how to choose between maintenance policies 6

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Maintenance strategy selectionusing AHP and ANP algorithms:

a case studySelim Zaim

Department of Mechanical Engineering, Marmara University, Istanbul, Turkey

Ali Turkylmaz Department of Industrial Engineering, Fatih University, Istanbul, Turkey

Mehmet F. Acar Department of Management, Fatih University, Istanbul, Turkey

Umar Al-TurkiSystems Engineering Department,

King Fahd University of Petroleum & Minerals, Dhahran, Saudi Arabia, and

Omer F. Demirel Department of Industrial Engineering, Fatih University, Istanbul, Turkey

AbstractPurpose The purpose of this paper is to demonstrate the use of two general purposedecision-making techniques in selecting the most appropriate maintenance strategy for organizationswith critical production requirements.Design/methodology/approach The Analytical Hierarchical Process (AHP) and the AnalyticalNetwork Process (ANP) are used for the selection of the most appropriate maintenance strategy in a

local newspaper printing facility in Turkey.Findings The two methods were shown to be effective in choosing a strategy for maintaining theprinting machines. The two methods resulted in almost the same results. Both methods take intoaccount the specic requirements of the organization through its own available expertise.Practical implications The techniques demonstrated in this paper can be used by all types of organizations for selecting and adopting maintenance strategies that have higher impact onmaintenance performance and hence overall business productivity. The two methods are explained ina step-by-step approach for easier adaptation by practitioners in all types of organizations.Originality/value The value of the paper is in applying AHP and ANP decision-makingmethodologies in maintenance strategy selection. These two methods are not very common in the areaof maintenance, and hence add to the pool of techniques utilized in selecting maintenance strategies.

Keywords Maintenance planning, AHP, ANP, Maintenance strategy, Strategy selection, Maintenance,Turkey

Paper type Case study

The current issue and full text archive of this journal is available atwww.emeraldinsight.com/1355-2511.htm

The authors acknowledge the support of both Fatih University and King Fahd University. Theyalso acknowledge the anonymous referees for their constructive comments. They thank SedatKzltunc for his collaboration. They also wish to acknowledge the support provided by uppermanagement of Zaman newspaper.

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Journal of Quality in MaintenanceEngineeringVol. 18 No. 1, 2012pp. 16-29q Emerald Group Publishing Limited1355-2511DOI 10.1108/13552511211226166


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1. IntroductionThe cost of maintenance is becoming increasingly critical with the increasingcompetition in the business environment. The competition is leading to more focus oncost reduction in operations and maintenance. Cost reduction may immediately bereected on pricing and hence, gaining edge over competitors. Maintenance costconstitutes a major portion of total operations cost and hence is central to most costreduction programs. Such programs should be done with care so that otherrequirements such as quality are not sacriced.

Maintenance costs can reach to 15-70 percent of production costs according todifferent sectors (Bevilacqua and Braglia, 2000). Moreover, maintenance directly orindirectly inuences product quality, safety and reliability. Nowadays, maintenance isconsidered as prot contributor and partner for worldclass competitiveness(Waeyenberg and Pintelon, 2002). Rausand (1998) identied the four probableconsequences of failure:

(1) safety of personnel;

(2) environmental impact;(3) production availability; and(4) cost of material loss.

Maintenance is one of the most crucial issues in todays competitive manufacturingenvironment. Machine failure may cause various business related problems such as;missing delivery dates, loss of image and direct and indirect loss of prot and opportunityloss. As such, maintenance should be carefully dealt with in terms of planning,investment, and control. In terms of planning, appropriate maintenance strategies shouldbe selected that are in line with companys global and operational objectives. However,maintenance strategies change rapidly with new options and practices. In fact any change

in operations requires some adjustment or major change in the adopted maintenancestrategy to be compatible with the new requirements. The selection process itself isbecoming crucial for achieving highest performance. Such decisions that highly impacttechnology are usually dealt with in technically founded manner.

The motivation of this work is the existing need for some technical methodologiesfor optimum selection of the most t maintenance strategies. In this research, two of the commonly methods for decision making, namely the Analytical Network Process(ANP) and the Analytical Hierarchy Process (AHP), are used for the selection of thebest maintenance policy. The two methods are simple but powerful in makingdecisions at different business and functional levels under high complex and uncertainconditions.

This paper is organized as follows. First, literature review is written about

maintenance and maintenance selection, then AHP and ANP are overviewed andproposed model is introduced. Last, case study is introduced in this paper. The methodis demonstrated through a case study from local industry in Turkey. Results arediscussed and benets are identied.

2. Literature reviewMaintenance is classied into two main categories: corrective and preventive (Li et al.,2006; Waeyenberg and Pintelon, 2004). Corrective maintenance is performed after

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system failure and preventive maintenance is performed before its failure (Wang,2002). Corrective maintenance, also called breakdown maintenance, is the oldeststrategy in the industry (Waeyenberg and Pintelon, 2002; Mechefske and Wang, 2003;Wang et al., 2007). For large prot margin organizations, this policy can be seen asfeasible strategy (Sharma et al., 2005).

Preventive maintenance, in practice, has two forms; periodic and predictive. Inperiodic maintenance, as the name suggests, maintenance is performed periodically toprevent sudden failure (Wang et al., 2007). This strategy is also called time-basedmaintenance and is used by many rms in the industry following manufacturersrecommendations, which sometimes results in unnecessary maintenance activities.

In predictive maintenance, maintenance decisions are made based on informationcollected from special measurement instruments like sensor systems, monitoringtechniques, vibration monitoring, lubrication analysis and ultrasonic testing (Wanget al., 2007). This strategy is also known as condition-based maintenance.

