95ndash111 2012

[17] K Govindan R Khodaverdi and A Jafarian ldquoA fuzzy multicriteria approach for measuring sustainability performance ofa supplier based on triple bottom line approachrdquo Journal ofCleaner Production vol 47 pp 345ndash354 2013

[18] L Shen L Olfat K Govindan R Khodaverdi and A DiabatldquoA fuzzy multi criteria approach for evaluating green supplierrsquosperformance in green supply chain with linguistic preferencesrdquoResources Conservation and Recycling vol 74 pp 170ndash179 2013

[19] M Yazdani ldquoAn integrated MCDM approach to green supplierselectionrdquo International Journal of Industrial Engineering Com-putations vol 5 no 3 pp 443ndash458 2014

[20] T Chen and J Freeman ldquoUsing AHP-Entropy weight and TOP-SIS methodology in green supplier selectionrdquo in Proceedings ofthe European Operations Management Association Conference(EurOMA rsquo14) Palermo Italy June 2014

[21] D Kannan A B L D S Jabbour andC J C Jabbour ldquoSelectinggreen suppliers based on GSCM practices using fuzzy TOPSISapplied to a Brazilian electronics companyrdquo European Journal ofOperational Research vol 233 no 2 pp 432ndash447 2014

[22] H Zhao and S Guo ldquoSelecting green supplier of thermalpower equipment by using a Hybrid MCDM Method forsustainabilityrdquo Sustainability vol 6 no 1 pp 217ndash235 2014

[23] J Figueira S Greco and M EhrgottMultiple Criteria DecisionAnalysis State of the Art Surveys Springer Science BostonMass USA 2005

[24] B Roy ldquoThe outranking approach and the foundations ofELECTREmethodsrdquoTheory and Decision vol 31 no 1 pp 49ndash73 1991

[25] J Hokkanen and P Salminen ldquoChoosing a solid waste man-agement systemusingmulticriteria decision analysisrdquoEuropeanJournal of Operational Research vol 98 no 1 pp 19ndash36 1997

[26] R J Lin ldquoUsing fuzzy DEMATEL to evaluate the green supplychain management practicesrdquo Journal of Cleaner Productionvol 40 pp 32ndash39 2013

[27] M L Tseng and A S F Chiu ldquoEvaluating firmrsquos green supplychain management in linguistic preferencesrdquo Journal of CleanerProduction vol 40 pp 22ndash31 2013

[28] G Buyukozkan and G Cifci ldquoA novel fuzzy mulati -criteriadecision framework for sustainable supplier selection withincomplete informationrdquo Computers in Industry vol 62 no 2pp 164ndash174 2011

[29] M T Escobar and J M Moreno-Jimenez ldquoAggregation of indi-vidual preference structures in AHP-group decision makingrdquoGroup Decision andNegotiation vol 16 no 4 pp 287ndash301 2007

[30] V Tsyganok ldquoInvestigation of the aggregation effectiveness ofexpert estimates obtained by the pairwise comparisonmethodrdquoMathematical and ComputerModelling vol 52 no 3-4 pp 538ndash544 2010

[31] J J Bernardo and J M Blin ldquoA programming model ofconsumer choice among multi-attributed brandsrdquo Journal ofConsumer Research vol 4 no 2 pp 111ndash118 1977

[32] P K Humphreys Y K Wong and F T S Chan ldquoIntegratingenvironmental criteria into the supplier selection processrdquoJournal of Materials Processing Technology vol 138 no 1ndash3 pp349ndash356 2003

[33] C Bai and J Sarkis ldquoIntegrating sustainability into supplierselection with grey system and rough set methodologiesrdquoInternational Journal of Production Economics vol 124 no 1 pp252ndash264 2010

[34] X Fu Q Zhu and J Sarkis ldquoEvaluating green supplier devel-opment programs at a telecommunications systems providerrdquoInternational Journal of Production Economics vol 140 no 1pp 357ndash367 2012

[35] A Amindoust S Ahmed A Saghafinia and A BahreininejadldquoSustainable supplier selection a ranking model based on fuzzyinference systemrdquoApplied Soft Computing Journal vol 12 no 6pp 1668ndash1677 2012

[36] K Shaw R Shankar S S Yadav and L S Thakur ldquoSupplierselection using fuzzy AHP and fuzzy multi-objective linearprogramming for developing low carbon supply chainrdquo ExpertSystems with Applications vol 39 no 9 pp 8182ndash8192 2012

Mathematical Problems in Engineering 13

[37] A Awasthi S S Chauhan and S K Goyal ldquoA fuzzy multi-criteria approach for evaluating environmental performance ofsuppliersrdquo International Journal of Production Economics vol126 no 2 pp 370ndash378 2010

[38] T Y Chiou H K Chan F Lettice and S H Chung ldquoThe influ-ence of greening the suppliers and green innovation on envi-ronmental performance and competitive advantage in TaiwanrdquoTransportation Research Part E Logistics and TransportationReview vol 47 no 6 pp 822ndash836 2011

[39] T L Saaty ldquoA scaling method for priorities in hierarchicalstructuresrdquo Journal of Mathematical Psychology vol 15 no 3pp 234ndash281 1977

[40] T L Saaty The Analytic Hierarchy Process McGraw-Hill NewYork NY USA 1980

[41] CMacharis J Springael K De Brucker andA Verbeke ldquoPRO-METHEE and AHP the design of operational synergies inmulticriteria analysis Strengthening PROMETHEE with ideasof AHPrdquo European Journal of Operational Research vol 153 no2 pp 307ndash317 2004

[42] M Zeleny Multiple Criteria Decision Making McGraw-HillNew York NY USA 1982

[43] C L Hwang and K Yoon Multiple Attribute Decision MakingMethods and Applications A State-of-the-Art Survey SpringerNew York NY USA 1981

[44] B Roy ldquoELECTRE III un algorithme de classements fondesur une repre sentation floue des preferences en preferences enpresence de criteres multiplesrdquo Cahiers du Centre drsquoEtudes deRecherche Operationnelle vol 20 no 1 pp 3ndash24 1978

[45] P Vincke Multiple Criteria Decision-Aid John Wiley amp SonsChichester UK 1992

[46] M Rogers and M Bruen ldquoUsing ELECTRE III to chooseroute for Dublin Port motorwayrdquo Journal of TransportationEngineering vol 126 no 4 pp 313ndash323 2000

[47] M Rogers and M Bruen ELECTRE and Decision SupportMethods and Applications in Engineering and InfrastructureInvestment Kluwer Academic Publishers Boston Mass USA2000

[48] V Belton and T J Stewart Multiple Criteria Decision AnalysisAn Integrated Approach Kluwer Academic Publishers BostonMass USA 2002

[49] B Chang C W Chang and C H Wu ldquoFuzzy DEMATELmethod for developing supplier selection criteriardquo Expert Sys-tems with Applications vol 38 no 3 pp 1850ndash1858 2011

Submit your manuscripts athttpwwwhindawicom

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

MathematicsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Mathematical Problems in Engineering

Hindawi Publishing Corporationhttpwwwhindawicom

Differential EquationsInternational Journal of

Volume 2014

Applied MathematicsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Probability and StatisticsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Mathematical PhysicsAdvances in

Complex AnalysisJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

OptimizationJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

CombinatoricsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Operations ResearchAdvances in

Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Function Spaces

Abstract and Applied AnalysisHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of Mathematics and Mathematical Sciences

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Algebra

Discrete Dynamics in Nature and Society

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

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Decision SciencesAdvances in

Discrete MathematicsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom

Volume 2014 Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Stochastic AnalysisInternational Journal of

4 Mathematical Problems in Engineering

denoted as (1198631 119863

119905 119863

119897)The weights of each criterion

for DMs are 119908119905119895and 119909

119894119895 representing the performance value

of alternative 119886119894under criterion 119892

119895 The AHP method the

entropy method the ELECTRE III method and LAM aredescribed as follows

31 The AHP Method for Determining the Subjective WeightsSaaty [39 40] used AHP to determine the DMrsquos subjectiveweight in a hierarchical structure All decision problemscan be considered as having a hierarchical structure Theimportant advantage of AHP is that a decision problemcan be decomposed into a number of subsystems [41] anda complicated decision problem can be systematized to ahierarchical structure The hierarchical structure starts witha goal and then moves to the intermediate level containingaspects and criteria and the alternatives are at the bottom Inthis study the first level is the goal for the overall objectivesof the problem for green supplier selection The second levelis decomposed into three aspects and each aspect can alsobe decomposed into several criteria as shown in Table 1If the criteria at the low levels exist the criteria at thelower levels can be generated based on the same principleAfter constructing the hierarchical structure of the problemfor green supplier selection the next step is to survey thecomparative weights among the aspectscriteria with a 1ndash9 point scale ranging from equally important to extremelyimportant to build the comparison matrices The subjectiveweight of criterion 119895 can be found with respect to themaximum eigenvalue of the comparative matrix for DM 119905which can be expressed as 1199081015840

119905119895 Then we can further check

the consistency index (CI) and consistency ratio (CR) ofeach comparison matrix via the following equation

CI =120582max minus 119899

119899 minus 1

CR = CIRI

(1)

The CI and CR values should not be larger than 01 for aconfident result If CI or CR value is larger than 01 we willinterview manager or assistant manager for the inconsistentcomparative weight and further adjust the value

32 The Entropy Method for Determining Objective WeightsThe entropymethod is used to evaluate the objective weightswhich is an important concept in information theory [42]This concept measures the expected information content ofa specific message [43] If the entropy measure is larger theinformation contained is less Therefore we can decide theobjective weight of each criterion based on the informationcontained in the decision matrix First a decision matrixis a matrix whose elements express the performance of analternative with respect to a criterion Then the decisionmatrix can be normalized in a linear manner which can bedescribed as

119903119894119895=

119909119894119895

max119895119909119894119895

119894 = 1 2 119898 119895 = 1 2 119899 (2)

where

119909119894119895=

119909119894119895 if 119892

119895is a benefit criterion

1

119909119894119895

if 119892119895is a cost criterion

(3)

Then the degree of diversification of the normalized decisionmatrix can be considered using the following equation

120575119895= 1 + 119887

119898

sum

119894=1

119903119894119895ln 119903119894119895 119895 = 1 2 119899 (4)

where 119887 = 1 ln119898 and 119887 is a constantFinally the objective weight of criterion 119895 can be obtained

as follows

11990810158401015840

119895=

120575119895

sum119899

119894=1120575119895

119895 = 1 2 119899 (5)

In this study according to the subjective weights and objec-tive weights the compromised weights of criterion 119895 for DM119905 can be expressed as

119908119905119895=

1199081015840

11990511989511990810158401015840

119895

sum119899

119895=1119908101584011990511989511990810158401015840119895

119905 = 1 2 119897 119895 = 1 2 119899 (6)

33 The ELECTRE III Method to Evaluate the Performance ofSuppliers The ELECTRE III method proposed by Roy [44]was designed to address inaccurate imprecise uncertain orill-determined data such as qualitative data ELECTRE IIInot only evaluates the best choice but also presents a specificranking result and leaves the final selection to the DMsFor the issue of green supplier selection ELECTRE III ischosen as a suitable method and it involves aspects that areoften neglected by othermethods for yielding relatively stableresults [45] Before we illustrate ELECTRE III we definethree threshold values that establish the DMrsquos preferencefor each criterion 119895 including indifference preference andveto thresholds The indifference threshold (119902

119895) indicates a

gap between the evaluation scores that are still compatiblewith a situation of indifference The preference threshold(119901119895) expresses the minimum difference between the values

of criterion 119895 to which the DM attributes significance interms of strict preference The veto threshold (V

119895) expresses

the minimum difference between the values of criterion 119895

beyond which the DM believes the gap between the twoscores cannot be compensated by the good performance ofthe other criteria In this study these thresholds for eachcriterion were set by interviews with DMs Then outrankingdegree can be defined as that alternative 119894 outranks alternative119896 forDM 119905 which can be calculated by the following equation

119862119905(119886119894 119886119896) =

1

119908

119899

sum

119895=1

119908119905119895119888119895(119886119894 119886119896) (7)

Mathematical Problems in Engineering 5

where

119908 =

119899

sum

119895=1

119908119905119895

119888119895(119886119894 119886119896) =

1 if 119909119894119895+ 119902119895ge 119909119896119895

0 if 119909119894119895+ 119901119895le 119909119896119895

119901119895+ 119909119894119895minus 119909119896119895

119901119895minus 119902119895

otherwise

(8)

We then consider rejecting degree which means alternative 119894is dominated by alternative 119896 under criterion 119895 as follows

119889119895(119886119894 119886119896)

=

1 if 119892119895(119886119896) ge 119892119895(119886119894) + V119895

0 if 119892119895(119886119896) le 119892119895(119886119894) + 119901119895

119892119895(119886119896) minus 119892119895(119886119894) minus 119901119895

V119895minus 119901119895

otherwise

(9)

Therefore the overall outranking degree of DM 119905 can beconsidered with outranking degree and rejecting degreewhich is called overall outranking degree as indicated in thefollowing equation

119878119905(119886119894 119886119896)

=

119862119905(119886119894 119886119896) if 119889

119895(119886119894 119886119896)

le 119862119905(119886119894 119886119896) forall119895

119862119905(119886119894 119886119896)

times prod

119895isin119869(119886119894 119886119896)

1 minus 119889119895(119886119894 119886119896)

1 minus 119862119905(119886119894 119886119896) otherwise

(10)

where 119869(119886119894 119886119896) is the set of criteria such that 119889

119895(119886119894 119886119896) gt

119862119905(119886119894 119886119896)

The final step is to exploit the model and produce aranking result from the overall outranking degrees The gen-eral approach for exploitation is to construct two preorders1198851and 119885

2using the descending and ascending distillation

process and then combine these two to produce a partialpreorder 119885 = 119885

1cap 1198852 The descending process is to clas-

sify the alternatives from the best to the worst while theascending process is from the worst to the best [46ndash48]

34 The LAM for Integrating Ranking Results of DMs Ber-nardo and Blin [31] developed LAM to transform individualranking result into an overall ranking result Therefore LAMcan integrate the ranking result of each DM into an overallranking result We can apply this simple method to sum theranking score from each DM and rank the overall score fromthe lowest score to the highest score [43] If an alternativeis ranked as the first order the alternative gets one pointIf an alternative is ranked as the second it gets two pointsand so on On the other hand LAM also considers the

weights of the DMs to integrate the overall ranking resultIn our case study we consider the weights of DMs basedon their positions For example the RampD department hasapproximately 27 of the decision-making power for greensupplier selection so the weight of the RampD departmentis 027 in the procedure of green supplier selection LAMcan be illustrated as follows First an overall ranking matrixis established as Π = [120587

119894119904] where 120587

119894119904represents the

weighted frequency that 119886119894is ranked the 119904th with the different

weights of the DMs Then depending on the above overallranking matrix we formulate a linear programming modelto optimize the permutationThe linear programmingmodeluses the binary decision variables 120593

119894119904 The decision variable

120593119894119904

= 1 means 119886119894is assigned to the 119904th overall rank and

120593119894119904= 0 otherwise Obviously one alternative can be assigned

to only one rank and one rank can only be assigned by onealternative Therefore the linear programming model can bewritten as follows

max119898

sum

119894=1

119898

sum

119891=1

120587119894119904120593119894119904

subject to119898

sum

119894=1

120593119894119904= 1 119904 = 1 2 119898

119898

sum

119891=1

120593119894119904= 1 119894 = 1 2 119898

120593119894119904isin 0 1 119894 119904 = 1 2 119898

(11)

4 Application Case

Y-TECH is the first manufacturer in Taiwan to mass-produceTFT-LCD panels and it is also one of the top five TFT-LCD panel manufacturers in the world By utilizing thedata coming from Y-TECH we aimed at providing a usefulevaluation method with continuous improvement Becauseof the global competitive market the well-known TFT-LCD manufacturers hope to develop and invest in theirsuppliers especially for some of the important componentsfor TFT-LCD panel such as the color filter back-lightunit and polarizer Because good suppliers may affect themanufacturerrsquos performance and competitiveness and theentire supply chain in this study we demonstrate the supplierevaluation procedure and provide information about supplierassistance for TFT-LCD manufacturers

41 Study Background In the polarizer industry the polar-izer manufacturer of the highest market share is in SouthKorea while the Japanese polarizer manufacturer has thesecond highestmarket share In recent years Y-TECHmainlydepended on materials and technical support from Japanesepolarizer manufacturers Also the Korean polarizer manu-facturers have aggressively intervened in the supply chainof Taiwanese TFT-LCD industry Y-TECH has started toreconsider its supplier evaluation procedure and involvedthe group of GPM department in auditing and evaluatingits suppliers Figure 1 shows the new decision procedure for

6 Mathematical Problems in Engineering

Quarterly business reviewQuarterly quality review

Annual and audit

Qualified greensuppliers lists

MM material managementSQM supplier quality managementESH environment safety healthHR human resourceRD research and designGPM green product management

SQMESHHR

SQM (36)MM (27)RD (27)GPM (10)

SQMESHHR

1st tier2nd tierfreeze

New green suppliers

Survey and audit

MMSQM

Figure 1 The decision procedure of green supplier selection in the case study

green supplier selection at Y-TECH Before being a formalpolarizer supplier in Y-TECH the candidate companiesshould pass a two-stage assessment At the first stage anew polarizer supplier should accept a preliminary surveyby the teams of MM and SQM departments and then theteams of SQM ESH and HR departments further executethe preliminary audit for the polarizer suppliers A qualifiedpolarizer manufacturera should pass at least 70 of the itemson all supplier audit forms At the second stage the formalevaluation is conducted by the teams of SQM MM RD andGPM departments After the second-stage assessment thequalified polarizer suppliers will be ranked in the first tierif they pass more than 80 of the items on supplier auditforms and the suppliers passing 70ndash80 of the items willbe ranked in the second tier If the qualified polarizer supplierpasses fewer than 70 of the items it will be regarded asdisqualified All of the formal suppliers are reviewed regularlyby the teams of SQM ESH and HR departments Further-more IS9001 ISO14001 and OHSAS18001 certifications arenot necessary in the evaluation procedure because Y-TECHwill assist their formal suppliers to apply for environmentalcertifications within a limited timeThe first-stage assessmentis the preliminary survey and audit so we focus on applyingthe proposed MCGDM method to the formal evaluation forthe assessment at the second stage

In this case study although four qualified polarizer sup-pliers for Y-TECH outperformed in all other suppliers for thefirst-stage survey our focus will be on supplier 3 because it isa subsidiary company for Y-TECH In fact strategic purchaseis an important strategy to enhance competitiveness in theTFT-LCD industry Therefore the decision procedure is notonly to select the subsidiary into the supply chain but alsoto provide improvement reports for the subsidiary companyAdditionally it is expected that the subsidiary supplier willbe the primary one In addition to the three aspects andfifty criteria (as mentioned in Section 2) we also surveyed12 managers on the expert committee In practice the SQMdepartment holds 36 of the decision-making power theMM and RampD departments 27 and the GPM department10 to select suitable suppliers during the decision-makingprocess

42 Empirical Results First we designed a questionnaire for12 experts to measure the comparative weights between theaspects and criteria Table 2 presents the subjective weights ofeach criterion for DMs based on AHPThemanagers of MMRD GPM and SQM departments are 119863

1 1198632 1198633 1198634 1198635

1198636 1198637 1198638 1198639 and 119863

10 11986311 11986312 respectively Then we

focused on the criteria weights that were 15 more thanthe average weight (01) Notice that many managers think

Mathematical Problems in Engineering 7

Table 2 Subjective weights of each criterion for DMs

Dept MM RD GPM SQMCriteria 119863

11198632

1198633

1198634

1198635

1198636

1198637

1198638

1198639

11986310

11986311

11986312

1198921

0014 0192 0009 0177 0349 0007 0106 0111 0120 0006 0117 00221198922

0014 0021 0033 0421 0050 0007 0032 0111 0120 0042 0290 01191198923

0036 0043 0012 0083 0174 0037 0048 0056 0033 0016 0036 00441198924

0007 0006 0002 0028 0174 0007 0010 0056 0007 0002 0012 00061198925

0405 0006 0048 0016 0009 0131 0093 0093 0004 0019 0011 00171198926

0081 0001 0048 0005 0003 0026 0031 0019 0001 0004 0002 00171198927

0039 0043 0048 0071 0057 0131 0197 0056 0025 0161 0029 00071198928

0039 0009 0048 0010 0019 0026 0049 0056 0008 0032 0010 00021198929

0064 0009 0045 0006 0015 0071 0073 0079 0010 0007 0016 000211989210

0016 0009 0045 0003 0013 0071 0031 0020 0021 0026 0005 000211989211

0007 0002 0006 0001 0004 0014 0019 0013 0003 0044 0018 000111989212

0174 0480 0421 0026 0083 0196 0078 0243 0270 0013 0085 047611989213

0058 0096 0070 0130 0028 0196 0155 0049 0270 0093 0256 015911989214

0035 0069 0143 0019 0006 0020 0039 0021 0081 0089 0028 010611989215

0012 0014 0020 0004 0017 0059 0039 0021 0027 0445 0085 0021

highly of the current capability (11989212) and RampD capability

(11989213) The managers of MM department generally place

importance on the current capability (11989212) and the weights

of 1198631 1198632 and 119863

3are 0174 0480 and 0421 respectively

Moreover MM department is also concerned with the price(1198925) and the average weight is 0153 It is generally agreed

that the managers of RampD department put emphasis onenvironmental factors because of their relevance to productdesign Taking strategic fit (119892

2) for example suppliers should

review environment-related substance list regularly whendeveloping a green productThe average weights of safety andhealth (119892

1) strategic fit (119892

2) environmental control (119892

3) and

recovery (1198924) are 0263 0236 0129 and 0101 respectively

and the importance of environmental factors is over 70 ofthe total weight

In particular GPM department does not give moreweight to environmental factors and the average weight ofenvironmental factors is only 0054 The most likely reasonis that the GPM department holds less decision-makingpower GPM department is concerned with quality control(1198927) current capability (119892

12) and RampD capability (119892

13) and

the average weights are 0102 0196 and 0168 respectivelyOverall themanagers ofGPMdepartment are still concernedwith the current capability (119892

12) and theRampDcapability (119892

13)

Finally SQM department focuses on the strategic fit (1198922)

current capability (11989212) RampD capability (119892

13) and informa-

tion share (11989215) and the average weights of these factors

are 0150 0191 0169 and 0184 respectively Based on ourexpectation SQM department is not significantly concernedwith quality control (119892

7) or other criteria of the enterprise

operation and the average weight for quality control (1198927)

is only 0066 However SQM department emphasizes theimportance of information share (119892

15) Information share

brings the benefit of enhancing the competitiveness of theentire supply chain especially technology development Insum SQM department focuses on the criteria of strategic

Table 3 Objective weights of each criterion for individual DM

Criteria Objective weights1198921

00721198922

00761198923

00671198924

00661198925

00641198926

00751198927

00811198928

00781198929

008011989210

006411989211

004711989212

005011989213

007011989214

005611989215

0054

technology and developmentThus to avoid overly subjectiveweights we include the entropy method in the proposedevaluation procedure using (2) through (5) The objectiveweights of the criteria are shown in Table 3 while Table 4indicates the compromised weights of the criteria for eachDM by (6)

After the compromised criteria weights of the DMsare decided we further apply the criteria weights and theperformance to the ELECTRE III method (7) through (10)to evaluate the four polarizer suppliers For each DM theoutranking degree of any two alternatives can be calculatedby (7) and the results are shown in Table 5 For examplefor manager 119863

1 the outranking degree of alternative 1 to

alternative 3 is 0743 and the outranking degree of alternative3 to alternative 1 is 0493 Obviously manager 119863

1believes

8 Mathematical Problems in Engineering

Table 4 Compromised weights of each criterion for DMs

Dept MM RD GPM SQMCriteria 119863

11198632

1198633

1198634

1198635

1198636

1198637

1198638

1198639

11986310

11986311

11986312

1198921

0016 0230 0011 0176 0367 0008 0109 0121 0135 0007 0123 00271198922

0016 0027 0042 0441 0055 0008 0035 0127 0142 0051 0322 01521198923

0037 0048 0013 0077 0170 0038 0046 0057 0034 0017 0035 00501198924

0007 0007 0002 0025 0168 0007 0009 0056 0007 0002 0012 00071198925

0399 0006 0051 0014 0008 0128 0085 0090 0004 0020 0010 00181198926

0093 0001 0060 0005 0003 0030 0033 0022 0001 0005 0002 00211198927

0049 0058 0065 0079 0067 0162 0229 0068 0032 0209 0034 00101198928

0047 0012 0062 0011 0022 0031 0055 0066 0010 0040 0011 00031198929

0079 0012 0060 0007 0018 0087 0084 0095 0012 0009 0019 000311989210

0016 0010 0048 0003 0012 0069 0028 0019 0021 0027 0005 000211989211

0005 0002 0005 0001 0003 0010 0013 0009 0002 0033 0012 000111989212

0134 0400 0350 0018 0061 0149 0056 0183 0211 0010 0062 040111989213

0062 0112 0081 0126 0029 0209 0156 0052 0295 0104 0262 018711989214

0030 0064 0133 0015 0005 0017 0031 0018 0071 008 0023 010011989215

0010 0013 0018 0003 0013 0049 0030 0017 0023 0385 0067 0019

Table 5 Outranking degrees of each DM

1198631

1198632

1198633

1198861

1198862

1198863

1198864

1198861

1198862

1198863

1198864

1198861

1198862

1198863

1198864

1198861

0 0588 0743 0867 1198861

0 0771 0578 0868 1198861

0 0688 0547 08441198862

0521 0 0598 0633 1198862

0310 0 0374 0425 1198862

0409 0 0442 05291198863

0493 0538 0 0762 1198863

0715 0779 0 0803 1198863

0669 0719 0 07631198864

0591 0619 0479 0 1198864

0783 0787 0476 0 1198864

0771 0735 0488 01198634

1198635

1198636

a1 a2 a3 a4 a1 a2 a3 a4 a1 a2 a3 a41198861

0 0785 0839 0862 1198861

0 0790 0804 0858 1198861

0 0635 0685 07701198862

0306 0 0413 0440 1198862

0378 0 0444 0463 1198862

0477 0 0625 06441198863

0673 0750 0 0780 1198863

0650 0678 0 0703 1198863

0531 0632 0 07111198864

0740 0782 0694 0 1198864

0745 0832 0643 0 1198864

0665 0674 0565 01198637

1198638

1198639

1198861

1198862

1198863

1198864

1198861

1198862

1198863

1198864

1198861

1198862

1198863

1198864

1198861

0 0642 0762 0767 1198861

0 0736 0706 0853 1198861

0 0762 0664 08641198862

0473 0 0602 0634 1198862

0404 0 0492 0518 1198862

0320 0 0465 05201198863

0534 0604 0 0667 1198863

0608 0646 0 0712 1198863

0634 0788 0 08091198864

0687 0663 0628 0 1198864

0734 0777 0588 0 1198864

0702 0735 0532 011986310

11986311

11986312

1198861

1198862

1198863

1198864

1198861

1198862

1198863

1198864

1198861

1198862

1198863

1198864

1198861

0 0471 0557 0585 1198861

0 0762 0754 0841 1198861

0 0765 0562 08861198862

0607 0 0671 0661 1198862

0326 0 0491 0509 1198862

0313 0 0387 04551198863

0660 0704 0 0663 1198863

0630 0776 0 0791 1198863

0697 0798 0 08261198864

0780 0713 0706 0 1198864

0704 0761 0644 0 1198864

0749 0762 0453 0

that alternative 1 is superior to alternative 3 However acharacteristic of the ELECTRE III method is that a penaltyis set if the alternative performs the worst for a criterionThus we can use (9) to calculate the rejecting degree dueto the penalty and the overall outranking degree can beobtained by (10) as shown in Table 6 As another examplefor the 119863

6manager the outranking degree of alternative 3

to alternative 2 is 0632 but the overall outranking degreeof alternative 3 to alternative 2 is 0000 This means thatalternative 3 performs too poorly to be accepted based onsome criteria The results presented in Table 7 show thatalternative 3 performs too poorly on quality control (119892

7) and

delivery (1198929) The rejecting degrees for quality control (119892

7)

and delivery (1198929) are 1000 and 0778 respectively which

Mathematical Problems in Engineering 9

Table 6 Overall outranking degree of each DM

1198631

1198632

1198633

1198861

1198862

1198863

1198864

1198861

1198862

1198863

1198864

1198861

1198862

1198863

1198864

1198861

0 0588 0743 0867 1198861

0 0771 0564 0868 1198861

0 0688 0246 08441198862

0342 0 0331 0633 1198862

0026 0 0 0148 1198862

0115 0 0 04151198863

0273 0334 0 0762 1198863

0715 0779 0 0803 1198863

0669 0719 0 07631198864

0563 0 0384 0 1198864

0783 0787 0361 0 1198864

0771 0735 0207 01198634

1198635

1198636

1198861

1198862

1198863

1198864

1198861

1198862

1198863

1198864

1198861

1198862

1198863

1198864

1198861

0 0785 0839 0862 1198861

0 0790 0804 0858 1198861

0 0245 0483 07461198862

0 0 0 0 1198862

0 0 0 0037 1198862

0090 0 0 06441198863

0673 0750 0 0780 1198863

0413 0468 0 0526 1198863

0091 0 0 05461198864

0633 0782 0694 0 1198864

0745 0832 0643 0 1198864

0 0459 0541 01198637

1198638

1198639

1198861

1198862

1198863

1198864

1198861

1198862

1198863

1198864

1198861

1198862

1198863

1198864

1198861

0 0413 0762 0767 1198861

0 0736 0706 0853 1198861

0 0762 0664 08641198862

0054 0 0336 0634 1198862

0037 0 0 0163 1198862

0005 0 0 01661198863

0094 0 0 0445 1198863

0344 0406 0 0550 1198863

0372 0788 0 08091198864

0 0437 0628 0 1198864

0734 0774 0588 0 1198864

0 0735 0474 011986310

11986311

11986312

1198861

1198862

1198863

1198864

1198861

1198862

1198863

1198864

1198861

1198862

1198863

1198864

1198861

0 0072 0123 0 1198861

0 0762 0754 0841 1198861

0 0765 0509 08861198862

0607 0 0671 0661 1198862

0 0 0 0 1198862

0034 0 0 02611198863

0660 0529 0 0663 1198863

0365 0776 0 0791 1198863

0697 0798 0 08261198864

0780 0713 0706 0 1198864

0 0761 0644 0 1198864

0664 0762 0315 0

Table 7 Rejecting degree for alternative 3 to alternative 2 from 1198896manager

1198921

1198922

1198923

1198924

1198925

1198926

1198927

1198928

1198929

11989210

11989211

11989212

11989213

11989214

11989215

Reject degree 0000 0000 0000 0583 0407 0000 1000 0288 0778 0000 0000 0000 0000 0000 0000

are obviously larger than the outranking degree of 0632 Inthe next section we further discuss which criteria shouldbe improved immediately Accordingly we can establish thedescending and ascending distillation for each DM and theranking results are shown in Table 8

Finally LAM is applied to establish linear programmingfor integrating the opinions of each DM According tothe decision-making power of each department the overallranking matrix is shown as follows

Π =[[[

[

06850 01950 00000 01200

00400 01350 00850 07400

02350 04700 02825 00125

00400 02000 06325 01275

]]]

]

(12)

Therefore we can formulate a linear programming modelusing (11) and obtain the solution which indicates that theoverall ranking result is 119886

1≻ 1198863≻ 1198864≻ 1198862The best supplier is

supplier 1 and the subsidiary company of Y-TECH is secondonly to supplier 1 Supplier 4 is ranked third and supplier 2performs the worst in this evaluation

43 Discussion and Implications In this study we aim topropose a hybridMCGDMmethod to evaluate and assist sup-pliers At the first stage we use the AHP method to measurethe subjective weights of the criteria for each DM Based onthe results of the AHP method it is obvious that most ofthe managers value the importance of current capability andRampD capability and this suggests that Y-TECH places a greatemphasis on technology development In the FPD industrydue to the rapid development of new technology many well-known enterprises regularly release new products to marketevery year A challenge for Y-TECH includes the develop-ment of other technologies such as organic light-emittingdisplays (OLED) For example the OLED panel led to thereduction of the demand for small and medium TFT-LCDdisplays in the global market in 2010 Therefore to enhancecompetitiveness TFT-LCD manufactures connecting withthe suppliers in the entire supply chain should continuouslydevelop new technology Quality control is an importantcriterion secondary only to technological capability At theearly stages of applying a new technology to a new productthe yield rate of the new product is low Consequently it isimportant not only to develop the new technology but alsoto maintain a highly qualified product Currently because

10 Mathematical Problems in Engineering

Table 8 Initial evaluation of individual DM

Dept MM RD GPM SQMRanking 119889

11198892

1198893

1198894

1198895

1198896

1198897

1198898

1198899

11988910

11988911

11988912

1st 1198861

1198861 1198863

1198863

1198861

1198861

1198861

1198861

1198861

1198861

1198862 1198863 1198864

1198861

1198861 1198863

2nd 1198862 1198863

1198861

1198863

1198864

1198862

1198862

1198864

1198863

1198863 1198864

3rd 1198864

1198864

1198864

1198863

1198863

1198863 1198864

1198863

1198864

1198864

4th 1198864

1198862

1198862

1198862

1198862

1198864

1198862

1198862

1198861

1198862

1198862

Table 9 The worst performing criteria of each alternative

1198861

1198862

1198863

1198864

1198631

mdash mdash mdash 1198925

1198632

mdash 11989212

mdash mdash1198633

mdash 11989212

mdash mdash1198634

mdash 1198922

mdash mdash1198635

mdash 1198921

mdash mdash1198636

mdash 11989212

g7 11989213

1198637

mdash mdash g7 11989213

1198638

mdash 11989212

mdash mdash1198639

mdash 11989212

mdash 11989213

11986310

g15 mdash mdash mdash11986311

mdash 1198922

mdash 11989213

11986312

mdash 11989212

mdash mdash

GPM department processes less decision-making power forgreen supplier selection it is obvious that environmentalfactors are not significantly considered in the evaluationprocedure with the exception that RampD department shoulddesign new products and the related materials must be listedfor MM department Then MM department can place anorder and ask the suppliers to follow the list SQMdepartmentis concerned with technological capability as well as infor-mation share Vertical integration is an important enterprisepolicy for the TFT-LCD industry and strategic purchase isa method for the implementation of vertical integration Asmentioned before the key components of TFT-LCD panelsthat are produced in-house have gradually increased year byyear so information share is also an important criterion forcultivating the core capability of enterprise and establishinga competitive advantage Furthermore all of the criteriaplay important roles in the evaluation procedure for greensupplier selection If the proposed hybrid MCGDM methodonly adopts the AHP method it will most likely lead to theoversubjective weighting of the criteria Therefore we applythe entropy method to consider the weights of the criteriasimultaneously

ELECTRE III is applied to evaluate the suppliers Duringthe evaluation procedure we compare the outranking degreeand the overall outranking degree and then decide whichcriterion should be improved immediately for each alterna-tive as shown in Table 9 If the overall outranking degreeof a criterion decreases to zero (comparing to outrankingdegree) this criterion should be improved immediately Forthe best supplier supplier 1 (119886

1) the 119863

10manager suggests

that alternative 1 should strengthen communication with Y-TECH by taking actions such as improving cooperation forthe development of new products and instant market infor-mation share (119892

15) In this case study one of the most impor-

tant aspects is how to provide assistance to the subsidiarycompany (119886

3) of Y-TECH Table 9 indicates that if supplier

3 improves quality control (1198927) for polarizer production its

performance will be improved significantly Generally it iscontended that managers should be concerned about criteriarelated to their job description Due to limited enterpriseresources the order of executing enterprise improvementis also an important issue Following the useful proposedmethod the discovery of the criteria that need to be improvedwill become easy

Different from many previous studies this study doesnot integrate the weights of the criteria at the first stageIn fact green supplier selection is a group decision-makingproblem and integrating the weights would also lead tothe loss of information Therefore we further consider theranking results of each department as shown in Table 10Although these four polarizermanufacturers are the suppliersof Y-TECH there are some differences among the rankingresults of each department especially for suppliers 2 and 4 Asmentioned before RampD department placed great emphasison the ldquogreenrdquo criteria and supplier 4rsquos good performance onthese criteria is the reason for the different ranking resultsConsequently in this study we proposed a novel method andviewpoint for green supplier selection and we suggested thatthe weights of the criteria should not be integrated at thefirst stage because further discussions and communicationare necessary to include the different opinions from eachdepartment Additionally we compare the results of theproposed method with the ones implemented by Y-TECHthat is simple additive method in Table 10 Both results showthat supplier 1 has the best performance and is superior tosupplier 3 Supplier 4 is in the third place while supplier2 is the worst Furthermore within the extensive literatureon green supplier selection methods the TOPSIS methodwas widely applied to evaluate green suppliersrsquo performance[15 17ndash22] Thus we further compare the TOPSIS methodwith the proposedmethod and the comparison is establishedon the basis of AHP-entropy criteria weights In the TOPSISmethod with AHP-entropy weights supplier 3 is in the firstplace and superior to supplier 1 and supplier 4 and supplier2 have the worst performance in this evaluation Althoughthe ranking results among the proposed method and othertwo methods are similar the emphasis of this study is onhow to improve the suppliersrsquo performance in the TFT-LCD industry More importantly the results of the proposed

Mathematical Problems in Engineering 11

Table 10 Comparison of ranking results for the proposed method with other methods

Ranking MM RD GPM SQM The proposed method Current methodlowast AHP-entropy TOPSIS1st 119886

31198861

1198861

1198861

1198861

1198861

1198863

2nd 1198861

1198864

1198862

1198863

1198863

1198863

1198861

3rd 1198864

1198863

1198863

1198864

1198864

1198864

1198864

4th 1198862

1198862

1198864

1198862

1198862

1198862

1198862

lowast

The current evaluation method in Y-TECH is simple additive method

method show that each department has its different opinionsand viewpoints of improvement Each department of theTFT-LCDmanufacturers can assist their suppliers within thecontext of business perspectives

5 Conclusion

In this study we propose a hybrid MCGDM method forgreen supplier selection for Y-TECH Our method focuseson not only the selection or ranking but also the providenceof assistance to suppliers to strengthen their competitivenessThis method also emphasizes continuous improvement Dueto the challenges and difficulties the TFT-LCD industriesworldwide face we intend to establish a systematic selectionprocedure for TFT-LCD manufacturers We developed adecision framework for green supplier selection based onliteratures and the supplier audit forms from Y-TECH Weconsidered AHP and the entropy method to measure thecompromised weights of the criteria that involve subjectiveand objective opinions simultaneously After that the ELEC-TRE III method provides polarizer manufacturersrsquo rankingresults for executive managers in addition to improvementreports The acquisition of key components used for in-house production of TFT-LCD panels has gradually becomea strategy for cost reduction and the enhancement of corecapability Thus TFT-LCD manufacturers should strengthentheir cooperation with their suppliers

Based on the results of the ELECTRE III method wecan compare the differences between the outranking degreeand the overall outranking degree Then we further considerwhich criteria should be improved immediately especially forthe suppliers of strategic purchases The results of this studyprovide an important reference for subsidiary companiesthat are seeking to be primary polarizer suppliers Finallydiffering from many of the previous studies we do notintegrate the weights of the criteria with regard to a singleDM in this study In other worlds we integrate the rankingresults of eachDM instead of theweights of the criteria In theproposed evaluation procedure LAM places more emphasison the discussion and communication among a group ofDMs which is consistent with the practical case

In summary the result of this study can be regardedas a problem-solving process for green supplier selectionRegardless of the proposed method or analytic procedurewe aim at helping TFT-LCD manufacturers to establish asystematic and useful evaluation procedure via the currentstudy Because all of the supplier audit forms are in Microsoft

Excel 2010 spreadsheets the proposedmethod is easy to applyusing the same file Executive managers can consider theperformance of all suppliers and refer to the results of theproposed method Three main contributions are made inthis study First depending on the literatures and supplieraudit forms we develop a decision framework for the TFT-LCD industry Second we propose the useful systematicand flexible hybrid MCGDMmethod (based on the businessprocess) and improve the current evaluation procedureThird we place more emphasis on improving suppliers toenhance the competitiveness of entire supply chain especiallythe subsidiary company In practice green supplier selectionis evaluated by each department separately and all criteria areregarded as independent In recent years increasing attentionhas been given to the interdependent relationships amongthe criteria [15 16 26 49] Therefore future studies arenecessary to clarify the interrelations among the criteriaWe hope that more detailed information influencing therelationships between the criteria can be provided in futureresearch Consequently it would bemore efficient to improvethe supplier selection process for the entire supply chain inthe future work

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper

Acknowledgments

This research was partially supported by the National ScienceCouncil of the Republic of China (Grant NSC 102-2221-E-007-084) The authors would like to thank the editorand anonymous referees for their valuable comments andsuggestions

References

[1] Taiwan Stock Exchange ldquoIndustry analysis the flat paneldisplay Industryrdquo February 2013 httpwwwtwsecomtwchproductspublicationessayphp

[2] S Hung ldquoCompetitive strategies for Taiwanrsquos thin film tran-sistor-liquid crystal display (TFT-LCD) industryrdquoTechnology inSociety vol 28 no 3 pp 349ndash361 2006

[3] W Ho X Xu and P K Dey ldquoMulti-criteria decision makingapproaches for supplier evaluation and selection a literaturereviewrdquo European Journal of Operational Research vol 202 no1 pp 16ndash24 2010

12 Mathematical Problems in Engineering

[4] J Chai J N K Liu and E W T Ngai ldquoApplication of decision-making techniques in supplier selection a systematic review ofliteraturerdquo Expert Systems with Applications vol 40 no 10 pp3872ndash3885 2013

[5] K Govindan S Rajendran J Sarkis and P Murugesan ldquoMulticriteria decision making approaches for green supplier eval-uation and selection a literature reviewrdquo Journal of CleanerProduction 2013

[6] M Herva and E Roca ldquoReview of combined approaches andmulti-criteria analysis for corporate environmental evaluationrdquoJournal of Cleaner Production vol 39 pp 355ndash371 2013

[7] S Seuring ldquoA review of modeling approaches for sustainablesupply chain managementrdquo Decision Support Systems vol 54no 4 pp 1513ndash1520 2013

[8] M Igarashi L De Boer and A M Fet ldquoWhat is required forgreener supplier selection A literature review and conceptualmodel developmentrdquo Journal of Purchasing and SupplyManage-ment vol 19 no 4 pp 247ndash263 2013

[9] A Genovese S C Koh G Bruno and E Esposito ldquoGreenersupplier selection state of the art and some empirical evidencerdquoInternational Journal of Production Research vol 51 no 10 pp2868ndash2886 2013

[10] A H I Lee H Y Kang C F Hsu and H C Hung ldquoA greensupplier selectionmodel for high-tech industryrdquo Expert Systemswith Applications vol 36 no 4 pp 7917ndash7927 2009

[11] G Tuzkaya A Ozgen D Ozgen and U R Tuzkaya ldquoEnvi-ronmental performance evaluation of suppliers a hybrid fuzzymulti-criteria decision approachrdquo International Journal of Envi-ronmental Science and Technology vol 6 no 3 pp 477ndash4902009

[12] R J Kuo Y C Wang and F C Tien ldquoIntegration of artificialneural network and MADA methods for green supplier selec-tionrdquo Journal of Cleaner Production vol 18 no 12 pp 1161ndash11702010

[13] Q Zhu Y Dou and J Sarkis ldquoA portfolio-based analysis forgreen supplier management using the analytical network pro-cessrdquo Supply Chain Management vol 15 no 4 pp 306ndash3192010

[14] G Buyukozkan ldquoAn integrated fuzzy multi-criteria groupdecision-making approach for green supplier evaluationrdquo Inter-national Journal of Production Research vol 50 no 11 pp 2892ndash2909 2012

[15] G Buyukozkan and G Cifci ldquoA novel hybrid MCDM approachbased on fuzzy DEMATEL fuzzy ANP and fuzzy TOPSIS toevaluate green suppliersrdquo Expert Systems with Applications vol39 no 3 pp 3000ndash3011 2012

[16] C H Hsu F KWang and G H Tzeng ldquoThe best vendor selec-tion for conducting the recycled material based on a hybridMCDM model combining DANP with VIKORrdquo ResourcesConservation and Recycling vol 66 pp 95ndash111 2012

[17] K Govindan R Khodaverdi and A Jafarian ldquoA fuzzy multicriteria approach for measuring sustainability performance ofa supplier based on triple bottom line approachrdquo Journal ofCleaner Production vol 47 pp 345ndash354 2013

[18] L Shen L Olfat K Govindan R Khodaverdi and A DiabatldquoA fuzzy multi criteria approach for evaluating green supplierrsquosperformance in green supply chain with linguistic preferencesrdquoResources Conservation and Recycling vol 74 pp 170ndash179 2013

[19] M Yazdani ldquoAn integrated MCDM approach to green supplierselectionrdquo International Journal of Industrial Engineering Com-putations vol 5 no 3 pp 443ndash458 2014

[20] T Chen and J Freeman ldquoUsing AHP-Entropy weight and TOP-SIS methodology in green supplier selectionrdquo in Proceedings ofthe European Operations Management Association Conference(EurOMA rsquo14) Palermo Italy June 2014

[21] D Kannan A B L D S Jabbour andC J C Jabbour ldquoSelectinggreen suppliers based on GSCM practices using fuzzy TOPSISapplied to a Brazilian electronics companyrdquo European Journal ofOperational Research vol 233 no 2 pp 432ndash447 2014

[22] H Zhao and S Guo ldquoSelecting green supplier of thermalpower equipment by using a Hybrid MCDM Method forsustainabilityrdquo Sustainability vol 6 no 1 pp 217ndash235 2014

[23] J Figueira S Greco and M EhrgottMultiple Criteria DecisionAnalysis State of the Art Surveys Springer Science BostonMass USA 2005

[24] B Roy ldquoThe outranking approach and the foundations ofELECTREmethodsrdquoTheory and Decision vol 31 no 1 pp 49ndash73 1991

[25] J Hokkanen and P Salminen ldquoChoosing a solid waste man-agement systemusingmulticriteria decision analysisrdquoEuropeanJournal of Operational Research vol 98 no 1 pp 19ndash36 1997

[26] R J Lin ldquoUsing fuzzy DEMATEL to evaluate the green supplychain management practicesrdquo Journal of Cleaner Productionvol 40 pp 32ndash39 2013

[27] M L Tseng and A S F Chiu ldquoEvaluating firmrsquos green supplychain management in linguistic preferencesrdquo Journal of CleanerProduction vol 40 pp 22ndash31 2013

[28] G Buyukozkan and G Cifci ldquoA novel fuzzy mulati -criteriadecision framework for sustainable supplier selection withincomplete informationrdquo Computers in Industry vol 62 no 2pp 164ndash174 2011

[29] M T Escobar and J M Moreno-Jimenez ldquoAggregation of indi-vidual preference structures in AHP-group decision makingrdquoGroup Decision andNegotiation vol 16 no 4 pp 287ndash301 2007

[30] V Tsyganok ldquoInvestigation of the aggregation effectiveness ofexpert estimates obtained by the pairwise comparisonmethodrdquoMathematical and ComputerModelling vol 52 no 3-4 pp 538ndash544 2010

[31] J J Bernardo and J M Blin ldquoA programming model ofconsumer choice among multi-attributed brandsrdquo Journal ofConsumer Research vol 4 no 2 pp 111ndash118 1977

[32] P K Humphreys Y K Wong and F T S Chan ldquoIntegratingenvironmental criteria into the supplier selection processrdquoJournal of Materials Processing Technology vol 138 no 1ndash3 pp349ndash356 2003

[33] C Bai and J Sarkis ldquoIntegrating sustainability into supplierselection with grey system and rough set methodologiesrdquoInternational Journal of Production Economics vol 124 no 1 pp252ndash264 2010

[34] X Fu Q Zhu and J Sarkis ldquoEvaluating green supplier devel-opment programs at a telecommunications systems providerrdquoInternational Journal of Production Economics vol 140 no 1pp 357ndash367 2012

[35] A Amindoust S Ahmed A Saghafinia and A BahreininejadldquoSustainable supplier selection a ranking model based on fuzzyinference systemrdquoApplied Soft Computing Journal vol 12 no 6pp 1668ndash1677 2012

[36] K Shaw R Shankar S S Yadav and L S Thakur ldquoSupplierselection using fuzzy AHP and fuzzy multi-objective linearprogramming for developing low carbon supply chainrdquo ExpertSystems with Applications vol 39 no 9 pp 8182ndash8192 2012

Mathematical Problems in Engineering 13

[37] A Awasthi S S Chauhan and S K Goyal ldquoA fuzzy multi-criteria approach for evaluating environmental performance ofsuppliersrdquo International Journal of Production Economics vol126 no 2 pp 370ndash378 2010

[38] T Y Chiou H K Chan F Lettice and S H Chung ldquoThe influ-ence of greening the suppliers and green innovation on envi-ronmental performance and competitive advantage in TaiwanrdquoTransportation Research Part E Logistics and TransportationReview vol 47 no 6 pp 822ndash836 2011

[39] T L Saaty ldquoA scaling method for priorities in hierarchicalstructuresrdquo Journal of Mathematical Psychology vol 15 no 3pp 234ndash281 1977

[40] T L Saaty The Analytic Hierarchy Process McGraw-Hill NewYork NY USA 1980

[41] CMacharis J Springael K De Brucker andA Verbeke ldquoPRO-METHEE and AHP the design of operational synergies inmulticriteria analysis Strengthening PROMETHEE with ideasof AHPrdquo European Journal of Operational Research vol 153 no2 pp 307ndash317 2004

[42] M Zeleny Multiple Criteria Decision Making McGraw-HillNew York NY USA 1982

[43] C L Hwang and K Yoon Multiple Attribute Decision MakingMethods and Applications A State-of-the-Art Survey SpringerNew York NY USA 1981

[44] B Roy ldquoELECTRE III un algorithme de classements fondesur une repre sentation floue des preferences en preferences enpresence de criteres multiplesrdquo Cahiers du Centre drsquoEtudes deRecherche Operationnelle vol 20 no 1 pp 3ndash24 1978

[45] P Vincke Multiple Criteria Decision-Aid John Wiley amp SonsChichester UK 1992

[46] M Rogers and M Bruen ldquoUsing ELECTRE III to chooseroute for Dublin Port motorwayrdquo Journal of TransportationEngineering vol 126 no 4 pp 313ndash323 2000

[47] M Rogers and M Bruen ELECTRE and Decision SupportMethods and Applications in Engineering and InfrastructureInvestment Kluwer Academic Publishers Boston Mass USA2000

[48] V Belton and T J Stewart Multiple Criteria Decision AnalysisAn Integrated Approach Kluwer Academic Publishers BostonMass USA 2002

[49] B Chang C W Chang and C H Wu ldquoFuzzy DEMATELmethod for developing supplier selection criteriardquo Expert Sys-tems with Applications vol 38 no 3 pp 1850ndash1858 2011

Submit your manuscripts athttpwwwhindawicom

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

MathematicsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Mathematical Problems in Engineering

Hindawi Publishing Corporationhttpwwwhindawicom

Differential EquationsInternational Journal of

Volume 2014

Applied MathematicsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Probability and StatisticsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Mathematical PhysicsAdvances in

Complex AnalysisJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

OptimizationJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

CombinatoricsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Operations ResearchAdvances in

Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Function Spaces

Abstract and Applied AnalysisHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of Mathematics and Mathematical Sciences

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Algebra

Discrete Dynamics in Nature and Society

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Decision SciencesAdvances in

Discrete MathematicsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom

Volume 2014 Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Stochastic AnalysisInternational Journal of

Mathematical Problems in Engineering 5

where

119908 =

119899

sum

119895=1

119908119905119895

119888119895(119886119894 119886119896) =

1 if 119909119894119895+ 119902119895ge 119909119896119895

0 if 119909119894119895+ 119901119895le 119909119896119895

119901119895+ 119909119894119895minus 119909119896119895

119901119895minus 119902119895

otherwise

(8)

We then consider rejecting degree which means alternative 119894is dominated by alternative 119896 under criterion 119895 as follows

119889119895(119886119894 119886119896)

=

1 if 119892119895(119886119896) ge 119892119895(119886119894) + V119895

0 if 119892119895(119886119896) le 119892119895(119886119894) + 119901119895

119892119895(119886119896) minus 119892119895(119886119894) minus 119901119895

V119895minus 119901119895

otherwise

(9)

Therefore the overall outranking degree of DM 119905 can beconsidered with outranking degree and rejecting degreewhich is called overall outranking degree as indicated in thefollowing equation

119878119905(119886119894 119886119896)

=

119862119905(119886119894 119886119896) if 119889

119895(119886119894 119886119896)

le 119862119905(119886119894 119886119896) forall119895

119862119905(119886119894 119886119896)

times prod

119895isin119869(119886119894 119886119896)

1 minus 119889119895(119886119894 119886119896)

1 minus 119862119905(119886119894 119886119896) otherwise

(10)

where 119869(119886119894 119886119896) is the set of criteria such that 119889

119895(119886119894 119886119896) gt

119862119905(119886119894 119886119896)

The final step is to exploit the model and produce aranking result from the overall outranking degrees The gen-eral approach for exploitation is to construct two preorders1198851and 119885

2using the descending and ascending distillation

process and then combine these two to produce a partialpreorder 119885 = 119885

1cap 1198852 The descending process is to clas-

sify the alternatives from the best to the worst while theascending process is from the worst to the best [46ndash48]

34 The LAM for Integrating Ranking Results of DMs Ber-nardo and Blin [31] developed LAM to transform individualranking result into an overall ranking result Therefore LAMcan integrate the ranking result of each DM into an overallranking result We can apply this simple method to sum theranking score from each DM and rank the overall score fromthe lowest score to the highest score [43] If an alternativeis ranked as the first order the alternative gets one pointIf an alternative is ranked as the second it gets two pointsand so on On the other hand LAM also considers the

weights of the DMs to integrate the overall ranking resultIn our case study we consider the weights of DMs basedon their positions For example the RampD department hasapproximately 27 of the decision-making power for greensupplier selection so the weight of the RampD departmentis 027 in the procedure of green supplier selection LAMcan be illustrated as follows First an overall ranking matrixis established as Π = [120587

119894119904] where 120587

119894119904represents the

weighted frequency that 119886119894is ranked the 119904th with the different

weights of the DMs Then depending on the above overallranking matrix we formulate a linear programming modelto optimize the permutationThe linear programmingmodeluses the binary decision variables 120593

119894119904 The decision variable

120593119894119904

= 1 means 119886119894is assigned to the 119904th overall rank and

120593119894119904= 0 otherwise Obviously one alternative can be assigned

to only one rank and one rank can only be assigned by onealternative Therefore the linear programming model can bewritten as follows

max119898

sum

119894=1

119898

sum

119891=1

120587119894119904120593119894119904

subject to119898

sum

119894=1

120593119894119904= 1 119904 = 1 2 119898

119898

sum

119891=1

120593119894119904= 1 119894 = 1 2 119898

120593119894119904isin 0 1 119894 119904 = 1 2 119898

(11)

4 Application Case

Y-TECH is the first manufacturer in Taiwan to mass-produceTFT-LCD panels and it is also one of the top five TFT-LCD panel manufacturers in the world By utilizing thedata coming from Y-TECH we aimed at providing a usefulevaluation method with continuous improvement Becauseof the global competitive market the well-known TFT-LCD manufacturers hope to develop and invest in theirsuppliers especially for some of the important componentsfor TFT-LCD panel such as the color filter back-lightunit and polarizer Because good suppliers may affect themanufacturerrsquos performance and competitiveness and theentire supply chain in this study we demonstrate the supplierevaluation procedure and provide information about supplierassistance for TFT-LCD manufacturers

41 Study Background In the polarizer industry the polar-izer manufacturer of the highest market share is in SouthKorea while the Japanese polarizer manufacturer has thesecond highestmarket share In recent years Y-TECHmainlydepended on materials and technical support from Japanesepolarizer manufacturers Also the Korean polarizer manu-facturers have aggressively intervened in the supply chainof Taiwanese TFT-LCD industry Y-TECH has started toreconsider its supplier evaluation procedure and involvedthe group of GPM department in auditing and evaluatingits suppliers Figure 1 shows the new decision procedure for

6 Mathematical Problems in Engineering

Quarterly business reviewQuarterly quality review

Annual and audit

Qualified greensuppliers lists

MM material managementSQM supplier quality managementESH environment safety healthHR human resourceRD research and designGPM green product management

SQMESHHR

SQM (36)MM (27)RD (27)GPM (10)

SQMESHHR

1st tier2nd tierfreeze

New green suppliers

Survey and audit

MMSQM

Figure 1 The decision procedure of green supplier selection in the case study

green supplier selection at Y-TECH Before being a formalpolarizer supplier in Y-TECH the candidate companiesshould pass a two-stage assessment At the first stage anew polarizer supplier should accept a preliminary surveyby the teams of MM and SQM departments and then theteams of SQM ESH and HR departments further executethe preliminary audit for the polarizer suppliers A qualifiedpolarizer manufacturera should pass at least 70 of the itemson all supplier audit forms At the second stage the formalevaluation is conducted by the teams of SQM MM RD andGPM departments After the second-stage assessment thequalified polarizer suppliers will be ranked in the first tierif they pass more than 80 of the items on supplier auditforms and the suppliers passing 70ndash80 of the items willbe ranked in the second tier If the qualified polarizer supplierpasses fewer than 70 of the items it will be regarded asdisqualified All of the formal suppliers are reviewed regularlyby the teams of SQM ESH and HR departments Further-more IS9001 ISO14001 and OHSAS18001 certifications arenot necessary in the evaluation procedure because Y-TECHwill assist their formal suppliers to apply for environmentalcertifications within a limited timeThe first-stage assessmentis the preliminary survey and audit so we focus on applyingthe proposed MCGDM method to the formal evaluation forthe assessment at the second stage

In this case study although four qualified polarizer sup-pliers for Y-TECH outperformed in all other suppliers for thefirst-stage survey our focus will be on supplier 3 because it isa subsidiary company for Y-TECH In fact strategic purchaseis an important strategy to enhance competitiveness in theTFT-LCD industry Therefore the decision procedure is notonly to select the subsidiary into the supply chain but alsoto provide improvement reports for the subsidiary companyAdditionally it is expected that the subsidiary supplier willbe the primary one In addition to the three aspects andfifty criteria (as mentioned in Section 2) we also surveyed12 managers on the expert committee In practice the SQMdepartment holds 36 of the decision-making power theMM and RampD departments 27 and the GPM department10 to select suitable suppliers during the decision-makingprocess

42 Empirical Results First we designed a questionnaire for12 experts to measure the comparative weights between theaspects and criteria Table 2 presents the subjective weights ofeach criterion for DMs based on AHPThemanagers of MMRD GPM and SQM departments are 119863

1 1198632 1198633 1198634 1198635

1198636 1198637 1198638 1198639 and 119863

10 11986311 11986312 respectively Then we

focused on the criteria weights that were 15 more thanthe average weight (01) Notice that many managers think

Mathematical Problems in Engineering 7

Table 2 Subjective weights of each criterion for DMs

Dept MM RD GPM SQMCriteria 119863

11198632

1198633

1198634

1198635

1198636

1198637

1198638

1198639

11986310

11986311

11986312

1198921

0014 0192 0009 0177 0349 0007 0106 0111 0120 0006 0117 00221198922

0014 0021 0033 0421 0050 0007 0032 0111 0120 0042 0290 01191198923

0036 0043 0012 0083 0174 0037 0048 0056 0033 0016 0036 00441198924

0007 0006 0002 0028 0174 0007 0010 0056 0007 0002 0012 00061198925

0405 0006 0048 0016 0009 0131 0093 0093 0004 0019 0011 00171198926

0081 0001 0048 0005 0003 0026 0031 0019 0001 0004 0002 00171198927

0039 0043 0048 0071 0057 0131 0197 0056 0025 0161 0029 00071198928

0039 0009 0048 0010 0019 0026 0049 0056 0008 0032 0010 00021198929

0064 0009 0045 0006 0015 0071 0073 0079 0010 0007 0016 000211989210

0016 0009 0045 0003 0013 0071 0031 0020 0021 0026 0005 000211989211

0007 0002 0006 0001 0004 0014 0019 0013 0003 0044 0018 000111989212

0174 0480 0421 0026 0083 0196 0078 0243 0270 0013 0085 047611989213

0058 0096 0070 0130 0028 0196 0155 0049 0270 0093 0256 015911989214

0035 0069 0143 0019 0006 0020 0039 0021 0081 0089 0028 010611989215

0012 0014 0020 0004 0017 0059 0039 0021 0027 0445 0085 0021

highly of the current capability (11989212) and RampD capability

(11989213) The managers of MM department generally place

importance on the current capability (11989212) and the weights

of 1198631 1198632 and 119863

3are 0174 0480 and 0421 respectively

Moreover MM department is also concerned with the price(1198925) and the average weight is 0153 It is generally agreed

that the managers of RampD department put emphasis onenvironmental factors because of their relevance to productdesign Taking strategic fit (119892

2) for example suppliers should

review environment-related substance list regularly whendeveloping a green productThe average weights of safety andhealth (119892

1) strategic fit (119892

2) environmental control (119892

3) and

recovery (1198924) are 0263 0236 0129 and 0101 respectively

and the importance of environmental factors is over 70 ofthe total weight

In particular GPM department does not give moreweight to environmental factors and the average weight ofenvironmental factors is only 0054 The most likely reasonis that the GPM department holds less decision-makingpower GPM department is concerned with quality control(1198927) current capability (119892

12) and RampD capability (119892

13) and

the average weights are 0102 0196 and 0168 respectivelyOverall themanagers ofGPMdepartment are still concernedwith the current capability (119892

12) and theRampDcapability (119892

13)

Finally SQM department focuses on the strategic fit (1198922)

current capability (11989212) RampD capability (119892

13) and informa-

tion share (11989215) and the average weights of these factors

are 0150 0191 0169 and 0184 respectively Based on ourexpectation SQM department is not significantly concernedwith quality control (119892

7) or other criteria of the enterprise

operation and the average weight for quality control (1198927)

is only 0066 However SQM department emphasizes theimportance of information share (119892

15) Information share

brings the benefit of enhancing the competitiveness of theentire supply chain especially technology development Insum SQM department focuses on the criteria of strategic

Table 3 Objective weights of each criterion for individual DM

Criteria Objective weights1198921

00721198922

00761198923

00671198924

00661198925

00641198926

00751198927

00811198928

00781198929

008011989210

006411989211

004711989212

005011989213

007011989214

005611989215

0054

technology and developmentThus to avoid overly subjectiveweights we include the entropy method in the proposedevaluation procedure using (2) through (5) The objectiveweights of the criteria are shown in Table 3 while Table 4indicates the compromised weights of the criteria for eachDM by (6)

After the compromised criteria weights of the DMsare decided we further apply the criteria weights and theperformance to the ELECTRE III method (7) through (10)to evaluate the four polarizer suppliers For each DM theoutranking degree of any two alternatives can be calculatedby (7) and the results are shown in Table 5 For examplefor manager 119863

1 the outranking degree of alternative 1 to

alternative 3 is 0743 and the outranking degree of alternative3 to alternative 1 is 0493 Obviously manager 119863

1believes

8 Mathematical Problems in Engineering

Table 4 Compromised weights of each criterion for DMs

Dept MM RD GPM SQMCriteria 119863

11198632

1198633

1198634

1198635

1198636

1198637

1198638

1198639

11986310

11986311

11986312

1198921

0016 0230 0011 0176 0367 0008 0109 0121 0135 0007 0123 00271198922

0016 0027 0042 0441 0055 0008 0035 0127 0142 0051 0322 01521198923

0037 0048 0013 0077 0170 0038 0046 0057 0034 0017 0035 00501198924

0007 0007 0002 0025 0168 0007 0009 0056 0007 0002 0012 00071198925

0399 0006 0051 0014 0008 0128 0085 0090 0004 0020 0010 00181198926

0093 0001 0060 0005 0003 0030 0033 0022 0001 0005 0002 00211198927

0049 0058 0065 0079 0067 0162 0229 0068 0032 0209 0034 00101198928

0047 0012 0062 0011 0022 0031 0055 0066 0010 0040 0011 00031198929

0079 0012 0060 0007 0018 0087 0084 0095 0012 0009 0019 000311989210

0016 0010 0048 0003 0012 0069 0028 0019 0021 0027 0005 000211989211

0005 0002 0005 0001 0003 0010 0013 0009 0002 0033 0012 000111989212

0134 0400 0350 0018 0061 0149 0056 0183 0211 0010 0062 040111989213

0062 0112 0081 0126 0029 0209 0156 0052 0295 0104 0262 018711989214

0030 0064 0133 0015 0005 0017 0031 0018 0071 008 0023 010011989215

0010 0013 0018 0003 0013 0049 0030 0017 0023 0385 0067 0019

Table 5 Outranking degrees of each DM

1198631

1198632

1198633

1198861

1198862

1198863

1198864

1198861

1198862

1198863

1198864

1198861

1198862

1198863

1198864

1198861

0 0588 0743 0867 1198861

0 0771 0578 0868 1198861

0 0688 0547 08441198862

0521 0 0598 0633 1198862

0310 0 0374 0425 1198862

0409 0 0442 05291198863

0493 0538 0 0762 1198863

0715 0779 0 0803 1198863

0669 0719 0 07631198864

0591 0619 0479 0 1198864

0783 0787 0476 0 1198864

0771 0735 0488 01198634

1198635

1198636

a1 a2 a3 a4 a1 a2 a3 a4 a1 a2 a3 a41198861

0 0785 0839 0862 1198861

0 0790 0804 0858 1198861

0 0635 0685 07701198862

0306 0 0413 0440 1198862

0378 0 0444 0463 1198862

0477 0 0625 06441198863

0673 0750 0 0780 1198863

0650 0678 0 0703 1198863

0531 0632 0 07111198864

0740 0782 0694 0 1198864

0745 0832 0643 0 1198864

0665 0674 0565 01198637

1198638

1198639

1198861

1198862

1198863

1198864

1198861

1198862

1198863

1198864

1198861

1198862

1198863

1198864

1198861

0 0642 0762 0767 1198861

0 0736 0706 0853 1198861

0 0762 0664 08641198862

0473 0 0602 0634 1198862

0404 0 0492 0518 1198862

0320 0 0465 05201198863

0534 0604 0 0667 1198863

0608 0646 0 0712 1198863

0634 0788 0 08091198864

0687 0663 0628 0 1198864

0734 0777 0588 0 1198864

0702 0735 0532 011986310

11986311

11986312

1198861

1198862

1198863

1198864

1198861

1198862

1198863

1198864

1198861

1198862

1198863

1198864

1198861

0 0471 0557 0585 1198861

0 0762 0754 0841 1198861

0 0765 0562 08861198862

0607 0 0671 0661 1198862

0326 0 0491 0509 1198862

0313 0 0387 04551198863

0660 0704 0 0663 1198863

0630 0776 0 0791 1198863

0697 0798 0 08261198864

0780 0713 0706 0 1198864

0704 0761 0644 0 1198864

0749 0762 0453 0

that alternative 1 is superior to alternative 3 However acharacteristic of the ELECTRE III method is that a penaltyis set if the alternative performs the worst for a criterionThus we can use (9) to calculate the rejecting degree dueto the penalty and the overall outranking degree can beobtained by (10) as shown in Table 6 As another examplefor the 119863

6manager the outranking degree of alternative 3

to alternative 2 is 0632 but the overall outranking degreeof alternative 3 to alternative 2 is 0000 This means thatalternative 3 performs too poorly to be accepted based onsome criteria The results presented in Table 7 show thatalternative 3 performs too poorly on quality control (119892

7) and

delivery (1198929) The rejecting degrees for quality control (119892

7)

and delivery (1198929) are 1000 and 0778 respectively which

Mathematical Problems in Engineering 9

Table 6 Overall outranking degree of each DM

1198631

1198632

1198633

1198861

1198862

1198863

1198864

1198861

1198862

1198863

1198864

1198861

1198862

1198863

1198864

1198861

0 0588 0743 0867 1198861

0 0771 0564 0868 1198861

0 0688 0246 08441198862

0342 0 0331 0633 1198862

0026 0 0 0148 1198862

0115 0 0 04151198863

0273 0334 0 0762 1198863

0715 0779 0 0803 1198863

0669 0719 0 07631198864

0563 0 0384 0 1198864

0783 0787 0361 0 1198864

0771 0735 0207 01198634

1198635

1198636

1198861

1198862

1198863

1198864

1198861

1198862

1198863

1198864

1198861

1198862

1198863

1198864

1198861

0 0785 0839 0862 1198861

0 0790 0804 0858 1198861

0 0245 0483 07461198862

0 0 0 0 1198862

0 0 0 0037 1198862

0090 0 0 06441198863

0673 0750 0 0780 1198863

0413 0468 0 0526 1198863

0091 0 0 05461198864

0633 0782 0694 0 1198864

0745 0832 0643 0 1198864

0 0459 0541 01198637

1198638

1198639

1198861

1198862

1198863

1198864

1198861

1198862

1198863

1198864

1198861

1198862

1198863

1198864

1198861

0 0413 0762 0767 1198861

0 0736 0706 0853 1198861

0 0762 0664 08641198862

0054 0 0336 0634 1198862

0037 0 0 0163 1198862

0005 0 0 01661198863

0094 0 0 0445 1198863

0344 0406 0 0550 1198863

0372 0788 0 08091198864

0 0437 0628 0 1198864

0734 0774 0588 0 1198864

0 0735 0474 011986310

11986311

11986312

1198861

1198862

1198863

1198864

1198861

1198862

1198863

1198864

1198861

1198862

1198863

1198864

1198861

0 0072 0123 0 1198861

0 0762 0754 0841 1198861

0 0765 0509 08861198862

0607 0 0671 0661 1198862

0 0 0 0 1198862

0034 0 0 02611198863

0660 0529 0 0663 1198863

0365 0776 0 0791 1198863

0697 0798 0 08261198864

0780 0713 0706 0 1198864

0 0761 0644 0 1198864

0664 0762 0315 0

Table 7 Rejecting degree for alternative 3 to alternative 2 from 1198896manager

1198921

1198922

1198923

1198924

1198925

1198926

1198927

1198928

1198929

11989210

11989211

11989212

11989213

11989214

11989215

Reject degree 0000 0000 0000 0583 0407 0000 1000 0288 0778 0000 0000 0000 0000 0000 0000

are obviously larger than the outranking degree of 0632 Inthe next section we further discuss which criteria shouldbe improved immediately Accordingly we can establish thedescending and ascending distillation for each DM and theranking results are shown in Table 8

Finally LAM is applied to establish linear programmingfor integrating the opinions of each DM According tothe decision-making power of each department the overallranking matrix is shown as follows

Π =[[[

[

06850 01950 00000 01200

00400 01350 00850 07400

02350 04700 02825 00125

00400 02000 06325 01275

]]]

]

(12)

Therefore we can formulate a linear programming modelusing (11) and obtain the solution which indicates that theoverall ranking result is 119886

1≻ 1198863≻ 1198864≻ 1198862The best supplier is

supplier 1 and the subsidiary company of Y-TECH is secondonly to supplier 1 Supplier 4 is ranked third and supplier 2performs the worst in this evaluation

43 Discussion and Implications In this study we aim topropose a hybridMCGDMmethod to evaluate and assist sup-pliers At the first stage we use the AHP method to measurethe subjective weights of the criteria for each DM Based onthe results of the AHP method it is obvious that most ofthe managers value the importance of current capability andRampD capability and this suggests that Y-TECH places a greatemphasis on technology development In the FPD industrydue to the rapid development of new technology many well-known enterprises regularly release new products to marketevery year A challenge for Y-TECH includes the develop-ment of other technologies such as organic light-emittingdisplays (OLED) For example the OLED panel led to thereduction of the demand for small and medium TFT-LCDdisplays in the global market in 2010 Therefore to enhancecompetitiveness TFT-LCD manufactures connecting withthe suppliers in the entire supply chain should continuouslydevelop new technology Quality control is an importantcriterion secondary only to technological capability At theearly stages of applying a new technology to a new productthe yield rate of the new product is low Consequently it isimportant not only to develop the new technology but alsoto maintain a highly qualified product Currently because

10 Mathematical Problems in Engineering

Table 8 Initial evaluation of individual DM

Dept MM RD GPM SQMRanking 119889

11198892

1198893

1198894

1198895

1198896

1198897

1198898

1198899

11988910

11988911

11988912

1st 1198861

1198861 1198863

1198863

1198861

1198861

1198861

1198861

1198861

1198861

1198862 1198863 1198864

1198861

1198861 1198863

2nd 1198862 1198863

1198861

1198863

1198864

1198862

1198862

1198864

1198863

1198863 1198864

3rd 1198864

1198864

1198864

1198863

1198863

1198863 1198864

1198863

1198864

1198864

4th 1198864

1198862

1198862

1198862

1198862

1198864

1198862

1198862

1198861

1198862

1198862

Table 9 The worst performing criteria of each alternative

1198861

1198862

1198863

1198864

1198631

mdash mdash mdash 1198925

1198632

mdash 11989212

mdash mdash1198633

mdash 11989212

mdash mdash1198634

mdash 1198922

mdash mdash1198635

mdash 1198921

mdash mdash1198636

mdash 11989212

g7 11989213

1198637

mdash mdash g7 11989213

1198638

mdash 11989212

mdash mdash1198639

mdash 11989212

mdash 11989213

11986310

g15 mdash mdash mdash11986311

mdash 1198922

mdash 11989213

11986312

mdash 11989212

mdash mdash

GPM department processes less decision-making power forgreen supplier selection it is obvious that environmentalfactors are not significantly considered in the evaluationprocedure with the exception that RampD department shoulddesign new products and the related materials must be listedfor MM department Then MM department can place anorder and ask the suppliers to follow the list SQMdepartmentis concerned with technological capability as well as infor-mation share Vertical integration is an important enterprisepolicy for the TFT-LCD industry and strategic purchase isa method for the implementation of vertical integration Asmentioned before the key components of TFT-LCD panelsthat are produced in-house have gradually increased year byyear so information share is also an important criterion forcultivating the core capability of enterprise and establishinga competitive advantage Furthermore all of the criteriaplay important roles in the evaluation procedure for greensupplier selection If the proposed hybrid MCGDM methodonly adopts the AHP method it will most likely lead to theoversubjective weighting of the criteria Therefore we applythe entropy method to consider the weights of the criteriasimultaneously

ELECTRE III is applied to evaluate the suppliers Duringthe evaluation procedure we compare the outranking degreeand the overall outranking degree and then decide whichcriterion should be improved immediately for each alterna-tive as shown in Table 9 If the overall outranking degreeof a criterion decreases to zero (comparing to outrankingdegree) this criterion should be improved immediately Forthe best supplier supplier 1 (119886

1) the 119863

10manager suggests

that alternative 1 should strengthen communication with Y-TECH by taking actions such as improving cooperation forthe development of new products and instant market infor-mation share (119892

15) In this case study one of the most impor-

tant aspects is how to provide assistance to the subsidiarycompany (119886

3) of Y-TECH Table 9 indicates that if supplier

3 improves quality control (1198927) for polarizer production its

performance will be improved significantly Generally it iscontended that managers should be concerned about criteriarelated to their job description Due to limited enterpriseresources the order of executing enterprise improvementis also an important issue Following the useful proposedmethod the discovery of the criteria that need to be improvedwill become easy

Different from many previous studies this study doesnot integrate the weights of the criteria at the first stageIn fact green supplier selection is a group decision-makingproblem and integrating the weights would also lead tothe loss of information Therefore we further consider theranking results of each department as shown in Table 10Although these four polarizermanufacturers are the suppliersof Y-TECH there are some differences among the rankingresults of each department especially for suppliers 2 and 4 Asmentioned before RampD department placed great emphasison the ldquogreenrdquo criteria and supplier 4rsquos good performance onthese criteria is the reason for the different ranking resultsConsequently in this study we proposed a novel method andviewpoint for green supplier selection and we suggested thatthe weights of the criteria should not be integrated at thefirst stage because further discussions and communicationare necessary to include the different opinions from eachdepartment Additionally we compare the results of theproposed method with the ones implemented by Y-TECHthat is simple additive method in Table 10 Both results showthat supplier 1 has the best performance and is superior tosupplier 3 Supplier 4 is in the third place while supplier2 is the worst Furthermore within the extensive literatureon green supplier selection methods the TOPSIS methodwas widely applied to evaluate green suppliersrsquo performance[15 17ndash22] Thus we further compare the TOPSIS methodwith the proposedmethod and the comparison is establishedon the basis of AHP-entropy criteria weights In the TOPSISmethod with AHP-entropy weights supplier 3 is in the firstplace and superior to supplier 1 and supplier 4 and supplier2 have the worst performance in this evaluation Althoughthe ranking results among the proposed method and othertwo methods are similar the emphasis of this study is onhow to improve the suppliersrsquo performance in the TFT-LCD industry More importantly the results of the proposed

Mathematical Problems in Engineering 11

Table 10 Comparison of ranking results for the proposed method with other methods

Ranking MM RD GPM SQM The proposed method Current methodlowast AHP-entropy TOPSIS1st 119886

31198861

1198861

1198861

1198861

1198861

1198863

2nd 1198861

1198864

1198862

1198863

1198863

1198863

1198861

3rd 1198864

1198863

1198863

1198864

1198864

1198864

1198864

4th 1198862

1198862

1198864

1198862

1198862

1198862

1198862

lowast

The current evaluation method in Y-TECH is simple additive method

method show that each department has its different opinionsand viewpoints of improvement Each department of theTFT-LCDmanufacturers can assist their suppliers within thecontext of business perspectives

5 Conclusion

In this study we propose a hybrid MCGDM method forgreen supplier selection for Y-TECH Our method focuseson not only the selection or ranking but also the providenceof assistance to suppliers to strengthen their competitivenessThis method also emphasizes continuous improvement Dueto the challenges and difficulties the TFT-LCD industriesworldwide face we intend to establish a systematic selectionprocedure for TFT-LCD manufacturers We developed adecision framework for green supplier selection based onliteratures and the supplier audit forms from Y-TECH Weconsidered AHP and the entropy method to measure thecompromised weights of the criteria that involve subjectiveand objective opinions simultaneously After that the ELEC-TRE III method provides polarizer manufacturersrsquo rankingresults for executive managers in addition to improvementreports The acquisition of key components used for in-house production of TFT-LCD panels has gradually becomea strategy for cost reduction and the enhancement of corecapability Thus TFT-LCD manufacturers should strengthentheir cooperation with their suppliers

Based on the results of the ELECTRE III method wecan compare the differences between the outranking degreeand the overall outranking degree Then we further considerwhich criteria should be improved immediately especially forthe suppliers of strategic purchases The results of this studyprovide an important reference for subsidiary companiesthat are seeking to be primary polarizer suppliers Finallydiffering from many of the previous studies we do notintegrate the weights of the criteria with regard to a singleDM in this study In other worlds we integrate the rankingresults of eachDM instead of theweights of the criteria In theproposed evaluation procedure LAM places more emphasison the discussion and communication among a group ofDMs which is consistent with the practical case

In summary the result of this study can be regardedas a problem-solving process for green supplier selectionRegardless of the proposed method or analytic procedurewe aim at helping TFT-LCD manufacturers to establish asystematic and useful evaluation procedure via the currentstudy Because all of the supplier audit forms are in Microsoft

Excel 2010 spreadsheets the proposedmethod is easy to applyusing the same file Executive managers can consider theperformance of all suppliers and refer to the results of theproposed method Three main contributions are made inthis study First depending on the literatures and supplieraudit forms we develop a decision framework for the TFT-LCD industry Second we propose the useful systematicand flexible hybrid MCGDMmethod (based on the businessprocess) and improve the current evaluation procedureThird we place more emphasis on improving suppliers toenhance the competitiveness of entire supply chain especiallythe subsidiary company In practice green supplier selectionis evaluated by each department separately and all criteria areregarded as independent In recent years increasing attentionhas been given to the interdependent relationships amongthe criteria [15 16 26 49] Therefore future studies arenecessary to clarify the interrelations among the criteriaWe hope that more detailed information influencing therelationships between the criteria can be provided in futureresearch Consequently it would bemore efficient to improvethe supplier selection process for the entire supply chain inthe future work

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper

Acknowledgments

This research was partially supported by the National ScienceCouncil of the Republic of China (Grant NSC 102-2221-E-007-084) The authors would like to thank the editorand anonymous referees for their valuable comments andsuggestions

References

[1] Taiwan Stock Exchange ldquoIndustry analysis the flat paneldisplay Industryrdquo February 2013 httpwwwtwsecomtwchproductspublicationessayphp

[2] S Hung ldquoCompetitive strategies for Taiwanrsquos thin film tran-sistor-liquid crystal display (TFT-LCD) industryrdquoTechnology inSociety vol 28 no 3 pp 349ndash361 2006

[3] W Ho X Xu and P K Dey ldquoMulti-criteria decision makingapproaches for supplier evaluation and selection a literaturereviewrdquo European Journal of Operational Research vol 202 no1 pp 16ndash24 2010

12 Mathematical Problems in Engineering

[4] J Chai J N K Liu and E W T Ngai ldquoApplication of decision-making techniques in supplier selection a systematic review ofliteraturerdquo Expert Systems with Applications vol 40 no 10 pp3872ndash3885 2013

[5] K Govindan S Rajendran J Sarkis and P Murugesan ldquoMulticriteria decision making approaches for green supplier eval-uation and selection a literature reviewrdquo Journal of CleanerProduction 2013

[6] M Herva and E Roca ldquoReview of combined approaches andmulti-criteria analysis for corporate environmental evaluationrdquoJournal of Cleaner Production vol 39 pp 355ndash371 2013

[7] S Seuring ldquoA review of modeling approaches for sustainablesupply chain managementrdquo Decision Support Systems vol 54no 4 pp 1513ndash1520 2013

[8] M Igarashi L De Boer and A M Fet ldquoWhat is required forgreener supplier selection A literature review and conceptualmodel developmentrdquo Journal of Purchasing and SupplyManage-ment vol 19 no 4 pp 247ndash263 2013

[9] A Genovese S C Koh G Bruno and E Esposito ldquoGreenersupplier selection state of the art and some empirical evidencerdquoInternational Journal of Production Research vol 51 no 10 pp2868ndash2886 2013

[10] A H I Lee H Y Kang C F Hsu and H C Hung ldquoA greensupplier selectionmodel for high-tech industryrdquo Expert Systemswith Applications vol 36 no 4 pp 7917ndash7927 2009

[11] G Tuzkaya A Ozgen D Ozgen and U R Tuzkaya ldquoEnvi-ronmental performance evaluation of suppliers a hybrid fuzzymulti-criteria decision approachrdquo International Journal of Envi-ronmental Science and Technology vol 6 no 3 pp 477ndash4902009

[12] R J Kuo Y C Wang and F C Tien ldquoIntegration of artificialneural network and MADA methods for green supplier selec-tionrdquo Journal of Cleaner Production vol 18 no 12 pp 1161ndash11702010

[13] Q Zhu Y Dou and J Sarkis ldquoA portfolio-based analysis forgreen supplier management using the analytical network pro-cessrdquo Supply Chain Management vol 15 no 4 pp 306ndash3192010

[14] G Buyukozkan ldquoAn integrated fuzzy multi-criteria groupdecision-making approach for green supplier evaluationrdquo Inter-national Journal of Production Research vol 50 no 11 pp 2892ndash2909 2012

[15] G Buyukozkan and G Cifci ldquoA novel hybrid MCDM approachbased on fuzzy DEMATEL fuzzy ANP and fuzzy TOPSIS toevaluate green suppliersrdquo Expert Systems with Applications vol39 no 3 pp 3000ndash3011 2012

[16] C H Hsu F KWang and G H Tzeng ldquoThe best vendor selec-tion for conducting the recycled material based on a hybridMCDM model combining DANP with VIKORrdquo ResourcesConservation and Recycling vol 66 pp 95ndash111 2012

[17] K Govindan R Khodaverdi and A Jafarian ldquoA fuzzy multicriteria approach for measuring sustainability performance ofa supplier based on triple bottom line approachrdquo Journal ofCleaner Production vol 47 pp 345ndash354 2013

[18] L Shen L Olfat K Govindan R Khodaverdi and A DiabatldquoA fuzzy multi criteria approach for evaluating green supplierrsquosperformance in green supply chain with linguistic preferencesrdquoResources Conservation and Recycling vol 74 pp 170ndash179 2013

[19] M Yazdani ldquoAn integrated MCDM approach to green supplierselectionrdquo International Journal of Industrial Engineering Com-putations vol 5 no 3 pp 443ndash458 2014

[20] T Chen and J Freeman ldquoUsing AHP-Entropy weight and TOP-SIS methodology in green supplier selectionrdquo in Proceedings ofthe European Operations Management Association Conference(EurOMA rsquo14) Palermo Italy June 2014

[21] D Kannan A B L D S Jabbour andC J C Jabbour ldquoSelectinggreen suppliers based on GSCM practices using fuzzy TOPSISapplied to a Brazilian electronics companyrdquo European Journal ofOperational Research vol 233 no 2 pp 432ndash447 2014

[22] H Zhao and S Guo ldquoSelecting green supplier of thermalpower equipment by using a Hybrid MCDM Method forsustainabilityrdquo Sustainability vol 6 no 1 pp 217ndash235 2014

[23] J Figueira S Greco and M EhrgottMultiple Criteria DecisionAnalysis State of the Art Surveys Springer Science BostonMass USA 2005

[24] B Roy ldquoThe outranking approach and the foundations ofELECTREmethodsrdquoTheory and Decision vol 31 no 1 pp 49ndash73 1991

[25] J Hokkanen and P Salminen ldquoChoosing a solid waste man-agement systemusingmulticriteria decision analysisrdquoEuropeanJournal of Operational Research vol 98 no 1 pp 19ndash36 1997

[26] R J Lin ldquoUsing fuzzy DEMATEL to evaluate the green supplychain management practicesrdquo Journal of Cleaner Productionvol 40 pp 32ndash39 2013

[27] M L Tseng and A S F Chiu ldquoEvaluating firmrsquos green supplychain management in linguistic preferencesrdquo Journal of CleanerProduction vol 40 pp 22ndash31 2013

[28] G Buyukozkan and G Cifci ldquoA novel fuzzy mulati -criteriadecision framework for sustainable supplier selection withincomplete informationrdquo Computers in Industry vol 62 no 2pp 164ndash174 2011

[29] M T Escobar and J M Moreno-Jimenez ldquoAggregation of indi-vidual preference structures in AHP-group decision makingrdquoGroup Decision andNegotiation vol 16 no 4 pp 287ndash301 2007

[30] V Tsyganok ldquoInvestigation of the aggregation effectiveness ofexpert estimates obtained by the pairwise comparisonmethodrdquoMathematical and ComputerModelling vol 52 no 3-4 pp 538ndash544 2010

[31] J J Bernardo and J M Blin ldquoA programming model ofconsumer choice among multi-attributed brandsrdquo Journal ofConsumer Research vol 4 no 2 pp 111ndash118 1977

[32] P K Humphreys Y K Wong and F T S Chan ldquoIntegratingenvironmental criteria into the supplier selection processrdquoJournal of Materials Processing Technology vol 138 no 1ndash3 pp349ndash356 2003

[33] C Bai and J Sarkis ldquoIntegrating sustainability into supplierselection with grey system and rough set methodologiesrdquoInternational Journal of Production Economics vol 124 no 1 pp252ndash264 2010

[34] X Fu Q Zhu and J Sarkis ldquoEvaluating green supplier devel-opment programs at a telecommunications systems providerrdquoInternational Journal of Production Economics vol 140 no 1pp 357ndash367 2012

[35] A Amindoust S Ahmed A Saghafinia and A BahreininejadldquoSustainable supplier selection a ranking model based on fuzzyinference systemrdquoApplied Soft Computing Journal vol 12 no 6pp 1668ndash1677 2012

[36] K Shaw R Shankar S S Yadav and L S Thakur ldquoSupplierselection using fuzzy AHP and fuzzy multi-objective linearprogramming for developing low carbon supply chainrdquo ExpertSystems with Applications vol 39 no 9 pp 8182ndash8192 2012

Mathematical Problems in Engineering 13

[37] A Awasthi S S Chauhan and S K Goyal ldquoA fuzzy multi-criteria approach for evaluating environmental performance ofsuppliersrdquo International Journal of Production Economics vol126 no 2 pp 370ndash378 2010

[38] T Y Chiou H K Chan F Lettice and S H Chung ldquoThe influ-ence of greening the suppliers and green innovation on envi-ronmental performance and competitive advantage in TaiwanrdquoTransportation Research Part E Logistics and TransportationReview vol 47 no 6 pp 822ndash836 2011

[39] T L Saaty ldquoA scaling method for priorities in hierarchicalstructuresrdquo Journal of Mathematical Psychology vol 15 no 3pp 234ndash281 1977

[40] T L Saaty The Analytic Hierarchy Process McGraw-Hill NewYork NY USA 1980

[41] CMacharis J Springael K De Brucker andA Verbeke ldquoPRO-METHEE and AHP the design of operational synergies inmulticriteria analysis Strengthening PROMETHEE with ideasof AHPrdquo European Journal of Operational Research vol 153 no2 pp 307ndash317 2004

[42] M Zeleny Multiple Criteria Decision Making McGraw-HillNew York NY USA 1982

[43] C L Hwang and K Yoon Multiple Attribute Decision MakingMethods and Applications A State-of-the-Art Survey SpringerNew York NY USA 1981

[44] B Roy ldquoELECTRE III un algorithme de classements fondesur une repre sentation floue des preferences en preferences enpresence de criteres multiplesrdquo Cahiers du Centre drsquoEtudes deRecherche Operationnelle vol 20 no 1 pp 3ndash24 1978

[45] P Vincke Multiple Criteria Decision-Aid John Wiley amp SonsChichester UK 1992

[46] M Rogers and M Bruen ldquoUsing ELECTRE III to chooseroute for Dublin Port motorwayrdquo Journal of TransportationEngineering vol 126 no 4 pp 313ndash323 2000

[47] M Rogers and M Bruen ELECTRE and Decision SupportMethods and Applications in Engineering and InfrastructureInvestment Kluwer Academic Publishers Boston Mass USA2000

[48] V Belton and T J Stewart Multiple Criteria Decision AnalysisAn Integrated Approach Kluwer Academic Publishers BostonMass USA 2002

[49] B Chang C W Chang and C H Wu ldquoFuzzy DEMATELmethod for developing supplier selection criteriardquo Expert Sys-tems with Applications vol 38 no 3 pp 1850ndash1858 2011

Submit your manuscripts athttpwwwhindawicom

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

MathematicsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Mathematical Problems in Engineering

Hindawi Publishing Corporationhttpwwwhindawicom

Differential EquationsInternational Journal of

Volume 2014

Applied MathematicsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Probability and StatisticsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Mathematical PhysicsAdvances in

Complex AnalysisJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

OptimizationJournal of

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CombinatoricsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Operations ResearchAdvances in

Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Function Spaces

Abstract and Applied AnalysisHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of Mathematics and Mathematical Sciences

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

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Discrete Dynamics in Nature and Society

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

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Decision SciencesAdvances in

Discrete MathematicsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom

Volume 2014 Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Stochastic AnalysisInternational Journal of

6 Mathematical Problems in Engineering

Quarterly business reviewQuarterly quality review

Annual and audit

Qualified greensuppliers lists

MM material managementSQM supplier quality managementESH environment safety healthHR human resourceRD research and designGPM green product management

SQMESHHR

SQM (36)MM (27)RD (27)GPM (10)

SQMESHHR

1st tier2nd tierfreeze

New green suppliers

Survey and audit

MMSQM

Figure 1 The decision procedure of green supplier selection in the case study

green supplier selection at Y-TECH Before being a formalpolarizer supplier in Y-TECH the candidate companiesshould pass a two-stage assessment At the first stage anew polarizer supplier should accept a preliminary surveyby the teams of MM and SQM departments and then theteams of SQM ESH and HR departments further executethe preliminary audit for the polarizer suppliers A qualifiedpolarizer manufacturera should pass at least 70 of the itemson all supplier audit forms At the second stage the formalevaluation is conducted by the teams of SQM MM RD andGPM departments After the second-stage assessment thequalified polarizer suppliers will be ranked in the first tierif they pass more than 80 of the items on supplier auditforms and the suppliers passing 70ndash80 of the items willbe ranked in the second tier If the qualified polarizer supplierpasses fewer than 70 of the items it will be regarded asdisqualified All of the formal suppliers are reviewed regularlyby the teams of SQM ESH and HR departments Further-more IS9001 ISO14001 and OHSAS18001 certifications arenot necessary in the evaluation procedure because Y-TECHwill assist their formal suppliers to apply for environmentalcertifications within a limited timeThe first-stage assessmentis the preliminary survey and audit so we focus on applyingthe proposed MCGDM method to the formal evaluation forthe assessment at the second stage

In this case study although four qualified polarizer sup-pliers for Y-TECH outperformed in all other suppliers for thefirst-stage survey our focus will be on supplier 3 because it isa subsidiary company for Y-TECH In fact strategic purchaseis an important strategy to enhance competitiveness in theTFT-LCD industry Therefore the decision procedure is notonly to select the subsidiary into the supply chain but alsoto provide improvement reports for the subsidiary companyAdditionally it is expected that the subsidiary supplier willbe the primary one In addition to the three aspects andfifty criteria (as mentioned in Section 2) we also surveyed12 managers on the expert committee In practice the SQMdepartment holds 36 of the decision-making power theMM and RampD departments 27 and the GPM department10 to select suitable suppliers during the decision-makingprocess

42 Empirical Results First we designed a questionnaire for12 experts to measure the comparative weights between theaspects and criteria Table 2 presents the subjective weights ofeach criterion for DMs based on AHPThemanagers of MMRD GPM and SQM departments are 119863

1 1198632 1198633 1198634 1198635

1198636 1198637 1198638 1198639 and 119863

10 11986311 11986312 respectively Then we

focused on the criteria weights that were 15 more thanthe average weight (01) Notice that many managers think

Mathematical Problems in Engineering 7

Table 2 Subjective weights of each criterion for DMs

Dept MM RD GPM SQMCriteria 119863

11198632

1198633

1198634

1198635

1198636

1198637

1198638

1198639

11986310

11986311

11986312

1198921

0014 0192 0009 0177 0349 0007 0106 0111 0120 0006 0117 00221198922

0014 0021 0033 0421 0050 0007 0032 0111 0120 0042 0290 01191198923

0036 0043 0012 0083 0174 0037 0048 0056 0033 0016 0036 00441198924

0007 0006 0002 0028 0174 0007 0010 0056 0007 0002 0012 00061198925

0405 0006 0048 0016 0009 0131 0093 0093 0004 0019 0011 00171198926

0081 0001 0048 0005 0003 0026 0031 0019 0001 0004 0002 00171198927

0039 0043 0048 0071 0057 0131 0197 0056 0025 0161 0029 00071198928

0039 0009 0048 0010 0019 0026 0049 0056 0008 0032 0010 00021198929

0064 0009 0045 0006 0015 0071 0073 0079 0010 0007 0016 000211989210

0016 0009 0045 0003 0013 0071 0031 0020 0021 0026 0005 000211989211

0007 0002 0006 0001 0004 0014 0019 0013 0003 0044 0018 000111989212

0174 0480 0421 0026 0083 0196 0078 0243 0270 0013 0085 047611989213

0058 0096 0070 0130 0028 0196 0155 0049 0270 0093 0256 015911989214

0035 0069 0143 0019 0006 0020 0039 0021 0081 0089 0028 010611989215

0012 0014 0020 0004 0017 0059 0039 0021 0027 0445 0085 0021

highly of the current capability (11989212) and RampD capability

(11989213) The managers of MM department generally place

importance on the current capability (11989212) and the weights

of 1198631 1198632 and 119863

3are 0174 0480 and 0421 respectively

Moreover MM department is also concerned with the price(1198925) and the average weight is 0153 It is generally agreed

that the managers of RampD department put emphasis onenvironmental factors because of their relevance to productdesign Taking strategic fit (119892

2) for example suppliers should

review environment-related substance list regularly whendeveloping a green productThe average weights of safety andhealth (119892

1) strategic fit (119892

2) environmental control (119892

3) and

recovery (1198924) are 0263 0236 0129 and 0101 respectively

and the importance of environmental factors is over 70 ofthe total weight

In particular GPM department does not give moreweight to environmental factors and the average weight ofenvironmental factors is only 0054 The most likely reasonis that the GPM department holds less decision-makingpower GPM department is concerned with quality control(1198927) current capability (119892

12) and RampD capability (119892

13) and

the average weights are 0102 0196 and 0168 respectivelyOverall themanagers ofGPMdepartment are still concernedwith the current capability (119892

12) and theRampDcapability (119892

13)

Finally SQM department focuses on the strategic fit (1198922)

current capability (11989212) RampD capability (119892

13) and informa-

tion share (11989215) and the average weights of these factors

are 0150 0191 0169 and 0184 respectively Based on ourexpectation SQM department is not significantly concernedwith quality control (119892

7) or other criteria of the enterprise

operation and the average weight for quality control (1198927)

is only 0066 However SQM department emphasizes theimportance of information share (119892

15) Information share

brings the benefit of enhancing the competitiveness of theentire supply chain especially technology development Insum SQM department focuses on the criteria of strategic

Table 3 Objective weights of each criterion for individual DM

Criteria Objective weights1198921

00721198922

00761198923

00671198924

00661198925

00641198926

00751198927

00811198928

00781198929

008011989210

006411989211

004711989212

005011989213

007011989214

005611989215

0054

technology and developmentThus to avoid overly subjectiveweights we include the entropy method in the proposedevaluation procedure using (2) through (5) The objectiveweights of the criteria are shown in Table 3 while Table 4indicates the compromised weights of the criteria for eachDM by (6)

After the compromised criteria weights of the DMsare decided we further apply the criteria weights and theperformance to the ELECTRE III method (7) through (10)to evaluate the four polarizer suppliers For each DM theoutranking degree of any two alternatives can be calculatedby (7) and the results are shown in Table 5 For examplefor manager 119863

1 the outranking degree of alternative 1 to

alternative 3 is 0743 and the outranking degree of alternative3 to alternative 1 is 0493 Obviously manager 119863

1believes

8 Mathematical Problems in Engineering

Table 4 Compromised weights of each criterion for DMs

Dept MM RD GPM SQMCriteria 119863

11198632

1198633

1198634

1198635

1198636

1198637

1198638

1198639

11986310

11986311

11986312

1198921

0016 0230 0011 0176 0367 0008 0109 0121 0135 0007 0123 00271198922

0016 0027 0042 0441 0055 0008 0035 0127 0142 0051 0322 01521198923

0037 0048 0013 0077 0170 0038 0046 0057 0034 0017 0035 00501198924

0007 0007 0002 0025 0168 0007 0009 0056 0007 0002 0012 00071198925

0399 0006 0051 0014 0008 0128 0085 0090 0004 0020 0010 00181198926

0093 0001 0060 0005 0003 0030 0033 0022 0001 0005 0002 00211198927

0049 0058 0065 0079 0067 0162 0229 0068 0032 0209 0034 00101198928

0047 0012 0062 0011 0022 0031 0055 0066 0010 0040 0011 00031198929

0079 0012 0060 0007 0018 0087 0084 0095 0012 0009 0019 000311989210

0016 0010 0048 0003 0012 0069 0028 0019 0021 0027 0005 000211989211

0005 0002 0005 0001 0003 0010 0013 0009 0002 0033 0012 000111989212

0134 0400 0350 0018 0061 0149 0056 0183 0211 0010 0062 040111989213

0062 0112 0081 0126 0029 0209 0156 0052 0295 0104 0262 018711989214

0030 0064 0133 0015 0005 0017 0031 0018 0071 008 0023 010011989215

0010 0013 0018 0003 0013 0049 0030 0017 0023 0385 0067 0019

Table 5 Outranking degrees of each DM

1198631

1198632

1198633

1198861

1198862

1198863

1198864

1198861

1198862

1198863

1198864

1198861

1198862

1198863

1198864

1198861

0 0588 0743 0867 1198861

0 0771 0578 0868 1198861

0 0688 0547 08441198862

0521 0 0598 0633 1198862

0310 0 0374 0425 1198862

0409 0 0442 05291198863

0493 0538 0 0762 1198863

0715 0779 0 0803 1198863

0669 0719 0 07631198864

0591 0619 0479 0 1198864

0783 0787 0476 0 1198864

0771 0735 0488 01198634

1198635

1198636

a1 a2 a3 a4 a1 a2 a3 a4 a1 a2 a3 a41198861

0 0785 0839 0862 1198861

0 0790 0804 0858 1198861

0 0635 0685 07701198862

0306 0 0413 0440 1198862

0378 0 0444 0463 1198862

0477 0 0625 06441198863

0673 0750 0 0780 1198863

0650 0678 0 0703 1198863

0531 0632 0 07111198864

0740 0782 0694 0 1198864

0745 0832 0643 0 1198864

0665 0674 0565 01198637

1198638

1198639

1198861

1198862

1198863

1198864

1198861

1198862

1198863

1198864

1198861

1198862

1198863

1198864

1198861

0 0642 0762 0767 1198861

0 0736 0706 0853 1198861

0 0762 0664 08641198862

0473 0 0602 0634 1198862

0404 0 0492 0518 1198862

0320 0 0465 05201198863

0534 0604 0 0667 1198863

0608 0646 0 0712 1198863

0634 0788 0 08091198864

0687 0663 0628 0 1198864

0734 0777 0588 0 1198864

0702 0735 0532 011986310

11986311

11986312

1198861

1198862

1198863

1198864

1198861

1198862

1198863

1198864

1198861

1198862

1198863

1198864

1198861

0 0471 0557 0585 1198861

0 0762 0754 0841 1198861

0 0765 0562 08861198862

0607 0 0671 0661 1198862

0326 0 0491 0509 1198862

0313 0 0387 04551198863

0660 0704 0 0663 1198863

0630 0776 0 0791 1198863

0697 0798 0 08261198864

0780 0713 0706 0 1198864

0704 0761 0644 0 1198864

0749 0762 0453 0

that alternative 1 is superior to alternative 3 However acharacteristic of the ELECTRE III method is that a penaltyis set if the alternative performs the worst for a criterionThus we can use (9) to calculate the rejecting degree dueto the penalty and the overall outranking degree can beobtained by (10) as shown in Table 6 As another examplefor the 119863

6manager the outranking degree of alternative 3

to alternative 2 is 0632 but the overall outranking degreeof alternative 3 to alternative 2 is 0000 This means thatalternative 3 performs too poorly to be accepted based onsome criteria The results presented in Table 7 show thatalternative 3 performs too poorly on quality control (119892

7) and

delivery (1198929) The rejecting degrees for quality control (119892

7)

and delivery (1198929) are 1000 and 0778 respectively which

Mathematical Problems in Engineering 9

Table 6 Overall outranking degree of each DM

1198631

1198632

1198633

1198861

1198862

1198863

1198864

1198861

1198862

1198863

1198864

1198861

1198862

1198863

1198864

1198861

0 0588 0743 0867 1198861

0 0771 0564 0868 1198861

0 0688 0246 08441198862

0342 0 0331 0633 1198862

0026 0 0 0148 1198862

0115 0 0 04151198863

0273 0334 0 0762 1198863

0715 0779 0 0803 1198863

0669 0719 0 07631198864

0563 0 0384 0 1198864

0783 0787 0361 0 1198864

0771 0735 0207 01198634

1198635

1198636

1198861

1198862

1198863

1198864

1198861

1198862

1198863

1198864

1198861

1198862

1198863

1198864

1198861

0 0785 0839 0862 1198861

0 0790 0804 0858 1198861

0 0245 0483 07461198862

0 0 0 0 1198862

0 0 0 0037 1198862

0090 0 0 06441198863

0673 0750 0 0780 1198863

0413 0468 0 0526 1198863

0091 0 0 05461198864

0633 0782 0694 0 1198864

0745 0832 0643 0 1198864

0 0459 0541 01198637

1198638

1198639

1198861

1198862

1198863

1198864

1198861

1198862

1198863

1198864

1198861

1198862

1198863

1198864

1198861

0 0413 0762 0767 1198861

0 0736 0706 0853 1198861

0 0762 0664 08641198862

0054 0 0336 0634 1198862

0037 0 0 0163 1198862

0005 0 0 01661198863

0094 0 0 0445 1198863

0344 0406 0 0550 1198863

0372 0788 0 08091198864

0 0437 0628 0 1198864

0734 0774 0588 0 1198864

0 0735 0474 011986310

11986311

11986312

1198861

1198862

1198863

1198864

1198861

1198862

1198863

1198864

1198861

1198862

1198863

1198864

1198861

0 0072 0123 0 1198861

0 0762 0754 0841 1198861

0 0765 0509 08861198862

0607 0 0671 0661 1198862

0 0 0 0 1198862

0034 0 0 02611198863

0660 0529 0 0663 1198863

0365 0776 0 0791 1198863

0697 0798 0 08261198864

0780 0713 0706 0 1198864

0 0761 0644 0 1198864

0664 0762 0315 0

Table 7 Rejecting degree for alternative 3 to alternative 2 from 1198896manager

1198921

1198922

1198923

1198924

1198925

1198926

1198927

1198928

1198929

11989210

11989211

11989212

11989213

11989214

11989215

Reject degree 0000 0000 0000 0583 0407 0000 1000 0288 0778 0000 0000 0000 0000 0000 0000

are obviously larger than the outranking degree of 0632 Inthe next section we further discuss which criteria shouldbe improved immediately Accordingly we can establish thedescending and ascending distillation for each DM and theranking results are shown in Table 8

Finally LAM is applied to establish linear programmingfor integrating the opinions of each DM According tothe decision-making power of each department the overallranking matrix is shown as follows

Π =[[[

[

06850 01950 00000 01200

00400 01350 00850 07400

02350 04700 02825 00125

00400 02000 06325 01275

]]]

]

(12)

Therefore we can formulate a linear programming modelusing (11) and obtain the solution which indicates that theoverall ranking result is 119886

1≻ 1198863≻ 1198864≻ 1198862The best supplier is

supplier 1 and the subsidiary company of Y-TECH is secondonly to supplier 1 Supplier 4 is ranked third and supplier 2performs the worst in this evaluation

43 Discussion and Implications In this study we aim topropose a hybridMCGDMmethod to evaluate and assist sup-pliers At the first stage we use the AHP method to measurethe subjective weights of the criteria for each DM Based onthe results of the AHP method it is obvious that most ofthe managers value the importance of current capability andRampD capability and this suggests that Y-TECH places a greatemphasis on technology development In the FPD industrydue to the rapid development of new technology many well-known enterprises regularly release new products to marketevery year A challenge for Y-TECH includes the develop-ment of other technologies such as organic light-emittingdisplays (OLED) For example the OLED panel led to thereduction of the demand for small and medium TFT-LCDdisplays in the global market in 2010 Therefore to enhancecompetitiveness TFT-LCD manufactures connecting withthe suppliers in the entire supply chain should continuouslydevelop new technology Quality control is an importantcriterion secondary only to technological capability At theearly stages of applying a new technology to a new productthe yield rate of the new product is low Consequently it isimportant not only to develop the new technology but alsoto maintain a highly qualified product Currently because

10 Mathematical Problems in Engineering

Table 8 Initial evaluation of individual DM

Dept MM RD GPM SQMRanking 119889

11198892

1198893

1198894

1198895

1198896

1198897

1198898

1198899

11988910

11988911

11988912

1st 1198861

1198861 1198863

1198863

1198861

1198861

1198861

1198861

1198861

1198861

1198862 1198863 1198864

1198861

1198861 1198863

2nd 1198862 1198863

1198861

1198863

1198864

1198862

1198862

1198864

1198863

1198863 1198864

3rd 1198864

1198864

1198864

1198863

1198863

1198863 1198864

1198863

1198864

1198864

4th 1198864

1198862

1198862

1198862

1198862

1198864

1198862

1198862

1198861

1198862

1198862

Table 9 The worst performing criteria of each alternative

1198861

1198862

1198863

1198864

1198631

mdash mdash mdash 1198925

1198632

mdash 11989212

mdash mdash1198633

mdash 11989212

mdash mdash1198634

mdash 1198922

mdash mdash1198635

mdash 1198921

mdash mdash1198636

mdash 11989212

g7 11989213

1198637

mdash mdash g7 11989213

1198638

mdash 11989212

mdash mdash1198639

mdash 11989212

mdash 11989213

11986310

g15 mdash mdash mdash11986311

mdash 1198922

mdash 11989213

11986312

mdash 11989212

mdash mdash

GPM department processes less decision-making power forgreen supplier selection it is obvious that environmentalfactors are not significantly considered in the evaluationprocedure with the exception that RampD department shoulddesign new products and the related materials must be listedfor MM department Then MM department can place anorder and ask the suppliers to follow the list SQMdepartmentis concerned with technological capability as well as infor-mation share Vertical integration is an important enterprisepolicy for the TFT-LCD industry and strategic purchase isa method for the implementation of vertical integration Asmentioned before the key components of TFT-LCD panelsthat are produced in-house have gradually increased year byyear so information share is also an important criterion forcultivating the core capability of enterprise and establishinga competitive advantage Furthermore all of the criteriaplay important roles in the evaluation procedure for greensupplier selection If the proposed hybrid MCGDM methodonly adopts the AHP method it will most likely lead to theoversubjective weighting of the criteria Therefore we applythe entropy method to consider the weights of the criteriasimultaneously

ELECTRE III is applied to evaluate the suppliers Duringthe evaluation procedure we compare the outranking degreeand the overall outranking degree and then decide whichcriterion should be improved immediately for each alterna-tive as shown in Table 9 If the overall outranking degreeof a criterion decreases to zero (comparing to outrankingdegree) this criterion should be improved immediately Forthe best supplier supplier 1 (119886

1) the 119863

10manager suggests

that alternative 1 should strengthen communication with Y-TECH by taking actions such as improving cooperation forthe development of new products and instant market infor-mation share (119892

15) In this case study one of the most impor-

tant aspects is how to provide assistance to the subsidiarycompany (119886

3) of Y-TECH Table 9 indicates that if supplier

3 improves quality control (1198927) for polarizer production its

performance will be improved significantly Generally it iscontended that managers should be concerned about criteriarelated to their job description Due to limited enterpriseresources the order of executing enterprise improvementis also an important issue Following the useful proposedmethod the discovery of the criteria that need to be improvedwill become easy

Different from many previous studies this study doesnot integrate the weights of the criteria at the first stageIn fact green supplier selection is a group decision-makingproblem and integrating the weights would also lead tothe loss of information Therefore we further consider theranking results of each department as shown in Table 10Although these four polarizermanufacturers are the suppliersof Y-TECH there are some differences among the rankingresults of each department especially for suppliers 2 and 4 Asmentioned before RampD department placed great emphasison the ldquogreenrdquo criteria and supplier 4rsquos good performance onthese criteria is the reason for the different ranking resultsConsequently in this study we proposed a novel method andviewpoint for green supplier selection and we suggested thatthe weights of the criteria should not be integrated at thefirst stage because further discussions and communicationare necessary to include the different opinions from eachdepartment Additionally we compare the results of theproposed method with the ones implemented by Y-TECHthat is simple additive method in Table 10 Both results showthat supplier 1 has the best performance and is superior tosupplier 3 Supplier 4 is in the third place while supplier2 is the worst Furthermore within the extensive literatureon green supplier selection methods the TOPSIS methodwas widely applied to evaluate green suppliersrsquo performance[15 17ndash22] Thus we further compare the TOPSIS methodwith the proposedmethod and the comparison is establishedon the basis of AHP-entropy criteria weights In the TOPSISmethod with AHP-entropy weights supplier 3 is in the firstplace and superior to supplier 1 and supplier 4 and supplier2 have the worst performance in this evaluation Althoughthe ranking results among the proposed method and othertwo methods are similar the emphasis of this study is onhow to improve the suppliersrsquo performance in the TFT-LCD industry More importantly the results of the proposed

Mathematical Problems in Engineering 11

Table 10 Comparison of ranking results for the proposed method with other methods

Ranking MM RD GPM SQM The proposed method Current methodlowast AHP-entropy TOPSIS1st 119886

31198861

1198861

1198861

1198861

1198861

1198863

2nd 1198861

1198864

1198862

1198863

1198863

1198863

1198861

3rd 1198864

1198863

1198863

1198864

1198864

1198864

1198864

4th 1198862

1198862

1198864

1198862

1198862

1198862

1198862

lowast

The current evaluation method in Y-TECH is simple additive method

method show that each department has its different opinionsand viewpoints of improvement Each department of theTFT-LCDmanufacturers can assist their suppliers within thecontext of business perspectives

5 Conclusion

In this study we propose a hybrid MCGDM method forgreen supplier selection for Y-TECH Our method focuseson not only the selection or ranking but also the providenceof assistance to suppliers to strengthen their competitivenessThis method also emphasizes continuous improvement Dueto the challenges and difficulties the TFT-LCD industriesworldwide face we intend to establish a systematic selectionprocedure for TFT-LCD manufacturers We developed adecision framework for green supplier selection based onliteratures and the supplier audit forms from Y-TECH Weconsidered AHP and the entropy method to measure thecompromised weights of the criteria that involve subjectiveand objective opinions simultaneously After that the ELEC-TRE III method provides polarizer manufacturersrsquo rankingresults for executive managers in addition to improvementreports The acquisition of key components used for in-house production of TFT-LCD panels has gradually becomea strategy for cost reduction and the enhancement of corecapability Thus TFT-LCD manufacturers should strengthentheir cooperation with their suppliers

Based on the results of the ELECTRE III method wecan compare the differences between the outranking degreeand the overall outranking degree Then we further considerwhich criteria should be improved immediately especially forthe suppliers of strategic purchases The results of this studyprovide an important reference for subsidiary companiesthat are seeking to be primary polarizer suppliers Finallydiffering from many of the previous studies we do notintegrate the weights of the criteria with regard to a singleDM in this study In other worlds we integrate the rankingresults of eachDM instead of theweights of the criteria In theproposed evaluation procedure LAM places more emphasison the discussion and communication among a group ofDMs which is consistent with the practical case

In summary the result of this study can be regardedas a problem-solving process for green supplier selectionRegardless of the proposed method or analytic procedurewe aim at helping TFT-LCD manufacturers to establish asystematic and useful evaluation procedure via the currentstudy Because all of the supplier audit forms are in Microsoft

Excel 2010 spreadsheets the proposedmethod is easy to applyusing the same file Executive managers can consider theperformance of all suppliers and refer to the results of theproposed method Three main contributions are made inthis study First depending on the literatures and supplieraudit forms we develop a decision framework for the TFT-LCD industry Second we propose the useful systematicand flexible hybrid MCGDMmethod (based on the businessprocess) and improve the current evaluation procedureThird we place more emphasis on improving suppliers toenhance the competitiveness of entire supply chain especiallythe subsidiary company In practice green supplier selectionis evaluated by each department separately and all criteria areregarded as independent In recent years increasing attentionhas been given to the interdependent relationships amongthe criteria [15 16 26 49] Therefore future studies arenecessary to clarify the interrelations among the criteriaWe hope that more detailed information influencing therelationships between the criteria can be provided in futureresearch Consequently it would bemore efficient to improvethe supplier selection process for the entire supply chain inthe future work

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper

Acknowledgments

This research was partially supported by the National ScienceCouncil of the Republic of China (Grant NSC 102-2221-E-007-084) The authors would like to thank the editorand anonymous referees for their valuable comments andsuggestions

References

[1] Taiwan Stock Exchange ldquoIndustry analysis the flat paneldisplay Industryrdquo February 2013 httpwwwtwsecomtwchproductspublicationessayphp

[2] S Hung ldquoCompetitive strategies for Taiwanrsquos thin film tran-sistor-liquid crystal display (TFT-LCD) industryrdquoTechnology inSociety vol 28 no 3 pp 349ndash361 2006

[3] W Ho X Xu and P K Dey ldquoMulti-criteria decision makingapproaches for supplier evaluation and selection a literaturereviewrdquo European Journal of Operational Research vol 202 no1 pp 16ndash24 2010

12 Mathematical Problems in Engineering

[4] J Chai J N K Liu and E W T Ngai ldquoApplication of decision-making techniques in supplier selection a systematic review ofliteraturerdquo Expert Systems with Applications vol 40 no 10 pp3872ndash3885 2013

[5] K Govindan S Rajendran J Sarkis and P Murugesan ldquoMulticriteria decision making approaches for green supplier eval-uation and selection a literature reviewrdquo Journal of CleanerProduction 2013

[6] M Herva and E Roca ldquoReview of combined approaches andmulti-criteria analysis for corporate environmental evaluationrdquoJournal of Cleaner Production vol 39 pp 355ndash371 2013

[7] S Seuring ldquoA review of modeling approaches for sustainablesupply chain managementrdquo Decision Support Systems vol 54no 4 pp 1513ndash1520 2013

[8] M Igarashi L De Boer and A M Fet ldquoWhat is required forgreener supplier selection A literature review and conceptualmodel developmentrdquo Journal of Purchasing and SupplyManage-ment vol 19 no 4 pp 247ndash263 2013

[9] A Genovese S C Koh G Bruno and E Esposito ldquoGreenersupplier selection state of the art and some empirical evidencerdquoInternational Journal of Production Research vol 51 no 10 pp2868ndash2886 2013

[10] A H I Lee H Y Kang C F Hsu and H C Hung ldquoA greensupplier selectionmodel for high-tech industryrdquo Expert Systemswith Applications vol 36 no 4 pp 7917ndash7927 2009

[11] G Tuzkaya A Ozgen D Ozgen and U R Tuzkaya ldquoEnvi-ronmental performance evaluation of suppliers a hybrid fuzzymulti-criteria decision approachrdquo International Journal of Envi-ronmental Science and Technology vol 6 no 3 pp 477ndash4902009

[12] R J Kuo Y C Wang and F C Tien ldquoIntegration of artificialneural network and MADA methods for green supplier selec-tionrdquo Journal of Cleaner Production vol 18 no 12 pp 1161ndash11702010

[13] Q Zhu Y Dou and J Sarkis ldquoA portfolio-based analysis forgreen supplier management using the analytical network pro-cessrdquo Supply Chain Management vol 15 no 4 pp 306ndash3192010

[14] G Buyukozkan ldquoAn integrated fuzzy multi-criteria groupdecision-making approach for green supplier evaluationrdquo Inter-national Journal of Production Research vol 50 no 11 pp 2892ndash2909 2012

[15] G Buyukozkan and G Cifci ldquoA novel hybrid MCDM approachbased on fuzzy DEMATEL fuzzy ANP and fuzzy TOPSIS toevaluate green suppliersrdquo Expert Systems with Applications vol39 no 3 pp 3000ndash3011 2012

[16] C H Hsu F KWang and G H Tzeng ldquoThe best vendor selec-tion for conducting the recycled material based on a hybridMCDM model combining DANP with VIKORrdquo ResourcesConservation and Recycling vol 66 pp 95ndash111 2012

[17] K Govindan R Khodaverdi and A Jafarian ldquoA fuzzy multicriteria approach for measuring sustainability performance ofa supplier based on triple bottom line approachrdquo Journal ofCleaner Production vol 47 pp 345ndash354 2013

[18] L Shen L Olfat K Govindan R Khodaverdi and A DiabatldquoA fuzzy multi criteria approach for evaluating green supplierrsquosperformance in green supply chain with linguistic preferencesrdquoResources Conservation and Recycling vol 74 pp 170ndash179 2013

[19] M Yazdani ldquoAn integrated MCDM approach to green supplierselectionrdquo International Journal of Industrial Engineering Com-putations vol 5 no 3 pp 443ndash458 2014

[20] T Chen and J Freeman ldquoUsing AHP-Entropy weight and TOP-SIS methodology in green supplier selectionrdquo in Proceedings ofthe European Operations Management Association Conference(EurOMA rsquo14) Palermo Italy June 2014

[21] D Kannan A B L D S Jabbour andC J C Jabbour ldquoSelectinggreen suppliers based on GSCM practices using fuzzy TOPSISapplied to a Brazilian electronics companyrdquo European Journal ofOperational Research vol 233 no 2 pp 432ndash447 2014

[22] H Zhao and S Guo ldquoSelecting green supplier of thermalpower equipment by using a Hybrid MCDM Method forsustainabilityrdquo Sustainability vol 6 no 1 pp 217ndash235 2014

[23] J Figueira S Greco and M EhrgottMultiple Criteria DecisionAnalysis State of the Art Surveys Springer Science BostonMass USA 2005

[24] B Roy ldquoThe outranking approach and the foundations ofELECTREmethodsrdquoTheory and Decision vol 31 no 1 pp 49ndash73 1991

[25] J Hokkanen and P Salminen ldquoChoosing a solid waste man-agement systemusingmulticriteria decision analysisrdquoEuropeanJournal of Operational Research vol 98 no 1 pp 19ndash36 1997

[26] R J Lin ldquoUsing fuzzy DEMATEL to evaluate the green supplychain management practicesrdquo Journal of Cleaner Productionvol 40 pp 32ndash39 2013

[27] M L Tseng and A S F Chiu ldquoEvaluating firmrsquos green supplychain management in linguistic preferencesrdquo Journal of CleanerProduction vol 40 pp 22ndash31 2013

[28] G Buyukozkan and G Cifci ldquoA novel fuzzy mulati -criteriadecision framework for sustainable supplier selection withincomplete informationrdquo Computers in Industry vol 62 no 2pp 164ndash174 2011

[29] M T Escobar and J M Moreno-Jimenez ldquoAggregation of indi-vidual preference structures in AHP-group decision makingrdquoGroup Decision andNegotiation vol 16 no 4 pp 287ndash301 2007

[30] V Tsyganok ldquoInvestigation of the aggregation effectiveness ofexpert estimates obtained by the pairwise comparisonmethodrdquoMathematical and ComputerModelling vol 52 no 3-4 pp 538ndash544 2010

[31] J J Bernardo and J M Blin ldquoA programming model ofconsumer choice among multi-attributed brandsrdquo Journal ofConsumer Research vol 4 no 2 pp 111ndash118 1977

[32] P K Humphreys Y K Wong and F T S Chan ldquoIntegratingenvironmental criteria into the supplier selection processrdquoJournal of Materials Processing Technology vol 138 no 1ndash3 pp349ndash356 2003

[33] C Bai and J Sarkis ldquoIntegrating sustainability into supplierselection with grey system and rough set methodologiesrdquoInternational Journal of Production Economics vol 124 no 1 pp252ndash264 2010

[34] X Fu Q Zhu and J Sarkis ldquoEvaluating green supplier devel-opment programs at a telecommunications systems providerrdquoInternational Journal of Production Economics vol 140 no 1pp 357ndash367 2012

[35] A Amindoust S Ahmed A Saghafinia and A BahreininejadldquoSustainable supplier selection a ranking model based on fuzzyinference systemrdquoApplied Soft Computing Journal vol 12 no 6pp 1668ndash1677 2012

[36] K Shaw R Shankar S S Yadav and L S Thakur ldquoSupplierselection using fuzzy AHP and fuzzy multi-objective linearprogramming for developing low carbon supply chainrdquo ExpertSystems with Applications vol 39 no 9 pp 8182ndash8192 2012

Mathematical Problems in Engineering 13

[37] A Awasthi S S Chauhan and S K Goyal ldquoA fuzzy multi-criteria approach for evaluating environmental performance ofsuppliersrdquo International Journal of Production Economics vol126 no 2 pp 370ndash378 2010

[38] T Y Chiou H K Chan F Lettice and S H Chung ldquoThe influ-ence of greening the suppliers and green innovation on envi-ronmental performance and competitive advantage in TaiwanrdquoTransportation Research Part E Logistics and TransportationReview vol 47 no 6 pp 822ndash836 2011

[39] T L Saaty ldquoA scaling method for priorities in hierarchicalstructuresrdquo Journal of Mathematical Psychology vol 15 no 3pp 234ndash281 1977

[40] T L Saaty The Analytic Hierarchy Process McGraw-Hill NewYork NY USA 1980

[41] CMacharis J Springael K De Brucker andA Verbeke ldquoPRO-METHEE and AHP the design of operational synergies inmulticriteria analysis Strengthening PROMETHEE with ideasof AHPrdquo European Journal of Operational Research vol 153 no2 pp 307ndash317 2004

[42] M Zeleny Multiple Criteria Decision Making McGraw-HillNew York NY USA 1982

[43] C L Hwang and K Yoon Multiple Attribute Decision MakingMethods and Applications A State-of-the-Art Survey SpringerNew York NY USA 1981

[44] B Roy ldquoELECTRE III un algorithme de classements fondesur une repre sentation floue des preferences en preferences enpresence de criteres multiplesrdquo Cahiers du Centre drsquoEtudes deRecherche Operationnelle vol 20 no 1 pp 3ndash24 1978

[45] P Vincke Multiple Criteria Decision-Aid John Wiley amp SonsChichester UK 1992

[46] M Rogers and M Bruen ldquoUsing ELECTRE III to chooseroute for Dublin Port motorwayrdquo Journal of TransportationEngineering vol 126 no 4 pp 313ndash323 2000

[47] M Rogers and M Bruen ELECTRE and Decision SupportMethods and Applications in Engineering and InfrastructureInvestment Kluwer Academic Publishers Boston Mass USA2000

[48] V Belton and T J Stewart Multiple Criteria Decision AnalysisAn Integrated Approach Kluwer Academic Publishers BostonMass USA 2002

[49] B Chang C W Chang and C H Wu ldquoFuzzy DEMATELmethod for developing supplier selection criteriardquo Expert Sys-tems with Applications vol 38 no 3 pp 1850ndash1858 2011

Submit your manuscripts athttpwwwhindawicom

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Mathematical Problems in Engineering

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Differential EquationsInternational Journal of

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Stochastic AnalysisInternational Journal of

Mathematical Problems in Engineering 7

Table 2 Subjective weights of each criterion for DMs

Dept MM RD GPM SQMCriteria 119863

11198632

1198633

1198634

1198635

1198636

1198637

1198638

1198639

11986310

11986311

11986312

1198921

0014 0192 0009 0177 0349 0007 0106 0111 0120 0006 0117 00221198922

0014 0021 0033 0421 0050 0007 0032 0111 0120 0042 0290 01191198923

0036 0043 0012 0083 0174 0037 0048 0056 0033 0016 0036 00441198924

0007 0006 0002 0028 0174 0007 0010 0056 0007 0002 0012 00061198925

0405 0006 0048 0016 0009 0131 0093 0093 0004 0019 0011 00171198926

0081 0001 0048 0005 0003 0026 0031 0019 0001 0004 0002 00171198927

0039 0043 0048 0071 0057 0131 0197 0056 0025 0161 0029 00071198928

0039 0009 0048 0010 0019 0026 0049 0056 0008 0032 0010 00021198929

0064 0009 0045 0006 0015 0071 0073 0079 0010 0007 0016 000211989210

0016 0009 0045 0003 0013 0071 0031 0020 0021 0026 0005 000211989211

0007 0002 0006 0001 0004 0014 0019 0013 0003 0044 0018 000111989212

0174 0480 0421 0026 0083 0196 0078 0243 0270 0013 0085 047611989213

0058 0096 0070 0130 0028 0196 0155 0049 0270 0093 0256 015911989214

0035 0069 0143 0019 0006 0020 0039 0021 0081 0089 0028 010611989215

0012 0014 0020 0004 0017 0059 0039 0021 0027 0445 0085 0021

highly of the current capability (11989212) and RampD capability

(11989213) The managers of MM department generally place

importance on the current capability (11989212) and the weights

of 1198631 1198632 and 119863

3are 0174 0480 and 0421 respectively

Moreover MM department is also concerned with the price(1198925) and the average weight is 0153 It is generally agreed

that the managers of RampD department put emphasis onenvironmental factors because of their relevance to productdesign Taking strategic fit (119892

2) for example suppliers should

review environment-related substance list regularly whendeveloping a green productThe average weights of safety andhealth (119892

1) strategic fit (119892

2) environmental control (119892

3) and

recovery (1198924) are 0263 0236 0129 and 0101 respectively

and the importance of environmental factors is over 70 ofthe total weight

In particular GPM department does not give moreweight to environmental factors and the average weight ofenvironmental factors is only 0054 The most likely reasonis that the GPM department holds less decision-makingpower GPM department is concerned with quality control(1198927) current capability (119892

12) and RampD capability (119892

13) and

the average weights are 0102 0196 and 0168 respectivelyOverall themanagers ofGPMdepartment are still concernedwith the current capability (119892

12) and theRampDcapability (119892

13)

Finally SQM department focuses on the strategic fit (1198922)

current capability (11989212) RampD capability (119892

13) and informa-

tion share (11989215) and the average weights of these factors

are 0150 0191 0169 and 0184 respectively Based on ourexpectation SQM department is not significantly concernedwith quality control (119892

7) or other criteria of the enterprise

operation and the average weight for quality control (1198927)

is only 0066 However SQM department emphasizes theimportance of information share (119892

15) Information share

brings the benefit of enhancing the competitiveness of theentire supply chain especially technology development Insum SQM department focuses on the criteria of strategic

Table 3 Objective weights of each criterion for individual DM

Criteria Objective weights1198921

00721198922

00761198923

00671198924

00661198925

00641198926

00751198927

00811198928

00781198929

008011989210

006411989211

004711989212

005011989213

007011989214

005611989215

0054

technology and developmentThus to avoid overly subjectiveweights we include the entropy method in the proposedevaluation procedure using (2) through (5) The objectiveweights of the criteria are shown in Table 3 while Table 4indicates the compromised weights of the criteria for eachDM by (6)

After the compromised criteria weights of the DMsare decided we further apply the criteria weights and theperformance to the ELECTRE III method (7) through (10)to evaluate the four polarizer suppliers For each DM theoutranking degree of any two alternatives can be calculatedby (7) and the results are shown in Table 5 For examplefor manager 119863

1 the outranking degree of alternative 1 to

alternative 3 is 0743 and the outranking degree of alternative3 to alternative 1 is 0493 Obviously manager 119863

1believes

8 Mathematical Problems in Engineering

Table 4 Compromised weights of each criterion for DMs

Dept MM RD GPM SQMCriteria 119863

11198632

1198633

1198634

1198635

1198636

1198637

1198638

1198639

11986310

11986311

11986312

1198921

0016 0230 0011 0176 0367 0008 0109 0121 0135 0007 0123 00271198922

0016 0027 0042 0441 0055 0008 0035 0127 0142 0051 0322 01521198923

0037 0048 0013 0077 0170 0038 0046 0057 0034 0017 0035 00501198924

0007 0007 0002 0025 0168 0007 0009 0056 0007 0002 0012 00071198925

0399 0006 0051 0014 0008 0128 0085 0090 0004 0020 0010 00181198926

0093 0001 0060 0005 0003 0030 0033 0022 0001 0005 0002 00211198927

0049 0058 0065 0079 0067 0162 0229 0068 0032 0209 0034 00101198928

0047 0012 0062 0011 0022 0031 0055 0066 0010 0040 0011 00031198929

0079 0012 0060 0007 0018 0087 0084 0095 0012 0009 0019 000311989210

0016 0010 0048 0003 0012 0069 0028 0019 0021 0027 0005 000211989211

0005 0002 0005 0001 0003 0010 0013 0009 0002 0033 0012 000111989212

0134 0400 0350 0018 0061 0149 0056 0183 0211 0010 0062 040111989213

0062 0112 0081 0126 0029 0209 0156 0052 0295 0104 0262 018711989214

0030 0064 0133 0015 0005 0017 0031 0018 0071 008 0023 010011989215

0010 0013 0018 0003 0013 0049 0030 0017 0023 0385 0067 0019

Table 5 Outranking degrees of each DM

1198631

1198632

1198633

1198861

1198862

1198863

1198864

1198861

1198862

1198863

1198864

1198861

1198862

1198863

1198864

1198861

0 0588 0743 0867 1198861

0 0771 0578 0868 1198861

0 0688 0547 08441198862

0521 0 0598 0633 1198862

0310 0 0374 0425 1198862

0409 0 0442 05291198863

0493 0538 0 0762 1198863

0715 0779 0 0803 1198863

0669 0719 0 07631198864

0591 0619 0479 0 1198864

0783 0787 0476 0 1198864

0771 0735 0488 01198634

1198635

1198636

a1 a2 a3 a4 a1 a2 a3 a4 a1 a2 a3 a41198861

0 0785 0839 0862 1198861

0 0790 0804 0858 1198861

0 0635 0685 07701198862

0306 0 0413 0440 1198862

0378 0 0444 0463 1198862

0477 0 0625 06441198863

0673 0750 0 0780 1198863

0650 0678 0 0703 1198863

0531 0632 0 07111198864

0740 0782 0694 0 1198864

0745 0832 0643 0 1198864

0665 0674 0565 01198637

1198638

1198639

1198861

1198862

1198863

1198864

1198861

1198862

1198863

1198864

1198861

1198862

1198863

1198864

1198861

0 0642 0762 0767 1198861

0 0736 0706 0853 1198861

0 0762 0664 08641198862

0473 0 0602 0634 1198862

0404 0 0492 0518 1198862

0320 0 0465 05201198863

0534 0604 0 0667 1198863

0608 0646 0 0712 1198863

0634 0788 0 08091198864

0687 0663 0628 0 1198864

0734 0777 0588 0 1198864

0702 0735 0532 011986310

11986311

11986312

1198861

1198862

1198863

1198864

1198861

1198862

1198863

1198864

1198861

1198862

1198863

1198864

1198861

0 0471 0557 0585 1198861

0 0762 0754 0841 1198861

0 0765 0562 08861198862

0607 0 0671 0661 1198862

0326 0 0491 0509 1198862

0313 0 0387 04551198863

0660 0704 0 0663 1198863

0630 0776 0 0791 1198863

0697 0798 0 08261198864

0780 0713 0706 0 1198864

0704 0761 0644 0 1198864

0749 0762 0453 0

that alternative 1 is superior to alternative 3 However acharacteristic of the ELECTRE III method is that a penaltyis set if the alternative performs the worst for a criterionThus we can use (9) to calculate the rejecting degree dueto the penalty and the overall outranking degree can beobtained by (10) as shown in Table 6 As another examplefor the 119863

6manager the outranking degree of alternative 3

to alternative 2 is 0632 but the overall outranking degreeof alternative 3 to alternative 2 is 0000 This means thatalternative 3 performs too poorly to be accepted based onsome criteria The results presented in Table 7 show thatalternative 3 performs too poorly on quality control (119892

7) and

delivery (1198929) The rejecting degrees for quality control (119892

7)

and delivery (1198929) are 1000 and 0778 respectively which

Mathematical Problems in Engineering 9

Table 6 Overall outranking degree of each DM

1198631

1198632

1198633

1198861

1198862

1198863

1198864

1198861

1198862

1198863

1198864

1198861

1198862

1198863

1198864

1198861

0 0588 0743 0867 1198861

0 0771 0564 0868 1198861

0 0688 0246 08441198862

0342 0 0331 0633 1198862

0026 0 0 0148 1198862

0115 0 0 04151198863

0273 0334 0 0762 1198863

0715 0779 0 0803 1198863

0669 0719 0 07631198864

0563 0 0384 0 1198864

0783 0787 0361 0 1198864

0771 0735 0207 01198634

1198635

1198636

1198861

1198862

1198863

1198864

1198861

1198862

1198863

1198864

1198861

1198862

1198863

1198864

1198861

0 0785 0839 0862 1198861

0 0790 0804 0858 1198861

0 0245 0483 07461198862

0 0 0 0 1198862

0 0 0 0037 1198862

0090 0 0 06441198863

0673 0750 0 0780 1198863

0413 0468 0 0526 1198863

0091 0 0 05461198864

0633 0782 0694 0 1198864

0745 0832 0643 0 1198864

0 0459 0541 01198637

1198638

1198639

1198861

1198862

1198863

1198864

1198861

1198862

1198863

1198864

1198861

1198862

1198863

1198864

1198861

0 0413 0762 0767 1198861

0 0736 0706 0853 1198861

0 0762 0664 08641198862

0054 0 0336 0634 1198862

0037 0 0 0163 1198862

0005 0 0 01661198863

0094 0 0 0445 1198863

0344 0406 0 0550 1198863

0372 0788 0 08091198864

0 0437 0628 0 1198864

0734 0774 0588 0 1198864

0 0735 0474 011986310

11986311

11986312

1198861

1198862

1198863

1198864

1198861

1198862

1198863

1198864

1198861

1198862

1198863

1198864

1198861

0 0072 0123 0 1198861

0 0762 0754 0841 1198861

0 0765 0509 08861198862

0607 0 0671 0661 1198862

0 0 0 0 1198862

0034 0 0 02611198863

0660 0529 0 0663 1198863

0365 0776 0 0791 1198863

0697 0798 0 08261198864

0780 0713 0706 0 1198864

0 0761 0644 0 1198864

0664 0762 0315 0

Table 7 Rejecting degree for alternative 3 to alternative 2 from 1198896manager

1198921

1198922

1198923

1198924

1198925

1198926

1198927

1198928

1198929

11989210

11989211

11989212

11989213

11989214

11989215

Reject degree 0000 0000 0000 0583 0407 0000 1000 0288 0778 0000 0000 0000 0000 0000 0000

are obviously larger than the outranking degree of 0632 Inthe next section we further discuss which criteria shouldbe improved immediately Accordingly we can establish thedescending and ascending distillation for each DM and theranking results are shown in Table 8

Finally LAM is applied to establish linear programmingfor integrating the opinions of each DM According tothe decision-making power of each department the overallranking matrix is shown as follows

Π =[[[

[

06850 01950 00000 01200

00400 01350 00850 07400

02350 04700 02825 00125

00400 02000 06325 01275

]]]

]

(12)

Therefore we can formulate a linear programming modelusing (11) and obtain the solution which indicates that theoverall ranking result is 119886

1≻ 1198863≻ 1198864≻ 1198862The best supplier is

supplier 1 and the subsidiary company of Y-TECH is secondonly to supplier 1 Supplier 4 is ranked third and supplier 2performs the worst in this evaluation

43 Discussion and Implications In this study we aim topropose a hybridMCGDMmethod to evaluate and assist sup-pliers At the first stage we use the AHP method to measurethe subjective weights of the criteria for each DM Based onthe results of the AHP method it is obvious that most ofthe managers value the importance of current capability andRampD capability and this suggests that Y-TECH places a greatemphasis on technology development In the FPD industrydue to the rapid development of new technology many well-known enterprises regularly release new products to marketevery year A challenge for Y-TECH includes the develop-ment of other technologies such as organic light-emittingdisplays (OLED) For example the OLED panel led to thereduction of the demand for small and medium TFT-LCDdisplays in the global market in 2010 Therefore to enhancecompetitiveness TFT-LCD manufactures connecting withthe suppliers in the entire supply chain should continuouslydevelop new technology Quality control is an importantcriterion secondary only to technological capability At theearly stages of applying a new technology to a new productthe yield rate of the new product is low Consequently it isimportant not only to develop the new technology but alsoto maintain a highly qualified product Currently because

10 Mathematical Problems in Engineering

Table 8 Initial evaluation of individual DM

Dept MM RD GPM SQMRanking 119889

11198892

1198893

1198894

1198895

1198896

1198897

1198898

1198899

11988910

11988911

11988912

1st 1198861

1198861 1198863

1198863

1198861

1198861

1198861

1198861

1198861

1198861

1198862 1198863 1198864

1198861

1198861 1198863

2nd 1198862 1198863

1198861

1198863

1198864

1198862

1198862

1198864

1198863

1198863 1198864

3rd 1198864

1198864

1198864

1198863

1198863

1198863 1198864

1198863

1198864

1198864

4th 1198864

1198862

1198862

1198862

1198862

1198864

1198862

1198862

1198861

1198862

1198862

Table 9 The worst performing criteria of each alternative

1198861

1198862

1198863

1198864

1198631

mdash mdash mdash 1198925

1198632

mdash 11989212

mdash mdash1198633

mdash 11989212

mdash mdash1198634

mdash 1198922

mdash mdash1198635

mdash 1198921

mdash mdash1198636

mdash 11989212

g7 11989213

1198637

mdash mdash g7 11989213

1198638

mdash 11989212

mdash mdash1198639

mdash 11989212

mdash 11989213

11986310

g15 mdash mdash mdash11986311

mdash 1198922

mdash 11989213

11986312

mdash 11989212

mdash mdash

GPM department processes less decision-making power forgreen supplier selection it is obvious that environmentalfactors are not significantly considered in the evaluationprocedure with the exception that RampD department shoulddesign new products and the related materials must be listedfor MM department Then MM department can place anorder and ask the suppliers to follow the list SQMdepartmentis concerned with technological capability as well as infor-mation share Vertical integration is an important enterprisepolicy for the TFT-LCD industry and strategic purchase isa method for the implementation of vertical integration Asmentioned before the key components of TFT-LCD panelsthat are produced in-house have gradually increased year byyear so information share is also an important criterion forcultivating the core capability of enterprise and establishinga competitive advantage Furthermore all of the criteriaplay important roles in the evaluation procedure for greensupplier selection If the proposed hybrid MCGDM methodonly adopts the AHP method it will most likely lead to theoversubjective weighting of the criteria Therefore we applythe entropy method to consider the weights of the criteriasimultaneously

ELECTRE III is applied to evaluate the suppliers Duringthe evaluation procedure we compare the outranking degreeand the overall outranking degree and then decide whichcriterion should be improved immediately for each alterna-tive as shown in Table 9 If the overall outranking degreeof a criterion decreases to zero (comparing to outrankingdegree) this criterion should be improved immediately Forthe best supplier supplier 1 (119886

1) the 119863

10manager suggests

that alternative 1 should strengthen communication with Y-TECH by taking actions such as improving cooperation forthe development of new products and instant market infor-mation share (119892

15) In this case study one of the most impor-

tant aspects is how to provide assistance to the subsidiarycompany (119886

3) of Y-TECH Table 9 indicates that if supplier

3 improves quality control (1198927) for polarizer production its

performance will be improved significantly Generally it iscontended that managers should be concerned about criteriarelated to their job description Due to limited enterpriseresources the order of executing enterprise improvementis also an important issue Following the useful proposedmethod the discovery of the criteria that need to be improvedwill become easy

Different from many previous studies this study doesnot integrate the weights of the criteria at the first stageIn fact green supplier selection is a group decision-makingproblem and integrating the weights would also lead tothe loss of information Therefore we further consider theranking results of each department as shown in Table 10Although these four polarizermanufacturers are the suppliersof Y-TECH there are some differences among the rankingresults of each department especially for suppliers 2 and 4 Asmentioned before RampD department placed great emphasison the ldquogreenrdquo criteria and supplier 4rsquos good performance onthese criteria is the reason for the different ranking resultsConsequently in this study we proposed a novel method andviewpoint for green supplier selection and we suggested thatthe weights of the criteria should not be integrated at thefirst stage because further discussions and communicationare necessary to include the different opinions from eachdepartment Additionally we compare the results of theproposed method with the ones implemented by Y-TECHthat is simple additive method in Table 10 Both results showthat supplier 1 has the best performance and is superior tosupplier 3 Supplier 4 is in the third place while supplier2 is the worst Furthermore within the extensive literatureon green supplier selection methods the TOPSIS methodwas widely applied to evaluate green suppliersrsquo performance[15 17ndash22] Thus we further compare the TOPSIS methodwith the proposedmethod and the comparison is establishedon the basis of AHP-entropy criteria weights In the TOPSISmethod with AHP-entropy weights supplier 3 is in the firstplace and superior to supplier 1 and supplier 4 and supplier2 have the worst performance in this evaluation Althoughthe ranking results among the proposed method and othertwo methods are similar the emphasis of this study is onhow to improve the suppliersrsquo performance in the TFT-LCD industry More importantly the results of the proposed

Mathematical Problems in Engineering 11

Table 10 Comparison of ranking results for the proposed method with other methods

Ranking MM RD GPM SQM The proposed method Current methodlowast AHP-entropy TOPSIS1st 119886

31198861

1198861

1198861

1198861

1198861

1198863

2nd 1198861

1198864

1198862

1198863

1198863

1198863

1198861

3rd 1198864

1198863

1198863

1198864

1198864

1198864

1198864

4th 1198862

1198862

1198864

1198862

1198862

1198862

1198862

lowast

The current evaluation method in Y-TECH is simple additive method

method show that each department has its different opinionsand viewpoints of improvement Each department of theTFT-LCDmanufacturers can assist their suppliers within thecontext of business perspectives

5 Conclusion

In this study we propose a hybrid MCGDM method forgreen supplier selection for Y-TECH Our method focuseson not only the selection or ranking but also the providenceof assistance to suppliers to strengthen their competitivenessThis method also emphasizes continuous improvement Dueto the challenges and difficulties the TFT-LCD industriesworldwide face we intend to establish a systematic selectionprocedure for TFT-LCD manufacturers We developed adecision framework for green supplier selection based onliteratures and the supplier audit forms from Y-TECH Weconsidered AHP and the entropy method to measure thecompromised weights of the criteria that involve subjectiveand objective opinions simultaneously After that the ELEC-TRE III method provides polarizer manufacturersrsquo rankingresults for executive managers in addition to improvementreports The acquisition of key components used for in-house production of TFT-LCD panels has gradually becomea strategy for cost reduction and the enhancement of corecapability Thus TFT-LCD manufacturers should strengthentheir cooperation with their suppliers

Based on the results of the ELECTRE III method wecan compare the differences between the outranking degreeand the overall outranking degree Then we further considerwhich criteria should be improved immediately especially forthe suppliers of strategic purchases The results of this studyprovide an important reference for subsidiary companiesthat are seeking to be primary polarizer suppliers Finallydiffering from many of the previous studies we do notintegrate the weights of the criteria with regard to a singleDM in this study In other worlds we integrate the rankingresults of eachDM instead of theweights of the criteria In theproposed evaluation procedure LAM places more emphasison the discussion and communication among a group ofDMs which is consistent with the practical case

In summary the result of this study can be regardedas a problem-solving process for green supplier selectionRegardless of the proposed method or analytic procedurewe aim at helping TFT-LCD manufacturers to establish asystematic and useful evaluation procedure via the currentstudy Because all of the supplier audit forms are in Microsoft

Excel 2010 spreadsheets the proposedmethod is easy to applyusing the same file Executive managers can consider theperformance of all suppliers and refer to the results of theproposed method Three main contributions are made inthis study First depending on the literatures and supplieraudit forms we develop a decision framework for the TFT-LCD industry Second we propose the useful systematicand flexible hybrid MCGDMmethod (based on the businessprocess) and improve the current evaluation procedureThird we place more emphasis on improving suppliers toenhance the competitiveness of entire supply chain especiallythe subsidiary company In practice green supplier selectionis evaluated by each department separately and all criteria areregarded as independent In recent years increasing attentionhas been given to the interdependent relationships amongthe criteria [15 16 26 49] Therefore future studies arenecessary to clarify the interrelations among the criteriaWe hope that more detailed information influencing therelationships between the criteria can be provided in futureresearch Consequently it would bemore efficient to improvethe supplier selection process for the entire supply chain inthe future work

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper

Acknowledgments

This research was partially supported by the National ScienceCouncil of the Republic of China (Grant NSC 102-2221-E-007-084) The authors would like to thank the editorand anonymous referees for their valuable comments andsuggestions

References

[1] Taiwan Stock Exchange ldquoIndustry analysis the flat paneldisplay Industryrdquo February 2013 httpwwwtwsecomtwchproductspublicationessayphp

[2] S Hung ldquoCompetitive strategies for Taiwanrsquos thin film tran-sistor-liquid crystal display (TFT-LCD) industryrdquoTechnology inSociety vol 28 no 3 pp 349ndash361 2006

[3] W Ho X Xu and P K Dey ldquoMulti-criteria decision makingapproaches for supplier evaluation and selection a literaturereviewrdquo European Journal of Operational Research vol 202 no1 pp 16ndash24 2010

12 Mathematical Problems in Engineering

[4] J Chai J N K Liu and E W T Ngai ldquoApplication of decision-making techniques in supplier selection a systematic review ofliteraturerdquo Expert Systems with Applications vol 40 no 10 pp3872ndash3885 2013

[5] K Govindan S Rajendran J Sarkis and P Murugesan ldquoMulticriteria decision making approaches for green supplier eval-uation and selection a literature reviewrdquo Journal of CleanerProduction 2013

[6] M Herva and E Roca ldquoReview of combined approaches andmulti-criteria analysis for corporate environmental evaluationrdquoJournal of Cleaner Production vol 39 pp 355ndash371 2013

[7] S Seuring ldquoA review of modeling approaches for sustainablesupply chain managementrdquo Decision Support Systems vol 54no 4 pp 1513ndash1520 2013

[8] M Igarashi L De Boer and A M Fet ldquoWhat is required forgreener supplier selection A literature review and conceptualmodel developmentrdquo Journal of Purchasing and SupplyManage-ment vol 19 no 4 pp 247ndash263 2013

[9] A Genovese S C Koh G Bruno and E Esposito ldquoGreenersupplier selection state of the art and some empirical evidencerdquoInternational Journal of Production Research vol 51 no 10 pp2868ndash2886 2013

[10] A H I Lee H Y Kang C F Hsu and H C Hung ldquoA greensupplier selectionmodel for high-tech industryrdquo Expert Systemswith Applications vol 36 no 4 pp 7917ndash7927 2009

[11] G Tuzkaya A Ozgen D Ozgen and U R Tuzkaya ldquoEnvi-ronmental performance evaluation of suppliers a hybrid fuzzymulti-criteria decision approachrdquo International Journal of Envi-ronmental Science and Technology vol 6 no 3 pp 477ndash4902009

[12] R J Kuo Y C Wang and F C Tien ldquoIntegration of artificialneural network and MADA methods for green supplier selec-tionrdquo Journal of Cleaner Production vol 18 no 12 pp 1161ndash11702010

[13] Q Zhu Y Dou and J Sarkis ldquoA portfolio-based analysis forgreen supplier management using the analytical network pro-cessrdquo Supply Chain Management vol 15 no 4 pp 306ndash3192010

[14] G Buyukozkan ldquoAn integrated fuzzy multi-criteria groupdecision-making approach for green supplier evaluationrdquo Inter-national Journal of Production Research vol 50 no 11 pp 2892ndash2909 2012

[15] G Buyukozkan and G Cifci ldquoA novel hybrid MCDM approachbased on fuzzy DEMATEL fuzzy ANP and fuzzy TOPSIS toevaluate green suppliersrdquo Expert Systems with Applications vol39 no 3 pp 3000ndash3011 2012

[16] C H Hsu F KWang and G H Tzeng ldquoThe best vendor selec-tion for conducting the recycled material based on a hybridMCDM model combining DANP with VIKORrdquo ResourcesConservation and Recycling vol 66 pp 95ndash111 2012

[17] K Govindan R Khodaverdi and A Jafarian ldquoA fuzzy multicriteria approach for measuring sustainability performance ofa supplier based on triple bottom line approachrdquo Journal ofCleaner Production vol 47 pp 345ndash354 2013

[18] L Shen L Olfat K Govindan R Khodaverdi and A DiabatldquoA fuzzy multi criteria approach for evaluating green supplierrsquosperformance in green supply chain with linguistic preferencesrdquoResources Conservation and Recycling vol 74 pp 170ndash179 2013

[19] M Yazdani ldquoAn integrated MCDM approach to green supplierselectionrdquo International Journal of Industrial Engineering Com-putations vol 5 no 3 pp 443ndash458 2014

[20] T Chen and J Freeman ldquoUsing AHP-Entropy weight and TOP-SIS methodology in green supplier selectionrdquo in Proceedings ofthe European Operations Management Association Conference(EurOMA rsquo14) Palermo Italy June 2014

[21] D Kannan A B L D S Jabbour andC J C Jabbour ldquoSelectinggreen suppliers based on GSCM practices using fuzzy TOPSISapplied to a Brazilian electronics companyrdquo European Journal ofOperational Research vol 233 no 2 pp 432ndash447 2014

[22] H Zhao and S Guo ldquoSelecting green supplier of thermalpower equipment by using a Hybrid MCDM Method forsustainabilityrdquo Sustainability vol 6 no 1 pp 217ndash235 2014

[23] J Figueira S Greco and M EhrgottMultiple Criteria DecisionAnalysis State of the Art Surveys Springer Science BostonMass USA 2005

[24] B Roy ldquoThe outranking approach and the foundations ofELECTREmethodsrdquoTheory and Decision vol 31 no 1 pp 49ndash73 1991

[25] J Hokkanen and P Salminen ldquoChoosing a solid waste man-agement systemusingmulticriteria decision analysisrdquoEuropeanJournal of Operational Research vol 98 no 1 pp 19ndash36 1997

[26] R J Lin ldquoUsing fuzzy DEMATEL to evaluate the green supplychain management practicesrdquo Journal of Cleaner Productionvol 40 pp 32ndash39 2013

[27] M L Tseng and A S F Chiu ldquoEvaluating firmrsquos green supplychain management in linguistic preferencesrdquo Journal of CleanerProduction vol 40 pp 22ndash31 2013

[28] G Buyukozkan and G Cifci ldquoA novel fuzzy mulati -criteriadecision framework for sustainable supplier selection withincomplete informationrdquo Computers in Industry vol 62 no 2pp 164ndash174 2011

[29] M T Escobar and J M Moreno-Jimenez ldquoAggregation of indi-vidual preference structures in AHP-group decision makingrdquoGroup Decision andNegotiation vol 16 no 4 pp 287ndash301 2007

[30] V Tsyganok ldquoInvestigation of the aggregation effectiveness ofexpert estimates obtained by the pairwise comparisonmethodrdquoMathematical and ComputerModelling vol 52 no 3-4 pp 538ndash544 2010

[31] J J Bernardo and J M Blin ldquoA programming model ofconsumer choice among multi-attributed brandsrdquo Journal ofConsumer Research vol 4 no 2 pp 111ndash118 1977

[32] P K Humphreys Y K Wong and F T S Chan ldquoIntegratingenvironmental criteria into the supplier selection processrdquoJournal of Materials Processing Technology vol 138 no 1ndash3 pp349ndash356 2003

[33] C Bai and J Sarkis ldquoIntegrating sustainability into supplierselection with grey system and rough set methodologiesrdquoInternational Journal of Production Economics vol 124 no 1 pp252ndash264 2010

[34] X Fu Q Zhu and J Sarkis ldquoEvaluating green supplier devel-opment programs at a telecommunications systems providerrdquoInternational Journal of Production Economics vol 140 no 1pp 357ndash367 2012

[35] A Amindoust S Ahmed A Saghafinia and A BahreininejadldquoSustainable supplier selection a ranking model based on fuzzyinference systemrdquoApplied Soft Computing Journal vol 12 no 6pp 1668ndash1677 2012

[36] K Shaw R Shankar S S Yadav and L S Thakur ldquoSupplierselection using fuzzy AHP and fuzzy multi-objective linearprogramming for developing low carbon supply chainrdquo ExpertSystems with Applications vol 39 no 9 pp 8182ndash8192 2012

Mathematical Problems in Engineering 13

[37] A Awasthi S S Chauhan and S K Goyal ldquoA fuzzy multi-criteria approach for evaluating environmental performance ofsuppliersrdquo International Journal of Production Economics vol126 no 2 pp 370ndash378 2010

[38] T Y Chiou H K Chan F Lettice and S H Chung ldquoThe influ-ence of greening the suppliers and green innovation on envi-ronmental performance and competitive advantage in TaiwanrdquoTransportation Research Part E Logistics and TransportationReview vol 47 no 6 pp 822ndash836 2011

[39] T L Saaty ldquoA scaling method for priorities in hierarchicalstructuresrdquo Journal of Mathematical Psychology vol 15 no 3pp 234ndash281 1977

[40] T L Saaty The Analytic Hierarchy Process McGraw-Hill NewYork NY USA 1980

[41] CMacharis J Springael K De Brucker andA Verbeke ldquoPRO-METHEE and AHP the design of operational synergies inmulticriteria analysis Strengthening PROMETHEE with ideasof AHPrdquo European Journal of Operational Research vol 153 no2 pp 307ndash317 2004

[42] M Zeleny Multiple Criteria Decision Making McGraw-HillNew York NY USA 1982

[43] C L Hwang and K Yoon Multiple Attribute Decision MakingMethods and Applications A State-of-the-Art Survey SpringerNew York NY USA 1981

[44] B Roy ldquoELECTRE III un algorithme de classements fondesur une repre sentation floue des preferences en preferences enpresence de criteres multiplesrdquo Cahiers du Centre drsquoEtudes deRecherche Operationnelle vol 20 no 1 pp 3ndash24 1978

[45] P Vincke Multiple Criteria Decision-Aid John Wiley amp SonsChichester UK 1992

[46] M Rogers and M Bruen ldquoUsing ELECTRE III to chooseroute for Dublin Port motorwayrdquo Journal of TransportationEngineering vol 126 no 4 pp 313ndash323 2000

[47] M Rogers and M Bruen ELECTRE and Decision SupportMethods and Applications in Engineering and InfrastructureInvestment Kluwer Academic Publishers Boston Mass USA2000

[48] V Belton and T J Stewart Multiple Criteria Decision AnalysisAn Integrated Approach Kluwer Academic Publishers BostonMass USA 2002

[49] B Chang C W Chang and C H Wu ldquoFuzzy DEMATELmethod for developing supplier selection criteriardquo Expert Sys-tems with Applications vol 38 no 3 pp 1850ndash1858 2011

Submit your manuscripts athttpwwwhindawicom

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

MathematicsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Mathematical Problems in Engineering

Hindawi Publishing Corporationhttpwwwhindawicom

Differential EquationsInternational Journal of

Volume 2014

Applied MathematicsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Probability and StatisticsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of

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Mathematical PhysicsAdvances in

Complex AnalysisJournal of

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OptimizationJournal of

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CombinatoricsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of

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Operations ResearchAdvances in

Journal of

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Function Spaces

Abstract and Applied AnalysisHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of Mathematics and Mathematical Sciences

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Discrete Dynamics in Nature and Society

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Volume 2014 Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Stochastic AnalysisInternational Journal of

8 Mathematical Problems in Engineering

Table 4 Compromised weights of each criterion for DMs

Dept MM RD GPM SQMCriteria 119863

11198632

1198633

1198634

1198635

1198636

1198637

1198638

1198639

11986310

11986311

11986312

1198921

0016 0230 0011 0176 0367 0008 0109 0121 0135 0007 0123 00271198922

0016 0027 0042 0441 0055 0008 0035 0127 0142 0051 0322 01521198923

0037 0048 0013 0077 0170 0038 0046 0057 0034 0017 0035 00501198924

0007 0007 0002 0025 0168 0007 0009 0056 0007 0002 0012 00071198925

0399 0006 0051 0014 0008 0128 0085 0090 0004 0020 0010 00181198926

0093 0001 0060 0005 0003 0030 0033 0022 0001 0005 0002 00211198927

0049 0058 0065 0079 0067 0162 0229 0068 0032 0209 0034 00101198928

0047 0012 0062 0011 0022 0031 0055 0066 0010 0040 0011 00031198929

0079 0012 0060 0007 0018 0087 0084 0095 0012 0009 0019 000311989210

0016 0010 0048 0003 0012 0069 0028 0019 0021 0027 0005 000211989211

0005 0002 0005 0001 0003 0010 0013 0009 0002 0033 0012 000111989212

0134 0400 0350 0018 0061 0149 0056 0183 0211 0010 0062 040111989213

0062 0112 0081 0126 0029 0209 0156 0052 0295 0104 0262 018711989214

0030 0064 0133 0015 0005 0017 0031 0018 0071 008 0023 010011989215

0010 0013 0018 0003 0013 0049 0030 0017 0023 0385 0067 0019

Table 5 Outranking degrees of each DM

1198631

1198632

1198633

1198861

1198862

1198863

1198864

1198861

1198862

1198863

1198864

1198861

1198862

1198863

1198864

1198861

0 0588 0743 0867 1198861

0 0771 0578 0868 1198861

0 0688 0547 08441198862

0521 0 0598 0633 1198862

0310 0 0374 0425 1198862

0409 0 0442 05291198863

0493 0538 0 0762 1198863

0715 0779 0 0803 1198863

0669 0719 0 07631198864

0591 0619 0479 0 1198864

0783 0787 0476 0 1198864

0771 0735 0488 01198634

1198635

1198636

a1 a2 a3 a4 a1 a2 a3 a4 a1 a2 a3 a41198861

0 0785 0839 0862 1198861

0 0790 0804 0858 1198861

0 0635 0685 07701198862

0306 0 0413 0440 1198862

0378 0 0444 0463 1198862

0477 0 0625 06441198863

0673 0750 0 0780 1198863

0650 0678 0 0703 1198863

0531 0632 0 07111198864

0740 0782 0694 0 1198864

0745 0832 0643 0 1198864

0665 0674 0565 01198637

1198638

1198639

1198861

1198862

1198863

1198864

1198861

1198862

1198863

1198864

1198861

1198862

1198863

1198864

1198861

0 0642 0762 0767 1198861

0 0736 0706 0853 1198861

0 0762 0664 08641198862

0473 0 0602 0634 1198862

0404 0 0492 0518 1198862

0320 0 0465 05201198863

0534 0604 0 0667 1198863

0608 0646 0 0712 1198863

0634 0788 0 08091198864

0687 0663 0628 0 1198864

0734 0777 0588 0 1198864

0702 0735 0532 011986310

11986311

11986312

1198861

1198862

1198863

1198864

1198861

1198862

1198863

1198864

1198861

1198862

1198863

1198864

1198861

0 0471 0557 0585 1198861

0 0762 0754 0841 1198861

0 0765 0562 08861198862

0607 0 0671 0661 1198862

0326 0 0491 0509 1198862

0313 0 0387 04551198863

0660 0704 0 0663 1198863

0630 0776 0 0791 1198863

0697 0798 0 08261198864

0780 0713 0706 0 1198864

0704 0761 0644 0 1198864

0749 0762 0453 0

that alternative 1 is superior to alternative 3 However acharacteristic of the ELECTRE III method is that a penaltyis set if the alternative performs the worst for a criterionThus we can use (9) to calculate the rejecting degree dueto the penalty and the overall outranking degree can beobtained by (10) as shown in Table 6 As another examplefor the 119863

6manager the outranking degree of alternative 3

to alternative 2 is 0632 but the overall outranking degreeof alternative 3 to alternative 2 is 0000 This means thatalternative 3 performs too poorly to be accepted based onsome criteria The results presented in Table 7 show thatalternative 3 performs too poorly on quality control (119892

7) and

delivery (1198929) The rejecting degrees for quality control (119892

7)

and delivery (1198929) are 1000 and 0778 respectively which

Mathematical Problems in Engineering 9

Table 6 Overall outranking degree of each DM

1198631

1198632

1198633

1198861

1198862

1198863

1198864

1198861

1198862

1198863

1198864

1198861

1198862

1198863

1198864

1198861

0 0588 0743 0867 1198861

0 0771 0564 0868 1198861

0 0688 0246 08441198862

0342 0 0331 0633 1198862

0026 0 0 0148 1198862

0115 0 0 04151198863

0273 0334 0 0762 1198863

0715 0779 0 0803 1198863

0669 0719 0 07631198864

0563 0 0384 0 1198864

0783 0787 0361 0 1198864

0771 0735 0207 01198634

1198635

1198636

1198861

1198862

1198863

1198864

1198861

1198862

1198863

1198864

1198861

1198862

1198863

1198864

1198861

0 0785 0839 0862 1198861

0 0790 0804 0858 1198861

0 0245 0483 07461198862

0 0 0 0 1198862

0 0 0 0037 1198862

0090 0 0 06441198863

0673 0750 0 0780 1198863

0413 0468 0 0526 1198863

0091 0 0 05461198864

0633 0782 0694 0 1198864

0745 0832 0643 0 1198864

0 0459 0541 01198637

1198638

1198639

1198861

1198862

1198863

1198864

1198861

1198862

1198863

1198864

1198861

1198862

1198863

1198864

1198861

0 0413 0762 0767 1198861

0 0736 0706 0853 1198861

0 0762 0664 08641198862

0054 0 0336 0634 1198862

0037 0 0 0163 1198862

0005 0 0 01661198863

0094 0 0 0445 1198863

0344 0406 0 0550 1198863

0372 0788 0 08091198864

0 0437 0628 0 1198864

0734 0774 0588 0 1198864

0 0735 0474 011986310

11986311

11986312

1198861

1198862

1198863

1198864

1198861

1198862

1198863

1198864

1198861

1198862

1198863

1198864

1198861

0 0072 0123 0 1198861

0 0762 0754 0841 1198861

0 0765 0509 08861198862

0607 0 0671 0661 1198862

0 0 0 0 1198862

0034 0 0 02611198863

0660 0529 0 0663 1198863

0365 0776 0 0791 1198863

0697 0798 0 08261198864

0780 0713 0706 0 1198864

0 0761 0644 0 1198864

0664 0762 0315 0

Table 7 Rejecting degree for alternative 3 to alternative 2 from 1198896manager

1198921

1198922

1198923

1198924

1198925

1198926

1198927

1198928

1198929

11989210

11989211

11989212

11989213

11989214

11989215

Reject degree 0000 0000 0000 0583 0407 0000 1000 0288 0778 0000 0000 0000 0000 0000 0000

are obviously larger than the outranking degree of 0632 Inthe next section we further discuss which criteria shouldbe improved immediately Accordingly we can establish thedescending and ascending distillation for each DM and theranking results are shown in Table 8

Finally LAM is applied to establish linear programmingfor integrating the opinions of each DM According tothe decision-making power of each department the overallranking matrix is shown as follows

Π =[[[

[

06850 01950 00000 01200

00400 01350 00850 07400

02350 04700 02825 00125

00400 02000 06325 01275

]]]

]

(12)

Therefore we can formulate a linear programming modelusing (11) and obtain the solution which indicates that theoverall ranking result is 119886

1≻ 1198863≻ 1198864≻ 1198862The best supplier is

supplier 1 and the subsidiary company of Y-TECH is secondonly to supplier 1 Supplier 4 is ranked third and supplier 2performs the worst in this evaluation

43 Discussion and Implications In this study we aim topropose a hybridMCGDMmethod to evaluate and assist sup-pliers At the first stage we use the AHP method to measurethe subjective weights of the criteria for each DM Based onthe results of the AHP method it is obvious that most ofthe managers value the importance of current capability andRampD capability and this suggests that Y-TECH places a greatemphasis on technology development In the FPD industrydue to the rapid development of new technology many well-known enterprises regularly release new products to marketevery year A challenge for Y-TECH includes the develop-ment of other technologies such as organic light-emittingdisplays (OLED) For example the OLED panel led to thereduction of the demand for small and medium TFT-LCDdisplays in the global market in 2010 Therefore to enhancecompetitiveness TFT-LCD manufactures connecting withthe suppliers in the entire supply chain should continuouslydevelop new technology Quality control is an importantcriterion secondary only to technological capability At theearly stages of applying a new technology to a new productthe yield rate of the new product is low Consequently it isimportant not only to develop the new technology but alsoto maintain a highly qualified product Currently because

10 Mathematical Problems in Engineering

Table 8 Initial evaluation of individual DM

Dept MM RD GPM SQMRanking 119889

11198892

1198893

1198894

1198895

1198896

1198897

1198898

1198899

11988910

11988911

11988912

1st 1198861

1198861 1198863

1198863

1198861

1198861

1198861

1198861

1198861

1198861

1198862 1198863 1198864

1198861

1198861 1198863

2nd 1198862 1198863

1198861

1198863

1198864

1198862

1198862

1198864

1198863

1198863 1198864

3rd 1198864

1198864

1198864

1198863

1198863

1198863 1198864

1198863

1198864

1198864

4th 1198864

1198862

1198862

1198862

1198862

1198864

1198862

1198862

1198861

1198862

1198862

Table 9 The worst performing criteria of each alternative

1198861

1198862

1198863

1198864

1198631

mdash mdash mdash 1198925

1198632

mdash 11989212

mdash mdash1198633

mdash 11989212

mdash mdash1198634

mdash 1198922

mdash mdash1198635

mdash 1198921

mdash mdash1198636

mdash 11989212

g7 11989213

1198637

mdash mdash g7 11989213

1198638

mdash 11989212

mdash mdash1198639

mdash 11989212

mdash 11989213

11986310

g15 mdash mdash mdash11986311

mdash 1198922

mdash 11989213

11986312

mdash 11989212

mdash mdash

GPM department processes less decision-making power forgreen supplier selection it is obvious that environmentalfactors are not significantly considered in the evaluationprocedure with the exception that RampD department shoulddesign new products and the related materials must be listedfor MM department Then MM department can place anorder and ask the suppliers to follow the list SQMdepartmentis concerned with technological capability as well as infor-mation share Vertical integration is an important enterprisepolicy for the TFT-LCD industry and strategic purchase isa method for the implementation of vertical integration Asmentioned before the key components of TFT-LCD panelsthat are produced in-house have gradually increased year byyear so information share is also an important criterion forcultivating the core capability of enterprise and establishinga competitive advantage Furthermore all of the criteriaplay important roles in the evaluation procedure for greensupplier selection If the proposed hybrid MCGDM methodonly adopts the AHP method it will most likely lead to theoversubjective weighting of the criteria Therefore we applythe entropy method to consider the weights of the criteriasimultaneously

ELECTRE III is applied to evaluate the suppliers Duringthe evaluation procedure we compare the outranking degreeand the overall outranking degree and then decide whichcriterion should be improved immediately for each alterna-tive as shown in Table 9 If the overall outranking degreeof a criterion decreases to zero (comparing to outrankingdegree) this criterion should be improved immediately Forthe best supplier supplier 1 (119886

1) the 119863

10manager suggests

that alternative 1 should strengthen communication with Y-TECH by taking actions such as improving cooperation forthe development of new products and instant market infor-mation share (119892

15) In this case study one of the most impor-

tant aspects is how to provide assistance to the subsidiarycompany (119886

3) of Y-TECH Table 9 indicates that if supplier

3 improves quality control (1198927) for polarizer production its

performance will be improved significantly Generally it iscontended that managers should be concerned about criteriarelated to their job description Due to limited enterpriseresources the order of executing enterprise improvementis also an important issue Following the useful proposedmethod the discovery of the criteria that need to be improvedwill become easy

Different from many previous studies this study doesnot integrate the weights of the criteria at the first stageIn fact green supplier selection is a group decision-makingproblem and integrating the weights would also lead tothe loss of information Therefore we further consider theranking results of each department as shown in Table 10Although these four polarizermanufacturers are the suppliersof Y-TECH there are some differences among the rankingresults of each department especially for suppliers 2 and 4 Asmentioned before RampD department placed great emphasison the ldquogreenrdquo criteria and supplier 4rsquos good performance onthese criteria is the reason for the different ranking resultsConsequently in this study we proposed a novel method andviewpoint for green supplier selection and we suggested thatthe weights of the criteria should not be integrated at thefirst stage because further discussions and communicationare necessary to include the different opinions from eachdepartment Additionally we compare the results of theproposed method with the ones implemented by Y-TECHthat is simple additive method in Table 10 Both results showthat supplier 1 has the best performance and is superior tosupplier 3 Supplier 4 is in the third place while supplier2 is the worst Furthermore within the extensive literatureon green supplier selection methods the TOPSIS methodwas widely applied to evaluate green suppliersrsquo performance[15 17ndash22] Thus we further compare the TOPSIS methodwith the proposedmethod and the comparison is establishedon the basis of AHP-entropy criteria weights In the TOPSISmethod with AHP-entropy weights supplier 3 is in the firstplace and superior to supplier 1 and supplier 4 and supplier2 have the worst performance in this evaluation Althoughthe ranking results among the proposed method and othertwo methods are similar the emphasis of this study is onhow to improve the suppliersrsquo performance in the TFT-LCD industry More importantly the results of the proposed

Mathematical Problems in Engineering 11

Table 10 Comparison of ranking results for the proposed method with other methods

Ranking MM RD GPM SQM The proposed method Current methodlowast AHP-entropy TOPSIS1st 119886

31198861

1198861

1198861

1198861

1198861

1198863

2nd 1198861

1198864

1198862

1198863

1198863

1198863

1198861

3rd 1198864

1198863

1198863

1198864

1198864

1198864

1198864

4th 1198862

1198862

1198864

1198862

1198862

1198862

1198862

lowast

The current evaluation method in Y-TECH is simple additive method

method show that each department has its different opinionsand viewpoints of improvement Each department of theTFT-LCDmanufacturers can assist their suppliers within thecontext of business perspectives

5 Conclusion

In this study we propose a hybrid MCGDM method forgreen supplier selection for Y-TECH Our method focuseson not only the selection or ranking but also the providenceof assistance to suppliers to strengthen their competitivenessThis method also emphasizes continuous improvement Dueto the challenges and difficulties the TFT-LCD industriesworldwide face we intend to establish a systematic selectionprocedure for TFT-LCD manufacturers We developed adecision framework for green supplier selection based onliteratures and the supplier audit forms from Y-TECH Weconsidered AHP and the entropy method to measure thecompromised weights of the criteria that involve subjectiveand objective opinions simultaneously After that the ELEC-TRE III method provides polarizer manufacturersrsquo rankingresults for executive managers in addition to improvementreports The acquisition of key components used for in-house production of TFT-LCD panels has gradually becomea strategy for cost reduction and the enhancement of corecapability Thus TFT-LCD manufacturers should strengthentheir cooperation with their suppliers

Based on the results of the ELECTRE III method wecan compare the differences between the outranking degreeand the overall outranking degree Then we further considerwhich criteria should be improved immediately especially forthe suppliers of strategic purchases The results of this studyprovide an important reference for subsidiary companiesthat are seeking to be primary polarizer suppliers Finallydiffering from many of the previous studies we do notintegrate the weights of the criteria with regard to a singleDM in this study In other worlds we integrate the rankingresults of eachDM instead of theweights of the criteria In theproposed evaluation procedure LAM places more emphasison the discussion and communication among a group ofDMs which is consistent with the practical case

In summary the result of this study can be regardedas a problem-solving process for green supplier selectionRegardless of the proposed method or analytic procedurewe aim at helping TFT-LCD manufacturers to establish asystematic and useful evaluation procedure via the currentstudy Because all of the supplier audit forms are in Microsoft

Excel 2010 spreadsheets the proposedmethod is easy to applyusing the same file Executive managers can consider theperformance of all suppliers and refer to the results of theproposed method Three main contributions are made inthis study First depending on the literatures and supplieraudit forms we develop a decision framework for the TFT-LCD industry Second we propose the useful systematicand flexible hybrid MCGDMmethod (based on the businessprocess) and improve the current evaluation procedureThird we place more emphasis on improving suppliers toenhance the competitiveness of entire supply chain especiallythe subsidiary company In practice green supplier selectionis evaluated by each department separately and all criteria areregarded as independent In recent years increasing attentionhas been given to the interdependent relationships amongthe criteria [15 16 26 49] Therefore future studies arenecessary to clarify the interrelations among the criteriaWe hope that more detailed information influencing therelationships between the criteria can be provided in futureresearch Consequently it would bemore efficient to improvethe supplier selection process for the entire supply chain inthe future work

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper

Acknowledgments

This research was partially supported by the National ScienceCouncil of the Republic of China (Grant NSC 102-2221-E-007-084) The authors would like to thank the editorand anonymous referees for their valuable comments andsuggestions

References

[1] Taiwan Stock Exchange ldquoIndustry analysis the flat paneldisplay Industryrdquo February 2013 httpwwwtwsecomtwchproductspublicationessayphp

[2] S Hung ldquoCompetitive strategies for Taiwanrsquos thin film tran-sistor-liquid crystal display (TFT-LCD) industryrdquoTechnology inSociety vol 28 no 3 pp 349ndash361 2006

[3] W Ho X Xu and P K Dey ldquoMulti-criteria decision makingapproaches for supplier evaluation and selection a literaturereviewrdquo European Journal of Operational Research vol 202 no1 pp 16ndash24 2010

12 Mathematical Problems in Engineering

[4] J Chai J N K Liu and E W T Ngai ldquoApplication of decision-making techniques in supplier selection a systematic review ofliteraturerdquo Expert Systems with Applications vol 40 no 10 pp3872ndash3885 2013

[5] K Govindan S Rajendran J Sarkis and P Murugesan ldquoMulticriteria decision making approaches for green supplier eval-uation and selection a literature reviewrdquo Journal of CleanerProduction 2013

[6] M Herva and E Roca ldquoReview of combined approaches andmulti-criteria analysis for corporate environmental evaluationrdquoJournal of Cleaner Production vol 39 pp 355ndash371 2013

[7] S Seuring ldquoA review of modeling approaches for sustainablesupply chain managementrdquo Decision Support Systems vol 54no 4 pp 1513ndash1520 2013

[8] M Igarashi L De Boer and A M Fet ldquoWhat is required forgreener supplier selection A literature review and conceptualmodel developmentrdquo Journal of Purchasing and SupplyManage-ment vol 19 no 4 pp 247ndash263 2013

[9] A Genovese S C Koh G Bruno and E Esposito ldquoGreenersupplier selection state of the art and some empirical evidencerdquoInternational Journal of Production Research vol 51 no 10 pp2868ndash2886 2013

[10] A H I Lee H Y Kang C F Hsu and H C Hung ldquoA greensupplier selectionmodel for high-tech industryrdquo Expert Systemswith Applications vol 36 no 4 pp 7917ndash7927 2009

[11] G Tuzkaya A Ozgen D Ozgen and U R Tuzkaya ldquoEnvi-ronmental performance evaluation of suppliers a hybrid fuzzymulti-criteria decision approachrdquo International Journal of Envi-ronmental Science and Technology vol 6 no 3 pp 477ndash4902009

[12] R J Kuo Y C Wang and F C Tien ldquoIntegration of artificialneural network and MADA methods for green supplier selec-tionrdquo Journal of Cleaner Production vol 18 no 12 pp 1161ndash11702010

[13] Q Zhu Y Dou and J Sarkis ldquoA portfolio-based analysis forgreen supplier management using the analytical network pro-cessrdquo Supply Chain Management vol 15 no 4 pp 306ndash3192010

[14] G Buyukozkan ldquoAn integrated fuzzy multi-criteria groupdecision-making approach for green supplier evaluationrdquo Inter-national Journal of Production Research vol 50 no 11 pp 2892ndash2909 2012

[15] G Buyukozkan and G Cifci ldquoA novel hybrid MCDM approachbased on fuzzy DEMATEL fuzzy ANP and fuzzy TOPSIS toevaluate green suppliersrdquo Expert Systems with Applications vol39 no 3 pp 3000ndash3011 2012

[16] C H Hsu F KWang and G H Tzeng ldquoThe best vendor selec-tion for conducting the recycled material based on a hybridMCDM model combining DANP with VIKORrdquo ResourcesConservation and Recycling vol 66 pp 95ndash111 2012

[17] K Govindan R Khodaverdi and A Jafarian ldquoA fuzzy multicriteria approach for measuring sustainability performance ofa supplier based on triple bottom line approachrdquo Journal ofCleaner Production vol 47 pp 345ndash354 2013

[18] L Shen L Olfat K Govindan R Khodaverdi and A DiabatldquoA fuzzy multi criteria approach for evaluating green supplierrsquosperformance in green supply chain with linguistic preferencesrdquoResources Conservation and Recycling vol 74 pp 170ndash179 2013

[19] M Yazdani ldquoAn integrated MCDM approach to green supplierselectionrdquo International Journal of Industrial Engineering Com-putations vol 5 no 3 pp 443ndash458 2014

[20] T Chen and J Freeman ldquoUsing AHP-Entropy weight and TOP-SIS methodology in green supplier selectionrdquo in Proceedings ofthe European Operations Management Association Conference(EurOMA rsquo14) Palermo Italy June 2014

[21] D Kannan A B L D S Jabbour andC J C Jabbour ldquoSelectinggreen suppliers based on GSCM practices using fuzzy TOPSISapplied to a Brazilian electronics companyrdquo European Journal ofOperational Research vol 233 no 2 pp 432ndash447 2014

[22] H Zhao and S Guo ldquoSelecting green supplier of thermalpower equipment by using a Hybrid MCDM Method forsustainabilityrdquo Sustainability vol 6 no 1 pp 217ndash235 2014

[23] J Figueira S Greco and M EhrgottMultiple Criteria DecisionAnalysis State of the Art Surveys Springer Science BostonMass USA 2005

[24] B Roy ldquoThe outranking approach and the foundations ofELECTREmethodsrdquoTheory and Decision vol 31 no 1 pp 49ndash73 1991

[25] J Hokkanen and P Salminen ldquoChoosing a solid waste man-agement systemusingmulticriteria decision analysisrdquoEuropeanJournal of Operational Research vol 98 no 1 pp 19ndash36 1997

[26] R J Lin ldquoUsing fuzzy DEMATEL to evaluate the green supplychain management practicesrdquo Journal of Cleaner Productionvol 40 pp 32ndash39 2013

[27] M L Tseng and A S F Chiu ldquoEvaluating firmrsquos green supplychain management in linguistic preferencesrdquo Journal of CleanerProduction vol 40 pp 22ndash31 2013

[28] G Buyukozkan and G Cifci ldquoA novel fuzzy mulati -criteriadecision framework for sustainable supplier selection withincomplete informationrdquo Computers in Industry vol 62 no 2pp 164ndash174 2011

[29] M T Escobar and J M Moreno-Jimenez ldquoAggregation of indi-vidual preference structures in AHP-group decision makingrdquoGroup Decision andNegotiation vol 16 no 4 pp 287ndash301 2007

[30] V Tsyganok ldquoInvestigation of the aggregation effectiveness ofexpert estimates obtained by the pairwise comparisonmethodrdquoMathematical and ComputerModelling vol 52 no 3-4 pp 538ndash544 2010

[31] J J Bernardo and J M Blin ldquoA programming model ofconsumer choice among multi-attributed brandsrdquo Journal ofConsumer Research vol 4 no 2 pp 111ndash118 1977

[32] P K Humphreys Y K Wong and F T S Chan ldquoIntegratingenvironmental criteria into the supplier selection processrdquoJournal of Materials Processing Technology vol 138 no 1ndash3 pp349ndash356 2003

[33] C Bai and J Sarkis ldquoIntegrating sustainability into supplierselection with grey system and rough set methodologiesrdquoInternational Journal of Production Economics vol 124 no 1 pp252ndash264 2010

[34] X Fu Q Zhu and J Sarkis ldquoEvaluating green supplier devel-opment programs at a telecommunications systems providerrdquoInternational Journal of Production Economics vol 140 no 1pp 357ndash367 2012

[35] A Amindoust S Ahmed A Saghafinia and A BahreininejadldquoSustainable supplier selection a ranking model based on fuzzyinference systemrdquoApplied Soft Computing Journal vol 12 no 6pp 1668ndash1677 2012

[36] K Shaw R Shankar S S Yadav and L S Thakur ldquoSupplierselection using fuzzy AHP and fuzzy multi-objective linearprogramming for developing low carbon supply chainrdquo ExpertSystems with Applications vol 39 no 9 pp 8182ndash8192 2012

Mathematical Problems in Engineering 13

[37] A Awasthi S S Chauhan and S K Goyal ldquoA fuzzy multi-criteria approach for evaluating environmental performance ofsuppliersrdquo International Journal of Production Economics vol126 no 2 pp 370ndash378 2010

[38] T Y Chiou H K Chan F Lettice and S H Chung ldquoThe influ-ence of greening the suppliers and green innovation on envi-ronmental performance and competitive advantage in TaiwanrdquoTransportation Research Part E Logistics and TransportationReview vol 47 no 6 pp 822ndash836 2011

[39] T L Saaty ldquoA scaling method for priorities in hierarchicalstructuresrdquo Journal of Mathematical Psychology vol 15 no 3pp 234ndash281 1977

[40] T L Saaty The Analytic Hierarchy Process McGraw-Hill NewYork NY USA 1980

[41] CMacharis J Springael K De Brucker andA Verbeke ldquoPRO-METHEE and AHP the design of operational synergies inmulticriteria analysis Strengthening PROMETHEE with ideasof AHPrdquo European Journal of Operational Research vol 153 no2 pp 307ndash317 2004

[42] M Zeleny Multiple Criteria Decision Making McGraw-HillNew York NY USA 1982

[43] C L Hwang and K Yoon Multiple Attribute Decision MakingMethods and Applications A State-of-the-Art Survey SpringerNew York NY USA 1981

[44] B Roy ldquoELECTRE III un algorithme de classements fondesur une repre sentation floue des preferences en preferences enpresence de criteres multiplesrdquo Cahiers du Centre drsquoEtudes deRecherche Operationnelle vol 20 no 1 pp 3ndash24 1978

[45] P Vincke Multiple Criteria Decision-Aid John Wiley amp SonsChichester UK 1992

[46] M Rogers and M Bruen ldquoUsing ELECTRE III to chooseroute for Dublin Port motorwayrdquo Journal of TransportationEngineering vol 126 no 4 pp 313ndash323 2000

[47] M Rogers and M Bruen ELECTRE and Decision SupportMethods and Applications in Engineering and InfrastructureInvestment Kluwer Academic Publishers Boston Mass USA2000

[48] V Belton and T J Stewart Multiple Criteria Decision AnalysisAn Integrated Approach Kluwer Academic Publishers BostonMass USA 2002

[49] B Chang C W Chang and C H Wu ldquoFuzzy DEMATELmethod for developing supplier selection criteriardquo Expert Sys-tems with Applications vol 38 no 3 pp 1850ndash1858 2011

Submit your manuscripts athttpwwwhindawicom

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

MathematicsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Mathematical Problems in Engineering

Hindawi Publishing Corporationhttpwwwhindawicom

Differential EquationsInternational Journal of

Volume 2014

Applied MathematicsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Probability and StatisticsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Mathematical PhysicsAdvances in

Complex AnalysisJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

OptimizationJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

CombinatoricsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Operations ResearchAdvances in

Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Function Spaces

Abstract and Applied AnalysisHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of Mathematics and Mathematical Sciences

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Algebra

Discrete Dynamics in Nature and Society

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Decision SciencesAdvances in

Discrete MathematicsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom

Volume 2014 Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Stochastic AnalysisInternational Journal of

Mathematical Problems in Engineering 9

Table 6 Overall outranking degree of each DM

1198631

1198632

1198633

1198861

1198862

1198863

1198864

1198861

1198862

1198863

1198864

1198861

1198862

1198863

1198864

1198861

0 0588 0743 0867 1198861

0 0771 0564 0868 1198861

0 0688 0246 08441198862

0342 0 0331 0633 1198862

0026 0 0 0148 1198862

0115 0 0 04151198863

0273 0334 0 0762 1198863

0715 0779 0 0803 1198863

0669 0719 0 07631198864

0563 0 0384 0 1198864

0783 0787 0361 0 1198864

0771 0735 0207 01198634

1198635

1198636

1198861

1198862

1198863

1198864

1198861

1198862

1198863

1198864

1198861

1198862

1198863

1198864

1198861

0 0785 0839 0862 1198861

0 0790 0804 0858 1198861

0 0245 0483 07461198862

0 0 0 0 1198862

0 0 0 0037 1198862

0090 0 0 06441198863

0673 0750 0 0780 1198863

0413 0468 0 0526 1198863

0091 0 0 05461198864

0633 0782 0694 0 1198864

0745 0832 0643 0 1198864

0 0459 0541 01198637

1198638

1198639

1198861

1198862

1198863

1198864

1198861

1198862

1198863

1198864

1198861

1198862

1198863

1198864

1198861

0 0413 0762 0767 1198861

0 0736 0706 0853 1198861

0 0762 0664 08641198862

0054 0 0336 0634 1198862

0037 0 0 0163 1198862

0005 0 0 01661198863

0094 0 0 0445 1198863

0344 0406 0 0550 1198863

0372 0788 0 08091198864

0 0437 0628 0 1198864

0734 0774 0588 0 1198864

0 0735 0474 011986310

11986311

11986312

1198861

1198862

1198863

1198864

1198861

1198862

1198863

1198864

1198861

1198862

1198863

1198864

1198861

0 0072 0123 0 1198861

0 0762 0754 0841 1198861

0 0765 0509 08861198862

0607 0 0671 0661 1198862

0 0 0 0 1198862

0034 0 0 02611198863

0660 0529 0 0663 1198863

0365 0776 0 0791 1198863

0697 0798 0 08261198864

0780 0713 0706 0 1198864

0 0761 0644 0 1198864

0664 0762 0315 0

Table 7 Rejecting degree for alternative 3 to alternative 2 from 1198896manager

1198921

1198922

1198923

1198924

1198925

1198926

1198927

1198928

1198929

11989210

11989211

11989212

11989213

11989214

11989215

Reject degree 0000 0000 0000 0583 0407 0000 1000 0288 0778 0000 0000 0000 0000 0000 0000

are obviously larger than the outranking degree of 0632 Inthe next section we further discuss which criteria shouldbe improved immediately Accordingly we can establish thedescending and ascending distillation for each DM and theranking results are shown in Table 8

Finally LAM is applied to establish linear programmingfor integrating the opinions of each DM According tothe decision-making power of each department the overallranking matrix is shown as follows

Π =[[[

[

06850 01950 00000 01200

00400 01350 00850 07400

02350 04700 02825 00125

00400 02000 06325 01275

]]]

]

(12)

Therefore we can formulate a linear programming modelusing (11) and obtain the solution which indicates that theoverall ranking result is 119886

1≻ 1198863≻ 1198864≻ 1198862The best supplier is

supplier 1 and the subsidiary company of Y-TECH is secondonly to supplier 1 Supplier 4 is ranked third and supplier 2performs the worst in this evaluation

43 Discussion and Implications In this study we aim topropose a hybridMCGDMmethod to evaluate and assist sup-pliers At the first stage we use the AHP method to measurethe subjective weights of the criteria for each DM Based onthe results of the AHP method it is obvious that most ofthe managers value the importance of current capability andRampD capability and this suggests that Y-TECH places a greatemphasis on technology development In the FPD industrydue to the rapid development of new technology many well-known enterprises regularly release new products to marketevery year A challenge for Y-TECH includes the develop-ment of other technologies such as organic light-emittingdisplays (OLED) For example the OLED panel led to thereduction of the demand for small and medium TFT-LCDdisplays in the global market in 2010 Therefore to enhancecompetitiveness TFT-LCD manufactures connecting withthe suppliers in the entire supply chain should continuouslydevelop new technology Quality control is an importantcriterion secondary only to technological capability At theearly stages of applying a new technology to a new productthe yield rate of the new product is low Consequently it isimportant not only to develop the new technology but alsoto maintain a highly qualified product Currently because

10 Mathematical Problems in Engineering

Table 8 Initial evaluation of individual DM

Dept MM RD GPM SQMRanking 119889

11198892

1198893

1198894

1198895

1198896

1198897

1198898

1198899

11988910

11988911

11988912

1st 1198861

1198861 1198863

1198863

1198861

1198861

1198861

1198861

1198861

1198861

1198862 1198863 1198864

1198861

1198861 1198863

2nd 1198862 1198863

1198861

1198863

1198864

1198862

1198862

1198864

1198863

1198863 1198864

3rd 1198864

1198864

1198864

1198863

1198863

1198863 1198864

1198863

1198864

1198864

4th 1198864

1198862

1198862

1198862

1198862

1198864

1198862

1198862

1198861

1198862

1198862

Table 9 The worst performing criteria of each alternative

1198861

1198862

1198863

1198864

1198631

mdash mdash mdash 1198925

1198632

mdash 11989212

mdash mdash1198633

mdash 11989212

mdash mdash1198634

mdash 1198922

mdash mdash1198635

mdash 1198921

mdash mdash1198636

mdash 11989212

g7 11989213

1198637

mdash mdash g7 11989213

1198638

mdash 11989212

mdash mdash1198639

mdash 11989212

mdash 11989213

11986310

g15 mdash mdash mdash11986311

mdash 1198922

mdash 11989213

11986312

mdash 11989212

mdash mdash

GPM department processes less decision-making power forgreen supplier selection it is obvious that environmentalfactors are not significantly considered in the evaluationprocedure with the exception that RampD department shoulddesign new products and the related materials must be listedfor MM department Then MM department can place anorder and ask the suppliers to follow the list SQMdepartmentis concerned with technological capability as well as infor-mation share Vertical integration is an important enterprisepolicy for the TFT-LCD industry and strategic purchase isa method for the implementation of vertical integration Asmentioned before the key components of TFT-LCD panelsthat are produced in-house have gradually increased year byyear so information share is also an important criterion forcultivating the core capability of enterprise and establishinga competitive advantage Furthermore all of the criteriaplay important roles in the evaluation procedure for greensupplier selection If the proposed hybrid MCGDM methodonly adopts the AHP method it will most likely lead to theoversubjective weighting of the criteria Therefore we applythe entropy method to consider the weights of the criteriasimultaneously

ELECTRE III is applied to evaluate the suppliers Duringthe evaluation procedure we compare the outranking degreeand the overall outranking degree and then decide whichcriterion should be improved immediately for each alterna-tive as shown in Table 9 If the overall outranking degreeof a criterion decreases to zero (comparing to outrankingdegree) this criterion should be improved immediately Forthe best supplier supplier 1 (119886

1) the 119863

10manager suggests

that alternative 1 should strengthen communication with Y-TECH by taking actions such as improving cooperation forthe development of new products and instant market infor-mation share (119892

15) In this case study one of the most impor-

tant aspects is how to provide assistance to the subsidiarycompany (119886

3) of Y-TECH Table 9 indicates that if supplier

3 improves quality control (1198927) for polarizer production its

performance will be improved significantly Generally it iscontended that managers should be concerned about criteriarelated to their job description Due to limited enterpriseresources the order of executing enterprise improvementis also an important issue Following the useful proposedmethod the discovery of the criteria that need to be improvedwill become easy

Different from many previous studies this study doesnot integrate the weights of the criteria at the first stageIn fact green supplier selection is a group decision-makingproblem and integrating the weights would also lead tothe loss of information Therefore we further consider theranking results of each department as shown in Table 10Although these four polarizermanufacturers are the suppliersof Y-TECH there are some differences among the rankingresults of each department especially for suppliers 2 and 4 Asmentioned before RampD department placed great emphasison the ldquogreenrdquo criteria and supplier 4rsquos good performance onthese criteria is the reason for the different ranking resultsConsequently in this study we proposed a novel method andviewpoint for green supplier selection and we suggested thatthe weights of the criteria should not be integrated at thefirst stage because further discussions and communicationare necessary to include the different opinions from eachdepartment Additionally we compare the results of theproposed method with the ones implemented by Y-TECHthat is simple additive method in Table 10 Both results showthat supplier 1 has the best performance and is superior tosupplier 3 Supplier 4 is in the third place while supplier2 is the worst Furthermore within the extensive literatureon green supplier selection methods the TOPSIS methodwas widely applied to evaluate green suppliersrsquo performance[15 17ndash22] Thus we further compare the TOPSIS methodwith the proposedmethod and the comparison is establishedon the basis of AHP-entropy criteria weights In the TOPSISmethod with AHP-entropy weights supplier 3 is in the firstplace and superior to supplier 1 and supplier 4 and supplier2 have the worst performance in this evaluation Althoughthe ranking results among the proposed method and othertwo methods are similar the emphasis of this study is onhow to improve the suppliersrsquo performance in the TFT-LCD industry More importantly the results of the proposed

Mathematical Problems in Engineering 11

Table 10 Comparison of ranking results for the proposed method with other methods

Ranking MM RD GPM SQM The proposed method Current methodlowast AHP-entropy TOPSIS1st 119886

31198861

1198861

1198861

1198861

1198861

1198863

2nd 1198861

1198864

1198862

1198863

1198863

1198863

1198861

3rd 1198864

1198863

1198863

1198864

1198864

1198864

1198864

4th 1198862

1198862

1198864

1198862

1198862

1198862

1198862

lowast

The current evaluation method in Y-TECH is simple additive method

method show that each department has its different opinionsand viewpoints of improvement Each department of theTFT-LCDmanufacturers can assist their suppliers within thecontext of business perspectives

5 Conclusion

In this study we propose a hybrid MCGDM method forgreen supplier selection for Y-TECH Our method focuseson not only the selection or ranking but also the providenceof assistance to suppliers to strengthen their competitivenessThis method also emphasizes continuous improvement Dueto the challenges and difficulties the TFT-LCD industriesworldwide face we intend to establish a systematic selectionprocedure for TFT-LCD manufacturers We developed adecision framework for green supplier selection based onliteratures and the supplier audit forms from Y-TECH Weconsidered AHP and the entropy method to measure thecompromised weights of the criteria that involve subjectiveand objective opinions simultaneously After that the ELEC-TRE III method provides polarizer manufacturersrsquo rankingresults for executive managers in addition to improvementreports The acquisition of key components used for in-house production of TFT-LCD panels has gradually becomea strategy for cost reduction and the enhancement of corecapability Thus TFT-LCD manufacturers should strengthentheir cooperation with their suppliers

Based on the results of the ELECTRE III method wecan compare the differences between the outranking degreeand the overall outranking degree Then we further considerwhich criteria should be improved immediately especially forthe suppliers of strategic purchases The results of this studyprovide an important reference for subsidiary companiesthat are seeking to be primary polarizer suppliers Finallydiffering from many of the previous studies we do notintegrate the weights of the criteria with regard to a singleDM in this study In other worlds we integrate the rankingresults of eachDM instead of theweights of the criteria In theproposed evaluation procedure LAM places more emphasison the discussion and communication among a group ofDMs which is consistent with the practical case

In summary the result of this study can be regardedas a problem-solving process for green supplier selectionRegardless of the proposed method or analytic procedurewe aim at helping TFT-LCD manufacturers to establish asystematic and useful evaluation procedure via the currentstudy Because all of the supplier audit forms are in Microsoft

Excel 2010 spreadsheets the proposedmethod is easy to applyusing the same file Executive managers can consider theperformance of all suppliers and refer to the results of theproposed method Three main contributions are made inthis study First depending on the literatures and supplieraudit forms we develop a decision framework for the TFT-LCD industry Second we propose the useful systematicand flexible hybrid MCGDMmethod (based on the businessprocess) and improve the current evaluation procedureThird we place more emphasis on improving suppliers toenhance the competitiveness of entire supply chain especiallythe subsidiary company In practice green supplier selectionis evaluated by each department separately and all criteria areregarded as independent In recent years increasing attentionhas been given to the interdependent relationships amongthe criteria [15 16 26 49] Therefore future studies arenecessary to clarify the interrelations among the criteriaWe hope that more detailed information influencing therelationships between the criteria can be provided in futureresearch Consequently it would bemore efficient to improvethe supplier selection process for the entire supply chain inthe future work

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper

Acknowledgments

This research was partially supported by the National ScienceCouncil of the Republic of China (Grant NSC 102-2221-E-007-084) The authors would like to thank the editorand anonymous referees for their valuable comments andsuggestions

References

[1] Taiwan Stock Exchange ldquoIndustry analysis the flat paneldisplay Industryrdquo February 2013 httpwwwtwsecomtwchproductspublicationessayphp

[2] S Hung ldquoCompetitive strategies for Taiwanrsquos thin film tran-sistor-liquid crystal display (TFT-LCD) industryrdquoTechnology inSociety vol 28 no 3 pp 349ndash361 2006

[3] W Ho X Xu and P K Dey ldquoMulti-criteria decision makingapproaches for supplier evaluation and selection a literaturereviewrdquo European Journal of Operational Research vol 202 no1 pp 16ndash24 2010

12 Mathematical Problems in Engineering

[4] J Chai J N K Liu and E W T Ngai ldquoApplication of decision-making techniques in supplier selection a systematic review ofliteraturerdquo Expert Systems with Applications vol 40 no 10 pp3872ndash3885 2013

[5] K Govindan S Rajendran J Sarkis and P Murugesan ldquoMulticriteria decision making approaches for green supplier eval-uation and selection a literature reviewrdquo Journal of CleanerProduction 2013

[6] M Herva and E Roca ldquoReview of combined approaches andmulti-criteria analysis for corporate environmental evaluationrdquoJournal of Cleaner Production vol 39 pp 355ndash371 2013

[7] S Seuring ldquoA review of modeling approaches for sustainablesupply chain managementrdquo Decision Support Systems vol 54no 4 pp 1513ndash1520 2013

[8] M Igarashi L De Boer and A M Fet ldquoWhat is required forgreener supplier selection A literature review and conceptualmodel developmentrdquo Journal of Purchasing and SupplyManage-ment vol 19 no 4 pp 247ndash263 2013

[9] A Genovese S C Koh G Bruno and E Esposito ldquoGreenersupplier selection state of the art and some empirical evidencerdquoInternational Journal of Production Research vol 51 no 10 pp2868ndash2886 2013

[10] A H I Lee H Y Kang C F Hsu and H C Hung ldquoA greensupplier selectionmodel for high-tech industryrdquo Expert Systemswith Applications vol 36 no 4 pp 7917ndash7927 2009

[11] G Tuzkaya A Ozgen D Ozgen and U R Tuzkaya ldquoEnvi-ronmental performance evaluation of suppliers a hybrid fuzzymulti-criteria decision approachrdquo International Journal of Envi-ronmental Science and Technology vol 6 no 3 pp 477ndash4902009

[12] R J Kuo Y C Wang and F C Tien ldquoIntegration of artificialneural network and MADA methods for green supplier selec-tionrdquo Journal of Cleaner Production vol 18 no 12 pp 1161ndash11702010

[13] Q Zhu Y Dou and J Sarkis ldquoA portfolio-based analysis forgreen supplier management using the analytical network pro-cessrdquo Supply Chain Management vol 15 no 4 pp 306ndash3192010

[14] G Buyukozkan ldquoAn integrated fuzzy multi-criteria groupdecision-making approach for green supplier evaluationrdquo Inter-national Journal of Production Research vol 50 no 11 pp 2892ndash2909 2012

[15] G Buyukozkan and G Cifci ldquoA novel hybrid MCDM approachbased on fuzzy DEMATEL fuzzy ANP and fuzzy TOPSIS toevaluate green suppliersrdquo Expert Systems with Applications vol39 no 3 pp 3000ndash3011 2012

[16] C H Hsu F KWang and G H Tzeng ldquoThe best vendor selec-tion for conducting the recycled material based on a hybridMCDM model combining DANP with VIKORrdquo ResourcesConservation and Recycling vol 66 pp 95ndash111 2012

[17] K Govindan R Khodaverdi and A Jafarian ldquoA fuzzy multicriteria approach for measuring sustainability performance ofa supplier based on triple bottom line approachrdquo Journal ofCleaner Production vol 47 pp 345ndash354 2013

[18] L Shen L Olfat K Govindan R Khodaverdi and A DiabatldquoA fuzzy multi criteria approach for evaluating green supplierrsquosperformance in green supply chain with linguistic preferencesrdquoResources Conservation and Recycling vol 74 pp 170ndash179 2013

[19] M Yazdani ldquoAn integrated MCDM approach to green supplierselectionrdquo International Journal of Industrial Engineering Com-putations vol 5 no 3 pp 443ndash458 2014

[20] T Chen and J Freeman ldquoUsing AHP-Entropy weight and TOP-SIS methodology in green supplier selectionrdquo in Proceedings ofthe European Operations Management Association Conference(EurOMA rsquo14) Palermo Italy June 2014

[21] D Kannan A B L D S Jabbour andC J C Jabbour ldquoSelectinggreen suppliers based on GSCM practices using fuzzy TOPSISapplied to a Brazilian electronics companyrdquo European Journal ofOperational Research vol 233 no 2 pp 432ndash447 2014

[22] H Zhao and S Guo ldquoSelecting green supplier of thermalpower equipment by using a Hybrid MCDM Method forsustainabilityrdquo Sustainability vol 6 no 1 pp 217ndash235 2014

[23] J Figueira S Greco and M EhrgottMultiple Criteria DecisionAnalysis State of the Art Surveys Springer Science BostonMass USA 2005

[24] B Roy ldquoThe outranking approach and the foundations ofELECTREmethodsrdquoTheory and Decision vol 31 no 1 pp 49ndash73 1991

[25] J Hokkanen and P Salminen ldquoChoosing a solid waste man-agement systemusingmulticriteria decision analysisrdquoEuropeanJournal of Operational Research vol 98 no 1 pp 19ndash36 1997

[26] R J Lin ldquoUsing fuzzy DEMATEL to evaluate the green supplychain management practicesrdquo Journal of Cleaner Productionvol 40 pp 32ndash39 2013

[27] M L Tseng and A S F Chiu ldquoEvaluating firmrsquos green supplychain management in linguistic preferencesrdquo Journal of CleanerProduction vol 40 pp 22ndash31 2013

[28] G Buyukozkan and G Cifci ldquoA novel fuzzy mulati -criteriadecision framework for sustainable supplier selection withincomplete informationrdquo Computers in Industry vol 62 no 2pp 164ndash174 2011

[29] M T Escobar and J M Moreno-Jimenez ldquoAggregation of indi-vidual preference structures in AHP-group decision makingrdquoGroup Decision andNegotiation vol 16 no 4 pp 287ndash301 2007

[30] V Tsyganok ldquoInvestigation of the aggregation effectiveness ofexpert estimates obtained by the pairwise comparisonmethodrdquoMathematical and ComputerModelling vol 52 no 3-4 pp 538ndash544 2010

[31] J J Bernardo and J M Blin ldquoA programming model ofconsumer choice among multi-attributed brandsrdquo Journal ofConsumer Research vol 4 no 2 pp 111ndash118 1977

[32] P K Humphreys Y K Wong and F T S Chan ldquoIntegratingenvironmental criteria into the supplier selection processrdquoJournal of Materials Processing Technology vol 138 no 1ndash3 pp349ndash356 2003

[33] C Bai and J Sarkis ldquoIntegrating sustainability into supplierselection with grey system and rough set methodologiesrdquoInternational Journal of Production Economics vol 124 no 1 pp252ndash264 2010

[34] X Fu Q Zhu and J Sarkis ldquoEvaluating green supplier devel-opment programs at a telecommunications systems providerrdquoInternational Journal of Production Economics vol 140 no 1pp 357ndash367 2012

[35] A Amindoust S Ahmed A Saghafinia and A BahreininejadldquoSustainable supplier selection a ranking model based on fuzzyinference systemrdquoApplied Soft Computing Journal vol 12 no 6pp 1668ndash1677 2012

[36] K Shaw R Shankar S S Yadav and L S Thakur ldquoSupplierselection using fuzzy AHP and fuzzy multi-objective linearprogramming for developing low carbon supply chainrdquo ExpertSystems with Applications vol 39 no 9 pp 8182ndash8192 2012

Mathematical Problems in Engineering 13

[37] A Awasthi S S Chauhan and S K Goyal ldquoA fuzzy multi-criteria approach for evaluating environmental performance ofsuppliersrdquo International Journal of Production Economics vol126 no 2 pp 370ndash378 2010

[38] T Y Chiou H K Chan F Lettice and S H Chung ldquoThe influ-ence of greening the suppliers and green innovation on envi-ronmental performance and competitive advantage in TaiwanrdquoTransportation Research Part E Logistics and TransportationReview vol 47 no 6 pp 822ndash836 2011

[39] T L Saaty ldquoA scaling method for priorities in hierarchicalstructuresrdquo Journal of Mathematical Psychology vol 15 no 3pp 234ndash281 1977

[40] T L Saaty The Analytic Hierarchy Process McGraw-Hill NewYork NY USA 1980

[41] CMacharis J Springael K De Brucker andA Verbeke ldquoPRO-METHEE and AHP the design of operational synergies inmulticriteria analysis Strengthening PROMETHEE with ideasof AHPrdquo European Journal of Operational Research vol 153 no2 pp 307ndash317 2004

[42] M Zeleny Multiple Criteria Decision Making McGraw-HillNew York NY USA 1982

[43] C L Hwang and K Yoon Multiple Attribute Decision MakingMethods and Applications A State-of-the-Art Survey SpringerNew York NY USA 1981

[44] B Roy ldquoELECTRE III un algorithme de classements fondesur une repre sentation floue des preferences en preferences enpresence de criteres multiplesrdquo Cahiers du Centre drsquoEtudes deRecherche Operationnelle vol 20 no 1 pp 3ndash24 1978

[45] P Vincke Multiple Criteria Decision-Aid John Wiley amp SonsChichester UK 1992

[46] M Rogers and M Bruen ldquoUsing ELECTRE III to chooseroute for Dublin Port motorwayrdquo Journal of TransportationEngineering vol 126 no 4 pp 313ndash323 2000

[47] M Rogers and M Bruen ELECTRE and Decision SupportMethods and Applications in Engineering and InfrastructureInvestment Kluwer Academic Publishers Boston Mass USA2000

[48] V Belton and T J Stewart Multiple Criteria Decision AnalysisAn Integrated Approach Kluwer Academic Publishers BostonMass USA 2002

[49] B Chang C W Chang and C H Wu ldquoFuzzy DEMATELmethod for developing supplier selection criteriardquo Expert Sys-tems with Applications vol 38 no 3 pp 1850ndash1858 2011

Submit your manuscripts athttpwwwhindawicom

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

MathematicsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Mathematical Problems in Engineering

Hindawi Publishing Corporationhttpwwwhindawicom

Differential EquationsInternational Journal of

Volume 2014

Applied MathematicsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Probability and StatisticsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Mathematical PhysicsAdvances in

Complex AnalysisJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

OptimizationJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

CombinatoricsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Operations ResearchAdvances in

Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Function Spaces

Abstract and Applied AnalysisHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of Mathematics and Mathematical Sciences

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Algebra

Discrete Dynamics in Nature and Society

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Decision SciencesAdvances in

Discrete MathematicsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom

Volume 2014 Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Stochastic AnalysisInternational Journal of

10 Mathematical Problems in Engineering

Table 8 Initial evaluation of individual DM

Dept MM RD GPM SQMRanking 119889

11198892

1198893

1198894

1198895

1198896

1198897

1198898

1198899

11988910

11988911

11988912

1st 1198861

1198861 1198863

1198863

1198861

1198861

1198861

1198861

1198861

1198861

1198862 1198863 1198864

1198861

1198861 1198863

2nd 1198862 1198863

1198861

1198863

1198864

1198862

1198862

1198864

1198863

1198863 1198864

3rd 1198864

1198864

1198864

1198863

1198863

1198863 1198864

1198863

1198864

1198864

4th 1198864

1198862

1198862

1198862

1198862

1198864

1198862

1198862

1198861

1198862

1198862

Table 9 The worst performing criteria of each alternative

1198861

1198862

1198863

1198864

1198631

mdash mdash mdash 1198925

1198632

mdash 11989212

mdash mdash1198633

mdash 11989212

mdash mdash1198634

mdash 1198922

mdash mdash1198635

mdash 1198921

mdash mdash1198636

mdash 11989212

g7 11989213

1198637

mdash mdash g7 11989213

1198638

mdash 11989212

mdash mdash1198639

mdash 11989212

mdash 11989213

11986310

g15 mdash mdash mdash11986311

mdash 1198922

mdash 11989213

11986312

mdash 11989212

mdash mdash

GPM department processes less decision-making power forgreen supplier selection it is obvious that environmentalfactors are not significantly considered in the evaluationprocedure with the exception that RampD department shoulddesign new products and the related materials must be listedfor MM department Then MM department can place anorder and ask the suppliers to follow the list SQMdepartmentis concerned with technological capability as well as infor-mation share Vertical integration is an important enterprisepolicy for the TFT-LCD industry and strategic purchase isa method for the implementation of vertical integration Asmentioned before the key components of TFT-LCD panelsthat are produced in-house have gradually increased year byyear so information share is also an important criterion forcultivating the core capability of enterprise and establishinga competitive advantage Furthermore all of the criteriaplay important roles in the evaluation procedure for greensupplier selection If the proposed hybrid MCGDM methodonly adopts the AHP method it will most likely lead to theoversubjective weighting of the criteria Therefore we applythe entropy method to consider the weights of the criteriasimultaneously

ELECTRE III is applied to evaluate the suppliers Duringthe evaluation procedure we compare the outranking degreeand the overall outranking degree and then decide whichcriterion should be improved immediately for each alterna-tive as shown in Table 9 If the overall outranking degreeof a criterion decreases to zero (comparing to outrankingdegree) this criterion should be improved immediately Forthe best supplier supplier 1 (119886

1) the 119863

10manager suggests

that alternative 1 should strengthen communication with Y-TECH by taking actions such as improving cooperation forthe development of new products and instant market infor-mation share (119892

15) In this case study one of the most impor-

tant aspects is how to provide assistance to the subsidiarycompany (119886

3) of Y-TECH Table 9 indicates that if supplier

3 improves quality control (1198927) for polarizer production its

performance will be improved significantly Generally it iscontended that managers should be concerned about criteriarelated to their job description Due to limited enterpriseresources the order of executing enterprise improvementis also an important issue Following the useful proposedmethod the discovery of the criteria that need to be improvedwill become easy

Different from many previous studies this study doesnot integrate the weights of the criteria at the first stageIn fact green supplier selection is a group decision-makingproblem and integrating the weights would also lead tothe loss of information Therefore we further consider theranking results of each department as shown in Table 10Although these four polarizermanufacturers are the suppliersof Y-TECH there are some differences among the rankingresults of each department especially for suppliers 2 and 4 Asmentioned before RampD department placed great emphasison the ldquogreenrdquo criteria and supplier 4rsquos good performance onthese criteria is the reason for the different ranking resultsConsequently in this study we proposed a novel method andviewpoint for green supplier selection and we suggested thatthe weights of the criteria should not be integrated at thefirst stage because further discussions and communicationare necessary to include the different opinions from eachdepartment Additionally we compare the results of theproposed method with the ones implemented by Y-TECHthat is simple additive method in Table 10 Both results showthat supplier 1 has the best performance and is superior tosupplier 3 Supplier 4 is in the third place while supplier2 is the worst Furthermore within the extensive literatureon green supplier selection methods the TOPSIS methodwas widely applied to evaluate green suppliersrsquo performance[15 17ndash22] Thus we further compare the TOPSIS methodwith the proposedmethod and the comparison is establishedon the basis of AHP-entropy criteria weights In the TOPSISmethod with AHP-entropy weights supplier 3 is in the firstplace and superior to supplier 1 and supplier 4 and supplier2 have the worst performance in this evaluation Althoughthe ranking results among the proposed method and othertwo methods are similar the emphasis of this study is onhow to improve the suppliersrsquo performance in the TFT-LCD industry More importantly the results of the proposed

Mathematical Problems in Engineering 11

Table 10 Comparison of ranking results for the proposed method with other methods

Ranking MM RD GPM SQM The proposed method Current methodlowast AHP-entropy TOPSIS1st 119886

31198861

1198861

1198861

1198861

1198861

1198863

2nd 1198861

1198864

1198862

1198863

1198863

1198863

1198861

3rd 1198864

1198863

1198863

1198864

1198864

1198864

1198864

4th 1198862

1198862

1198864

1198862

1198862

1198862

1198862

lowast

The current evaluation method in Y-TECH is simple additive method

method show that each department has its different opinionsand viewpoints of improvement Each department of theTFT-LCDmanufacturers can assist their suppliers within thecontext of business perspectives

5 Conclusion

In this study we propose a hybrid MCGDM method forgreen supplier selection for Y-TECH Our method focuseson not only the selection or ranking but also the providenceof assistance to suppliers to strengthen their competitivenessThis method also emphasizes continuous improvement Dueto the challenges and difficulties the TFT-LCD industriesworldwide face we intend to establish a systematic selectionprocedure for TFT-LCD manufacturers We developed adecision framework for green supplier selection based onliteratures and the supplier audit forms from Y-TECH Weconsidered AHP and the entropy method to measure thecompromised weights of the criteria that involve subjectiveand objective opinions simultaneously After that the ELEC-TRE III method provides polarizer manufacturersrsquo rankingresults for executive managers in addition to improvementreports The acquisition of key components used for in-house production of TFT-LCD panels has gradually becomea strategy for cost reduction and the enhancement of corecapability Thus TFT-LCD manufacturers should strengthentheir cooperation with their suppliers

Based on the results of the ELECTRE III method wecan compare the differences between the outranking degreeand the overall outranking degree Then we further considerwhich criteria should be improved immediately especially forthe suppliers of strategic purchases The results of this studyprovide an important reference for subsidiary companiesthat are seeking to be primary polarizer suppliers Finallydiffering from many of the previous studies we do notintegrate the weights of the criteria with regard to a singleDM in this study In other worlds we integrate the rankingresults of eachDM instead of theweights of the criteria In theproposed evaluation procedure LAM places more emphasison the discussion and communication among a group ofDMs which is consistent with the practical case

In summary the result of this study can be regardedas a problem-solving process for green supplier selectionRegardless of the proposed method or analytic procedurewe aim at helping TFT-LCD manufacturers to establish asystematic and useful evaluation procedure via the currentstudy Because all of the supplier audit forms are in Microsoft

Excel 2010 spreadsheets the proposedmethod is easy to applyusing the same file Executive managers can consider theperformance of all suppliers and refer to the results of theproposed method Three main contributions are made inthis study First depending on the literatures and supplieraudit forms we develop a decision framework for the TFT-LCD industry Second we propose the useful systematicand flexible hybrid MCGDMmethod (based on the businessprocess) and improve the current evaluation procedureThird we place more emphasis on improving suppliers toenhance the competitiveness of entire supply chain especiallythe subsidiary company In practice green supplier selectionis evaluated by each department separately and all criteria areregarded as independent In recent years increasing attentionhas been given to the interdependent relationships amongthe criteria [15 16 26 49] Therefore future studies arenecessary to clarify the interrelations among the criteriaWe hope that more detailed information influencing therelationships between the criteria can be provided in futureresearch Consequently it would bemore efficient to improvethe supplier selection process for the entire supply chain inthe future work

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper

Acknowledgments

This research was partially supported by the National ScienceCouncil of the Republic of China (Grant NSC 102-2221-E-007-084) The authors would like to thank the editorand anonymous referees for their valuable comments andsuggestions

References

[1] Taiwan Stock Exchange ldquoIndustry analysis the flat paneldisplay Industryrdquo February 2013 httpwwwtwsecomtwchproductspublicationessayphp

[2] S Hung ldquoCompetitive strategies for Taiwanrsquos thin film tran-sistor-liquid crystal display (TFT-LCD) industryrdquoTechnology inSociety vol 28 no 3 pp 349ndash361 2006

[3] W Ho X Xu and P K Dey ldquoMulti-criteria decision makingapproaches for supplier evaluation and selection a literaturereviewrdquo European Journal of Operational Research vol 202 no1 pp 16ndash24 2010

12 Mathematical Problems in Engineering

[4] J Chai J N K Liu and E W T Ngai ldquoApplication of decision-making techniques in supplier selection a systematic review ofliteraturerdquo Expert Systems with Applications vol 40 no 10 pp3872ndash3885 2013

[5] K Govindan S Rajendran J Sarkis and P Murugesan ldquoMulticriteria decision making approaches for green supplier eval-uation and selection a literature reviewrdquo Journal of CleanerProduction 2013

[6] M Herva and E Roca ldquoReview of combined approaches andmulti-criteria analysis for corporate environmental evaluationrdquoJournal of Cleaner Production vol 39 pp 355ndash371 2013

[7] S Seuring ldquoA review of modeling approaches for sustainablesupply chain managementrdquo Decision Support Systems vol 54no 4 pp 1513ndash1520 2013

[8] M Igarashi L De Boer and A M Fet ldquoWhat is required forgreener supplier selection A literature review and conceptualmodel developmentrdquo Journal of Purchasing and SupplyManage-ment vol 19 no 4 pp 247ndash263 2013

[9] A Genovese S C Koh G Bruno and E Esposito ldquoGreenersupplier selection state of the art and some empirical evidencerdquoInternational Journal of Production Research vol 51 no 10 pp2868ndash2886 2013

[10] A H I Lee H Y Kang C F Hsu and H C Hung ldquoA greensupplier selectionmodel for high-tech industryrdquo Expert Systemswith Applications vol 36 no 4 pp 7917ndash7927 2009

[11] G Tuzkaya A Ozgen D Ozgen and U R Tuzkaya ldquoEnvi-ronmental performance evaluation of suppliers a hybrid fuzzymulti-criteria decision approachrdquo International Journal of Envi-ronmental Science and Technology vol 6 no 3 pp 477ndash4902009

[12] R J Kuo Y C Wang and F C Tien ldquoIntegration of artificialneural network and MADA methods for green supplier selec-tionrdquo Journal of Cleaner Production vol 18 no 12 pp 1161ndash11702010

[13] Q Zhu Y Dou and J Sarkis ldquoA portfolio-based analysis forgreen supplier management using the analytical network pro-cessrdquo Supply Chain Management vol 15 no 4 pp 306ndash3192010

[14] G Buyukozkan ldquoAn integrated fuzzy multi-criteria groupdecision-making approach for green supplier evaluationrdquo Inter-national Journal of Production Research vol 50 no 11 pp 2892ndash2909 2012

[15] G Buyukozkan and G Cifci ldquoA novel hybrid MCDM approachbased on fuzzy DEMATEL fuzzy ANP and fuzzy TOPSIS toevaluate green suppliersrdquo Expert Systems with Applications vol39 no 3 pp 3000ndash3011 2012

[16] C H Hsu F KWang and G H Tzeng ldquoThe best vendor selec-tion for conducting the recycled material based on a hybridMCDM model combining DANP with VIKORrdquo ResourcesConservation and Recycling vol 66 pp 95ndash111 2012

[17] K Govindan R Khodaverdi and A Jafarian ldquoA fuzzy multicriteria approach for measuring sustainability performance ofa supplier based on triple bottom line approachrdquo Journal ofCleaner Production vol 47 pp 345ndash354 2013

[18] L Shen L Olfat K Govindan R Khodaverdi and A DiabatldquoA fuzzy multi criteria approach for evaluating green supplierrsquosperformance in green supply chain with linguistic preferencesrdquoResources Conservation and Recycling vol 74 pp 170ndash179 2013

[19] M Yazdani ldquoAn integrated MCDM approach to green supplierselectionrdquo International Journal of Industrial Engineering Com-putations vol 5 no 3 pp 443ndash458 2014

[20] T Chen and J Freeman ldquoUsing AHP-Entropy weight and TOP-SIS methodology in green supplier selectionrdquo in Proceedings ofthe European Operations Management Association Conference(EurOMA rsquo14) Palermo Italy June 2014

[21] D Kannan A B L D S Jabbour andC J C Jabbour ldquoSelectinggreen suppliers based on GSCM practices using fuzzy TOPSISapplied to a Brazilian electronics companyrdquo European Journal ofOperational Research vol 233 no 2 pp 432ndash447 2014

[22] H Zhao and S Guo ldquoSelecting green supplier of thermalpower equipment by using a Hybrid MCDM Method forsustainabilityrdquo Sustainability vol 6 no 1 pp 217ndash235 2014

[23] J Figueira S Greco and M EhrgottMultiple Criteria DecisionAnalysis State of the Art Surveys Springer Science BostonMass USA 2005

[24] B Roy ldquoThe outranking approach and the foundations ofELECTREmethodsrdquoTheory and Decision vol 31 no 1 pp 49ndash73 1991

[25] J Hokkanen and P Salminen ldquoChoosing a solid waste man-agement systemusingmulticriteria decision analysisrdquoEuropeanJournal of Operational Research vol 98 no 1 pp 19ndash36 1997

[26] R J Lin ldquoUsing fuzzy DEMATEL to evaluate the green supplychain management practicesrdquo Journal of Cleaner Productionvol 40 pp 32ndash39 2013

[27] M L Tseng and A S F Chiu ldquoEvaluating firmrsquos green supplychain management in linguistic preferencesrdquo Journal of CleanerProduction vol 40 pp 22ndash31 2013

[28] G Buyukozkan and G Cifci ldquoA novel fuzzy mulati -criteriadecision framework for sustainable supplier selection withincomplete informationrdquo Computers in Industry vol 62 no 2pp 164ndash174 2011

[29] M T Escobar and J M Moreno-Jimenez ldquoAggregation of indi-vidual preference structures in AHP-group decision makingrdquoGroup Decision andNegotiation vol 16 no 4 pp 287ndash301 2007

[30] V Tsyganok ldquoInvestigation of the aggregation effectiveness ofexpert estimates obtained by the pairwise comparisonmethodrdquoMathematical and ComputerModelling vol 52 no 3-4 pp 538ndash544 2010

[31] J J Bernardo and J M Blin ldquoA programming model ofconsumer choice among multi-attributed brandsrdquo Journal ofConsumer Research vol 4 no 2 pp 111ndash118 1977

[32] P K Humphreys Y K Wong and F T S Chan ldquoIntegratingenvironmental criteria into the supplier selection processrdquoJournal of Materials Processing Technology vol 138 no 1ndash3 pp349ndash356 2003

[33] C Bai and J Sarkis ldquoIntegrating sustainability into supplierselection with grey system and rough set methodologiesrdquoInternational Journal of Production Economics vol 124 no 1 pp252ndash264 2010

[34] X Fu Q Zhu and J Sarkis ldquoEvaluating green supplier devel-opment programs at a telecommunications systems providerrdquoInternational Journal of Production Economics vol 140 no 1pp 357ndash367 2012

[35] A Amindoust S Ahmed A Saghafinia and A BahreininejadldquoSustainable supplier selection a ranking model based on fuzzyinference systemrdquoApplied Soft Computing Journal vol 12 no 6pp 1668ndash1677 2012

[36] K Shaw R Shankar S S Yadav and L S Thakur ldquoSupplierselection using fuzzy AHP and fuzzy multi-objective linearprogramming for developing low carbon supply chainrdquo ExpertSystems with Applications vol 39 no 9 pp 8182ndash8192 2012

Mathematical Problems in Engineering 13

[37] A Awasthi S S Chauhan and S K Goyal ldquoA fuzzy multi-criteria approach for evaluating environmental performance ofsuppliersrdquo International Journal of Production Economics vol126 no 2 pp 370ndash378 2010

[38] T Y Chiou H K Chan F Lettice and S H Chung ldquoThe influ-ence of greening the suppliers and green innovation on envi-ronmental performance and competitive advantage in TaiwanrdquoTransportation Research Part E Logistics and TransportationReview vol 47 no 6 pp 822ndash836 2011

[39] T L Saaty ldquoA scaling method for priorities in hierarchicalstructuresrdquo Journal of Mathematical Psychology vol 15 no 3pp 234ndash281 1977

[40] T L Saaty The Analytic Hierarchy Process McGraw-Hill NewYork NY USA 1980

[41] CMacharis J Springael K De Brucker andA Verbeke ldquoPRO-METHEE and AHP the design of operational synergies inmulticriteria analysis Strengthening PROMETHEE with ideasof AHPrdquo European Journal of Operational Research vol 153 no2 pp 307ndash317 2004

[42] M Zeleny Multiple Criteria Decision Making McGraw-HillNew York NY USA 1982

[43] C L Hwang and K Yoon Multiple Attribute Decision MakingMethods and Applications A State-of-the-Art Survey SpringerNew York NY USA 1981

[44] B Roy ldquoELECTRE III un algorithme de classements fondesur une repre sentation floue des preferences en preferences enpresence de criteres multiplesrdquo Cahiers du Centre drsquoEtudes deRecherche Operationnelle vol 20 no 1 pp 3ndash24 1978

[45] P Vincke Multiple Criteria Decision-Aid John Wiley amp SonsChichester UK 1992

[46] M Rogers and M Bruen ldquoUsing ELECTRE III to chooseroute for Dublin Port motorwayrdquo Journal of TransportationEngineering vol 126 no 4 pp 313ndash323 2000

[47] M Rogers and M Bruen ELECTRE and Decision SupportMethods and Applications in Engineering and InfrastructureInvestment Kluwer Academic Publishers Boston Mass USA2000

[48] V Belton and T J Stewart Multiple Criteria Decision AnalysisAn Integrated Approach Kluwer Academic Publishers BostonMass USA 2002

[49] B Chang C W Chang and C H Wu ldquoFuzzy DEMATELmethod for developing supplier selection criteriardquo Expert Sys-tems with Applications vol 38 no 3 pp 1850ndash1858 2011

Submit your manuscripts athttpwwwhindawicom

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

MathematicsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Mathematical Problems in Engineering

Hindawi Publishing Corporationhttpwwwhindawicom

Differential EquationsInternational Journal of

Volume 2014

Applied MathematicsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Probability and StatisticsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Mathematical PhysicsAdvances in

Complex AnalysisJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

OptimizationJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

CombinatoricsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Operations ResearchAdvances in

Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Function Spaces

Abstract and Applied AnalysisHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of Mathematics and Mathematical Sciences

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Algebra

Discrete Dynamics in Nature and Society

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Decision SciencesAdvances in

Discrete MathematicsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom

Volume 2014 Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Stochastic AnalysisInternational Journal of

Mathematical Problems in Engineering 11

Table 10 Comparison of ranking results for the proposed method with other methods

Ranking MM RD GPM SQM The proposed method Current methodlowast AHP-entropy TOPSIS1st 119886

31198861

1198861

1198861

1198861

1198861

1198863

2nd 1198861

1198864

1198862

1198863

1198863

1198863

1198861

3rd 1198864

1198863

1198863

1198864

1198864

1198864

1198864

4th 1198862

1198862

1198864

1198862

1198862

1198862

1198862

lowast

The current evaluation method in Y-TECH is simple additive method

method show that each department has its different opinionsand viewpoints of improvement Each department of theTFT-LCDmanufacturers can assist their suppliers within thecontext of business perspectives

5 Conclusion

In this study we propose a hybrid MCGDM method forgreen supplier selection for Y-TECH Our method focuseson not only the selection or ranking but also the providenceof assistance to suppliers to strengthen their competitivenessThis method also emphasizes continuous improvement Dueto the challenges and difficulties the TFT-LCD industriesworldwide face we intend to establish a systematic selectionprocedure for TFT-LCD manufacturers We developed adecision framework for green supplier selection based onliteratures and the supplier audit forms from Y-TECH Weconsidered AHP and the entropy method to measure thecompromised weights of the criteria that involve subjectiveand objective opinions simultaneously After that the ELEC-TRE III method provides polarizer manufacturersrsquo rankingresults for executive managers in addition to improvementreports The acquisition of key components used for in-house production of TFT-LCD panels has gradually becomea strategy for cost reduction and the enhancement of corecapability Thus TFT-LCD manufacturers should strengthentheir cooperation with their suppliers

Based on the results of the ELECTRE III method wecan compare the differences between the outranking degreeand the overall outranking degree Then we further considerwhich criteria should be improved immediately especially forthe suppliers of strategic purchases The results of this studyprovide an important reference for subsidiary companiesthat are seeking to be primary polarizer suppliers Finallydiffering from many of the previous studies we do notintegrate the weights of the criteria with regard to a singleDM in this study In other worlds we integrate the rankingresults of eachDM instead of theweights of the criteria In theproposed evaluation procedure LAM places more emphasison the discussion and communication among a group ofDMs which is consistent with the practical case

In summary the result of this study can be regardedas a problem-solving process for green supplier selectionRegardless of the proposed method or analytic procedurewe aim at helping TFT-LCD manufacturers to establish asystematic and useful evaluation procedure via the currentstudy Because all of the supplier audit forms are in Microsoft

Excel 2010 spreadsheets the proposedmethod is easy to applyusing the same file Executive managers can consider theperformance of all suppliers and refer to the results of theproposed method Three main contributions are made inthis study First depending on the literatures and supplieraudit forms we develop a decision framework for the TFT-LCD industry Second we propose the useful systematicand flexible hybrid MCGDMmethod (based on the businessprocess) and improve the current evaluation procedureThird we place more emphasis on improving suppliers toenhance the competitiveness of entire supply chain especiallythe subsidiary company In practice green supplier selectionis evaluated by each department separately and all criteria areregarded as independent In recent years increasing attentionhas been given to the interdependent relationships amongthe criteria [15 16 26 49] Therefore future studies arenecessary to clarify the interrelations among the criteriaWe hope that more detailed information influencing therelationships between the criteria can be provided in futureresearch Consequently it would bemore efficient to improvethe supplier selection process for the entire supply chain inthe future work

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper

Acknowledgments

This research was partially supported by the National ScienceCouncil of the Republic of China (Grant NSC 102-2221-E-007-084) The authors would like to thank the editorand anonymous referees for their valuable comments andsuggestions

References

[1] Taiwan Stock Exchange ldquoIndustry analysis the flat paneldisplay Industryrdquo February 2013 httpwwwtwsecomtwchproductspublicationessayphp

[2] S Hung ldquoCompetitive strategies for Taiwanrsquos thin film tran-sistor-liquid crystal display (TFT-LCD) industryrdquoTechnology inSociety vol 28 no 3 pp 349ndash361 2006

[3] W Ho X Xu and P K Dey ldquoMulti-criteria decision makingapproaches for supplier evaluation and selection a literaturereviewrdquo European Journal of Operational Research vol 202 no1 pp 16ndash24 2010

12 Mathematical Problems in Engineering

[4] J Chai J N K Liu and E W T Ngai ldquoApplication of decision-making techniques in supplier selection a systematic review ofliteraturerdquo Expert Systems with Applications vol 40 no 10 pp3872ndash3885 2013

[5] K Govindan S Rajendran J Sarkis and P Murugesan ldquoMulticriteria decision making approaches for green supplier eval-uation and selection a literature reviewrdquo Journal of CleanerProduction 2013

[6] M Herva and E Roca ldquoReview of combined approaches andmulti-criteria analysis for corporate environmental evaluationrdquoJournal of Cleaner Production vol 39 pp 355ndash371 2013

[7] S Seuring ldquoA review of modeling approaches for sustainablesupply chain managementrdquo Decision Support Systems vol 54no 4 pp 1513ndash1520 2013

[8] M Igarashi L De Boer and A M Fet ldquoWhat is required forgreener supplier selection A literature review and conceptualmodel developmentrdquo Journal of Purchasing and SupplyManage-ment vol 19 no 4 pp 247ndash263 2013

[9] A Genovese S C Koh G Bruno and E Esposito ldquoGreenersupplier selection state of the art and some empirical evidencerdquoInternational Journal of Production Research vol 51 no 10 pp2868ndash2886 2013

[10] A H I Lee H Y Kang C F Hsu and H C Hung ldquoA greensupplier selectionmodel for high-tech industryrdquo Expert Systemswith Applications vol 36 no 4 pp 7917ndash7927 2009

[11] G Tuzkaya A Ozgen D Ozgen and U R Tuzkaya ldquoEnvi-ronmental performance evaluation of suppliers a hybrid fuzzymulti-criteria decision approachrdquo International Journal of Envi-ronmental Science and Technology vol 6 no 3 pp 477ndash4902009

[12] R J Kuo Y C Wang and F C Tien ldquoIntegration of artificialneural network and MADA methods for green supplier selec-tionrdquo Journal of Cleaner Production vol 18 no 12 pp 1161ndash11702010

[13] Q Zhu Y Dou and J Sarkis ldquoA portfolio-based analysis forgreen supplier management using the analytical network pro-cessrdquo Supply Chain Management vol 15 no 4 pp 306ndash3192010

[14] G Buyukozkan ldquoAn integrated fuzzy multi-criteria groupdecision-making approach for green supplier evaluationrdquo Inter-national Journal of Production Research vol 50 no 11 pp 2892ndash2909 2012

[15] G Buyukozkan and G Cifci ldquoA novel hybrid MCDM approachbased on fuzzy DEMATEL fuzzy ANP and fuzzy TOPSIS toevaluate green suppliersrdquo Expert Systems with Applications vol39 no 3 pp 3000ndash3011 2012

[16] C H Hsu F KWang and G H Tzeng ldquoThe best vendor selec-tion for conducting the recycled material based on a hybridMCDM model combining DANP with VIKORrdquo ResourcesConservation and Recycling vol 66 pp 95ndash111 2012

[17] K Govindan R Khodaverdi and A Jafarian ldquoA fuzzy multicriteria approach for measuring sustainability performance ofa supplier based on triple bottom line approachrdquo Journal ofCleaner Production vol 47 pp 345ndash354 2013

[18] L Shen L Olfat K Govindan R Khodaverdi and A DiabatldquoA fuzzy multi criteria approach for evaluating green supplierrsquosperformance in green supply chain with linguistic preferencesrdquoResources Conservation and Recycling vol 74 pp 170ndash179 2013

[19] M Yazdani ldquoAn integrated MCDM approach to green supplierselectionrdquo International Journal of Industrial Engineering Com-putations vol 5 no 3 pp 443ndash458 2014

[20] T Chen and J Freeman ldquoUsing AHP-Entropy weight and TOP-SIS methodology in green supplier selectionrdquo in Proceedings ofthe European Operations Management Association Conference(EurOMA rsquo14) Palermo Italy June 2014

[21] D Kannan A B L D S Jabbour andC J C Jabbour ldquoSelectinggreen suppliers based on GSCM practices using fuzzy TOPSISapplied to a Brazilian electronics companyrdquo European Journal ofOperational Research vol 233 no 2 pp 432ndash447 2014

[22] H Zhao and S Guo ldquoSelecting green supplier of thermalpower equipment by using a Hybrid MCDM Method forsustainabilityrdquo Sustainability vol 6 no 1 pp 217ndash235 2014

[23] J Figueira S Greco and M EhrgottMultiple Criteria DecisionAnalysis State of the Art Surveys Springer Science BostonMass USA 2005

[24] B Roy ldquoThe outranking approach and the foundations ofELECTREmethodsrdquoTheory and Decision vol 31 no 1 pp 49ndash73 1991

[25] J Hokkanen and P Salminen ldquoChoosing a solid waste man-agement systemusingmulticriteria decision analysisrdquoEuropeanJournal of Operational Research vol 98 no 1 pp 19ndash36 1997

[26] R J Lin ldquoUsing fuzzy DEMATEL to evaluate the green supplychain management practicesrdquo Journal of Cleaner Productionvol 40 pp 32ndash39 2013

[27] M L Tseng and A S F Chiu ldquoEvaluating firmrsquos green supplychain management in linguistic preferencesrdquo Journal of CleanerProduction vol 40 pp 22ndash31 2013

[28] G Buyukozkan and G Cifci ldquoA novel fuzzy mulati -criteriadecision framework for sustainable supplier selection withincomplete informationrdquo Computers in Industry vol 62 no 2pp 164ndash174 2011

[29] M T Escobar and J M Moreno-Jimenez ldquoAggregation of indi-vidual preference structures in AHP-group decision makingrdquoGroup Decision andNegotiation vol 16 no 4 pp 287ndash301 2007

[30] V Tsyganok ldquoInvestigation of the aggregation effectiveness ofexpert estimates obtained by the pairwise comparisonmethodrdquoMathematical and ComputerModelling vol 52 no 3-4 pp 538ndash544 2010

[31] J J Bernardo and J M Blin ldquoA programming model ofconsumer choice among multi-attributed brandsrdquo Journal ofConsumer Research vol 4 no 2 pp 111ndash118 1977

[32] P K Humphreys Y K Wong and F T S Chan ldquoIntegratingenvironmental criteria into the supplier selection processrdquoJournal of Materials Processing Technology vol 138 no 1ndash3 pp349ndash356 2003

[33] C Bai and J Sarkis ldquoIntegrating sustainability into supplierselection with grey system and rough set methodologiesrdquoInternational Journal of Production Economics vol 124 no 1 pp252ndash264 2010

[34] X Fu Q Zhu and J Sarkis ldquoEvaluating green supplier devel-opment programs at a telecommunications systems providerrdquoInternational Journal of Production Economics vol 140 no 1pp 357ndash367 2012

[35] A Amindoust S Ahmed A Saghafinia and A BahreininejadldquoSustainable supplier selection a ranking model based on fuzzyinference systemrdquoApplied Soft Computing Journal vol 12 no 6pp 1668ndash1677 2012

[36] K Shaw R Shankar S S Yadav and L S Thakur ldquoSupplierselection using fuzzy AHP and fuzzy multi-objective linearprogramming for developing low carbon supply chainrdquo ExpertSystems with Applications vol 39 no 9 pp 8182ndash8192 2012

Mathematical Problems in Engineering 13

[37] A Awasthi S S Chauhan and S K Goyal ldquoA fuzzy multi-criteria approach for evaluating environmental performance ofsuppliersrdquo International Journal of Production Economics vol126 no 2 pp 370ndash378 2010

[38] T Y Chiou H K Chan F Lettice and S H Chung ldquoThe influ-ence of greening the suppliers and green innovation on envi-ronmental performance and competitive advantage in TaiwanrdquoTransportation Research Part E Logistics and TransportationReview vol 47 no 6 pp 822ndash836 2011

[39] T L Saaty ldquoA scaling method for priorities in hierarchicalstructuresrdquo Journal of Mathematical Psychology vol 15 no 3pp 234ndash281 1977

[40] T L Saaty The Analytic Hierarchy Process McGraw-Hill NewYork NY USA 1980

[41] CMacharis J Springael K De Brucker andA Verbeke ldquoPRO-METHEE and AHP the design of operational synergies inmulticriteria analysis Strengthening PROMETHEE with ideasof AHPrdquo European Journal of Operational Research vol 153 no2 pp 307ndash317 2004

[42] M Zeleny Multiple Criteria Decision Making McGraw-HillNew York NY USA 1982

[43] C L Hwang and K Yoon Multiple Attribute Decision MakingMethods and Applications A State-of-the-Art Survey SpringerNew York NY USA 1981

[44] B Roy ldquoELECTRE III un algorithme de classements fondesur une repre sentation floue des preferences en preferences enpresence de criteres multiplesrdquo Cahiers du Centre drsquoEtudes deRecherche Operationnelle vol 20 no 1 pp 3ndash24 1978

[45] P Vincke Multiple Criteria Decision-Aid John Wiley amp SonsChichester UK 1992

[46] M Rogers and M Bruen ldquoUsing ELECTRE III to chooseroute for Dublin Port motorwayrdquo Journal of TransportationEngineering vol 126 no 4 pp 313ndash323 2000

[47] M Rogers and M Bruen ELECTRE and Decision SupportMethods and Applications in Engineering and InfrastructureInvestment Kluwer Academic Publishers Boston Mass USA2000

[48] V Belton and T J Stewart Multiple Criteria Decision AnalysisAn Integrated Approach Kluwer Academic Publishers BostonMass USA 2002

[49] B Chang C W Chang and C H Wu ldquoFuzzy DEMATELmethod for developing supplier selection criteriardquo Expert Sys-tems with Applications vol 38 no 3 pp 1850ndash1858 2011

Submit your manuscripts athttpwwwhindawicom

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

MathematicsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Mathematical Problems in Engineering

Hindawi Publishing Corporationhttpwwwhindawicom

Differential EquationsInternational Journal of

Volume 2014

Applied MathematicsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Probability and StatisticsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Mathematical PhysicsAdvances in

Complex AnalysisJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

OptimizationJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

CombinatoricsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Operations ResearchAdvances in

Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Function Spaces

Abstract and Applied AnalysisHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of Mathematics and Mathematical Sciences

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Algebra

Discrete Dynamics in Nature and Society

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Decision SciencesAdvances in

Discrete MathematicsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom

Volume 2014 Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Stochastic AnalysisInternational Journal of

12 Mathematical Problems in Engineering

[4] J Chai J N K Liu and E W T Ngai ldquoApplication of decision-making techniques in supplier selection a systematic review ofliteraturerdquo Expert Systems with Applications vol 40 no 10 pp3872ndash3885 2013

[5] K Govindan S Rajendran J Sarkis and P Murugesan ldquoMulticriteria decision making approaches for green supplier eval-uation and selection a literature reviewrdquo Journal of CleanerProduction 2013

[6] M Herva and E Roca ldquoReview of combined approaches andmulti-criteria analysis for corporate environmental evaluationrdquoJournal of Cleaner Production vol 39 pp 355ndash371 2013

[7] S Seuring ldquoA review of modeling approaches for sustainablesupply chain managementrdquo Decision Support Systems vol 54no 4 pp 1513ndash1520 2013

[8] M Igarashi L De Boer and A M Fet ldquoWhat is required forgreener supplier selection A literature review and conceptualmodel developmentrdquo Journal of Purchasing and SupplyManage-ment vol 19 no 4 pp 247ndash263 2013

[9] A Genovese S C Koh G Bruno and E Esposito ldquoGreenersupplier selection state of the art and some empirical evidencerdquoInternational Journal of Production Research vol 51 no 10 pp2868ndash2886 2013

[10] A H I Lee H Y Kang C F Hsu and H C Hung ldquoA greensupplier selectionmodel for high-tech industryrdquo Expert Systemswith Applications vol 36 no 4 pp 7917ndash7927 2009

[11] G Tuzkaya A Ozgen D Ozgen and U R Tuzkaya ldquoEnvi-ronmental performance evaluation of suppliers a hybrid fuzzymulti-criteria decision approachrdquo International Journal of Envi-ronmental Science and Technology vol 6 no 3 pp 477ndash4902009

[12] R J Kuo Y C Wang and F C Tien ldquoIntegration of artificialneural network and MADA methods for green supplier selec-tionrdquo Journal of Cleaner Production vol 18 no 12 pp 1161ndash11702010

[13] Q Zhu Y Dou and J Sarkis ldquoA portfolio-based analysis forgreen supplier management using the analytical network pro-cessrdquo Supply Chain Management vol 15 no 4 pp 306ndash3192010

[14] G Buyukozkan ldquoAn integrated fuzzy multi-criteria groupdecision-making approach for green supplier evaluationrdquo Inter-national Journal of Production Research vol 50 no 11 pp 2892ndash2909 2012

[15] G Buyukozkan and G Cifci ldquoA novel hybrid MCDM approachbased on fuzzy DEMATEL fuzzy ANP and fuzzy TOPSIS toevaluate green suppliersrdquo Expert Systems with Applications vol39 no 3 pp 3000ndash3011 2012

[16] C H Hsu F KWang and G H Tzeng ldquoThe best vendor selec-tion for conducting the recycled material based on a hybridMCDM model combining DANP with VIKORrdquo ResourcesConservation and Recycling vol 66 pp 95ndash111 2012

[17] K Govindan R Khodaverdi and A Jafarian ldquoA fuzzy multicriteria approach for measuring sustainability performance ofa supplier based on triple bottom line approachrdquo Journal ofCleaner Production vol 47 pp 345ndash354 2013

[18] L Shen L Olfat K Govindan R Khodaverdi and A DiabatldquoA fuzzy multi criteria approach for evaluating green supplierrsquosperformance in green supply chain with linguistic preferencesrdquoResources Conservation and Recycling vol 74 pp 170ndash179 2013

[19] M Yazdani ldquoAn integrated MCDM approach to green supplierselectionrdquo International Journal of Industrial Engineering Com-putations vol 5 no 3 pp 443ndash458 2014

[20] T Chen and J Freeman ldquoUsing AHP-Entropy weight and TOP-SIS methodology in green supplier selectionrdquo in Proceedings ofthe European Operations Management Association Conference(EurOMA rsquo14) Palermo Italy June 2014

[21] D Kannan A B L D S Jabbour andC J C Jabbour ldquoSelectinggreen suppliers based on GSCM practices using fuzzy TOPSISapplied to a Brazilian electronics companyrdquo European Journal ofOperational Research vol 233 no 2 pp 432ndash447 2014

[22] H Zhao and S Guo ldquoSelecting green supplier of thermalpower equipment by using a Hybrid MCDM Method forsustainabilityrdquo Sustainability vol 6 no 1 pp 217ndash235 2014

[23] J Figueira S Greco and M EhrgottMultiple Criteria DecisionAnalysis State of the Art Surveys Springer Science BostonMass USA 2005

[24] B Roy ldquoThe outranking approach and the foundations ofELECTREmethodsrdquoTheory and Decision vol 31 no 1 pp 49ndash73 1991

[25] J Hokkanen and P Salminen ldquoChoosing a solid waste man-agement systemusingmulticriteria decision analysisrdquoEuropeanJournal of Operational Research vol 98 no 1 pp 19ndash36 1997

[26] R J Lin ldquoUsing fuzzy DEMATEL to evaluate the green supplychain management practicesrdquo Journal of Cleaner Productionvol 40 pp 32ndash39 2013

[27] M L Tseng and A S F Chiu ldquoEvaluating firmrsquos green supplychain management in linguistic preferencesrdquo Journal of CleanerProduction vol 40 pp 22ndash31 2013

[28] G Buyukozkan and G Cifci ldquoA novel fuzzy mulati -criteriadecision framework for sustainable supplier selection withincomplete informationrdquo Computers in Industry vol 62 no 2pp 164ndash174 2011

[29] M T Escobar and J M Moreno-Jimenez ldquoAggregation of indi-vidual preference structures in AHP-group decision makingrdquoGroup Decision andNegotiation vol 16 no 4 pp 287ndash301 2007

[30] V Tsyganok ldquoInvestigation of the aggregation effectiveness ofexpert estimates obtained by the pairwise comparisonmethodrdquoMathematical and ComputerModelling vol 52 no 3-4 pp 538ndash544 2010

[31] J J Bernardo and J M Blin ldquoA programming model ofconsumer choice among multi-attributed brandsrdquo Journal ofConsumer Research vol 4 no 2 pp 111ndash118 1977

[32] P K Humphreys Y K Wong and F T S Chan ldquoIntegratingenvironmental criteria into the supplier selection processrdquoJournal of Materials Processing Technology vol 138 no 1ndash3 pp349ndash356 2003

[33] C Bai and J Sarkis ldquoIntegrating sustainability into supplierselection with grey system and rough set methodologiesrdquoInternational Journal of Production Economics vol 124 no 1 pp252ndash264 2010

[34] X Fu Q Zhu and J Sarkis ldquoEvaluating green supplier devel-opment programs at a telecommunications systems providerrdquoInternational Journal of Production Economics vol 140 no 1pp 357ndash367 2012

[35] A Amindoust S Ahmed A Saghafinia and A BahreininejadldquoSustainable supplier selection a ranking model based on fuzzyinference systemrdquoApplied Soft Computing Journal vol 12 no 6pp 1668ndash1677 2012

[36] K Shaw R Shankar S S Yadav and L S Thakur ldquoSupplierselection using fuzzy AHP and fuzzy multi-objective linearprogramming for developing low carbon supply chainrdquo ExpertSystems with Applications vol 39 no 9 pp 8182ndash8192 2012

Mathematical Problems in Engineering 13

[37] A Awasthi S S Chauhan and S K Goyal ldquoA fuzzy multi-criteria approach for evaluating environmental performance ofsuppliersrdquo International Journal of Production Economics vol126 no 2 pp 370ndash378 2010

[38] T Y Chiou H K Chan F Lettice and S H Chung ldquoThe influ-ence of greening the suppliers and green innovation on envi-ronmental performance and competitive advantage in TaiwanrdquoTransportation Research Part E Logistics and TransportationReview vol 47 no 6 pp 822ndash836 2011

[39] T L Saaty ldquoA scaling method for priorities in hierarchicalstructuresrdquo Journal of Mathematical Psychology vol 15 no 3pp 234ndash281 1977

[40] T L Saaty The Analytic Hierarchy Process McGraw-Hill NewYork NY USA 1980

[41] CMacharis J Springael K De Brucker andA Verbeke ldquoPRO-METHEE and AHP the design of operational synergies inmulticriteria analysis Strengthening PROMETHEE with ideasof AHPrdquo European Journal of Operational Research vol 153 no2 pp 307ndash317 2004

[42] M Zeleny Multiple Criteria Decision Making McGraw-HillNew York NY USA 1982

[43] C L Hwang and K Yoon Multiple Attribute Decision MakingMethods and Applications A State-of-the-Art Survey SpringerNew York NY USA 1981

[44] B Roy ldquoELECTRE III un algorithme de classements fondesur une repre sentation floue des preferences en preferences enpresence de criteres multiplesrdquo Cahiers du Centre drsquoEtudes deRecherche Operationnelle vol 20 no 1 pp 3ndash24 1978

[45] P Vincke Multiple Criteria Decision-Aid John Wiley amp SonsChichester UK 1992

[46] M Rogers and M Bruen ldquoUsing ELECTRE III to chooseroute for Dublin Port motorwayrdquo Journal of TransportationEngineering vol 126 no 4 pp 313ndash323 2000

[47] M Rogers and M Bruen ELECTRE and Decision SupportMethods and Applications in Engineering and InfrastructureInvestment Kluwer Academic Publishers Boston Mass USA2000

[48] V Belton and T J Stewart Multiple Criteria Decision AnalysisAn Integrated Approach Kluwer Academic Publishers BostonMass USA 2002

[49] B Chang C W Chang and C H Wu ldquoFuzzy DEMATELmethod for developing supplier selection criteriardquo Expert Sys-tems with Applications vol 38 no 3 pp 1850ndash1858 2011

Submit your manuscripts athttpwwwhindawicom

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

MathematicsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Mathematical Problems in Engineering

Hindawi Publishing Corporationhttpwwwhindawicom

Differential EquationsInternational Journal of

Volume 2014

Applied MathematicsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Probability and StatisticsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Mathematical PhysicsAdvances in

Complex AnalysisJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

OptimizationJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

CombinatoricsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Operations ResearchAdvances in

Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Function Spaces

Abstract and Applied AnalysisHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of Mathematics and Mathematical Sciences

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Algebra

Discrete Dynamics in Nature and Society

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Decision SciencesAdvances in

Discrete MathematicsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom

Volume 2014 Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Stochastic AnalysisInternational Journal of

Mathematical Problems in Engineering 13

[37] A Awasthi S S Chauhan and S K Goyal ldquoA fuzzy multi-criteria approach for evaluating environmental performance ofsuppliersrdquo International Journal of Production Economics vol126 no 2 pp 370ndash378 2010

[38] T Y Chiou H K Chan F Lettice and S H Chung ldquoThe influ-ence of greening the suppliers and green innovation on envi-ronmental performance and competitive advantage in TaiwanrdquoTransportation Research Part E Logistics and TransportationReview vol 47 no 6 pp 822ndash836 2011

[39] T L Saaty ldquoA scaling method for priorities in hierarchicalstructuresrdquo Journal of Mathematical Psychology vol 15 no 3pp 234ndash281 1977

[40] T L Saaty The Analytic Hierarchy Process McGraw-Hill NewYork NY USA 1980

[41] CMacharis J Springael K De Brucker andA Verbeke ldquoPRO-METHEE and AHP the design of operational synergies inmulticriteria analysis Strengthening PROMETHEE with ideasof AHPrdquo European Journal of Operational Research vol 153 no2 pp 307ndash317 2004

[42] M Zeleny Multiple Criteria Decision Making McGraw-HillNew York NY USA 1982

[43] C L Hwang and K Yoon Multiple Attribute Decision MakingMethods and Applications A State-of-the-Art Survey SpringerNew York NY USA 1981

[44] B Roy ldquoELECTRE III un algorithme de classements fondesur une repre sentation floue des preferences en preferences enpresence de criteres multiplesrdquo Cahiers du Centre drsquoEtudes deRecherche Operationnelle vol 20 no 1 pp 3ndash24 1978

[45] P Vincke Multiple Criteria Decision-Aid John Wiley amp SonsChichester UK 1992

[46] M Rogers and M Bruen ldquoUsing ELECTRE III to chooseroute for Dublin Port motorwayrdquo Journal of TransportationEngineering vol 126 no 4 pp 313ndash323 2000

[47] M Rogers and M Bruen ELECTRE and Decision SupportMethods and Applications in Engineering and InfrastructureInvestment Kluwer Academic Publishers Boston Mass USA2000

[48] V Belton and T J Stewart Multiple Criteria Decision AnalysisAn Integrated Approach Kluwer Academic Publishers BostonMass USA 2002

[49] B Chang C W Chang and C H Wu ldquoFuzzy DEMATELmethod for developing supplier selection criteriardquo Expert Sys-tems with Applications vol 38 no 3 pp 1850ndash1858 2011

Submit your manuscripts athttpwwwhindawicom

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

MathematicsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Mathematical Problems in Engineering

Hindawi Publishing Corporationhttpwwwhindawicom

Differential EquationsInternational Journal of

Volume 2014

Applied MathematicsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Probability and StatisticsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Mathematical PhysicsAdvances in

Complex AnalysisJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

OptimizationJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

CombinatoricsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Operations ResearchAdvances in

Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Function Spaces

Abstract and Applied AnalysisHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of Mathematics and Mathematical Sciences

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Algebra

Discrete Dynamics in Nature and Society

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Decision SciencesAdvances in

Discrete MathematicsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom

Volume 2014 Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Stochastic AnalysisInternational Journal of

Submit your manuscripts athttpwwwhindawicom

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

MathematicsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Mathematical Problems in Engineering

Hindawi Publishing Corporationhttpwwwhindawicom

Differential EquationsInternational Journal of

Volume 2014

Applied MathematicsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Probability and StatisticsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Mathematical PhysicsAdvances in

Complex AnalysisJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

OptimizationJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

CombinatoricsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Operations ResearchAdvances in

Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Function Spaces

Abstract and Applied AnalysisHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of Mathematics and Mathematical Sciences

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Algebra

Discrete Dynamics in Nature and Society

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Decision SciencesAdvances in

Discrete MathematicsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom

Volume 2014 Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Stochastic AnalysisInternational Journal of