In addition to these, opportunistic maintenance is used by some large-scaleindustries such as petroleum and petrochemical industries. Bevilacqua and Braglia(2000) dened the opportunistic maintenance as maintenance can lead to the wholeplant being shut down at set times to perform all relevant maintenance interventions atthe same time.

Studies on maintenance systems in practice show that some managers are unawareof the different types of maintenance policies (Shorrocks, 2000; Shorrocks and Labib,2000) and selection methods. Luce (1999), Okumura and Okino (2003) presented themaintenance selection method based on production loss and maintenance cost.Azadivar and Shu (1999) showed the effective methods of selecting appropriate(optimum) maintenance strategies for just in time production systems. Bevilacqua andBraglia (2000) used Analytical Hierarchy Process (AHP) for maintenance selection inan oil renery and they described some features in the selection of maintenance

strategy, such as: economic factors, applicability, costs and safety. Al-Najjar andAlsyouf (2003), Sharma et al. (2005) used fuzzy inference theory and fuzzy multiplecriteria decision making methodology. Moreover, Mechefske and Wang (2003) showeda new method for selecting the optimum maintenance strategy and conditionmonitoring technique. Almeida and Bohoris (1995) developed a new method usingdecision-making theory especially the multi-attribute utility theory. Triantaphyllouet al. (1997) presented AHP model with four maintenance criteria:

(1) cost;(2) reparability;(3) reliability; and(4) availability.

In addition to these, Bertolini and Bevilacqua (2006) proposed a combined goalprogramming and AHP for maintenance selection. Wang et al. (2007) developed a fuzzyAHP model for selection of optimum maintenance strategy.

Labib et al. (1998) developed a model of maintenance decision making, which includesAHP. In the rst stage, criteria are identied and then in the second stage AHP isapplied. Last, machines are ranked according to their importance. Arunraj and Maiti(2010) used AHP and goal programming for maintenance policy selection according to

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risk of failure and cost of maintenance in a chemical factory. They concluded that if riskis chosen as a criterion, predictive maintenance is preferred policy over periodicmaintenance. Similarly, if cost is chosen as a criterion, corrective maintenance ispreferred. Nevertheless, if both risk and cost are considered, AHP-GP results show thatpredictive maintenance and corrective maintenance are best for high-risk equipment andlow-risk equipment, respectively. Labib (2004) also developed a model for maintenancepolicy selection using a computerized maintenance management system. In this study,fuzzy logic and AHP are used. HajShirmohammadi and Wedley (2004) used an AHPmodel for maintenance management for centralization and decentralization. Centralizedsystem means that all maintenance systems are managed from a centrally administeredlocation. However, decentralized system implies that each production area manages itsown maintenance systems.

Shyjith et al. (2008) developed a model using AHP and TOPSIS for maintenanceselection in textile industry and then Ilangkumaran and Kumanan (2009) integratedfuzzy AHP and TOPSIS algorithm to select the maintenance policy for textile industry.

It is clear from the literature that AHP has proven success in maintenance strategyselection, as it did for many other decision-making problems. As such, it was selectedto be the major tool in this paper.

3. Theoretical background3.1 The analytic hierarchy process (AHP) method The analytic hierarchy process (AHP) methodology, which was developed by Saaty(1980), is a powerful tool in solving complex decision problems. The AHP helps theanalysts organize the critical aspects of a problem into a hierarchical structure similarto a family tree. By reducing complex decisions to a series of simple comparisons andrankings, then synthesizing the results, the AHP not only helps the analysts arrive atthe best decision, but also provides a clear rationale for the choices made (Chin et al.,

1999).In the AHP approach, the decision problem is structured hierarchically at differentlevels with each level consisting of a nite number of decision elements. The upperlevel of the hierarchy represents the overall goal, while the lower level consists of allpossible alternatives. One or more intermediate level embody the decision criteria andsub-criteria (Partovi, 1994).

3.2 The analytical network process (ANP)The ANP method is an improved version of AHP method and it is more accurate withmany complicated models in which many criteria feedback and interrelations amongcriteria are used.

The ANP method evaluates all the relationships systematically by adding allinteractions, interdependences, and feedbacks in decision-making systems. Thepowerful side of our model is to represent the decision-making problem that involvesmany complicated relationships easily. This technique does not only enable thepair-wise comparisons of the sub-criteria under main criteria, but also enables us tocompare independently all the interacting sub-criteria.

Decision making problems that occur in rms cannot be explained by onlyhierarchical structures. The criteria and alternatives in a problem can haveinteractions. At these circumstance, complicated analyzes can be necessary to nd


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out the weights of all components. ANP technique is used for such as that kind of problems and it is based on pairwise comparisons as it is in AHP. For pairwisecomparisons the 1-9 scale of Saaty (1980) is used in Table I. In ANP model all thecomponents and relationships are dened and the relationships are determined astwo-way interactions. In the model the network structure is used and all therelationships in a cluster (that is relationships among sub-criteria in a cluster) andrelationships between sub-criteria under different clusters are considered. Because of such relationships, the ANP method is useful for getting more accurate and effectiveresults is a complex and crucial decision making problems.

In ANP method there are three matrix analyses; super matrix, weighted supermatrix and limit matrix. The super matrix provides relative importance of allcomponents and weighted super matrix nds out the value that is obtained by thesuper matrix values and the value of each cluster. In the limit matrix, the constantvalues of each value are determined by taking the necessary limit of the weightedsuper matrix. The results of the decision-making problem, is gained from the limitmatrix scores. It is important to value the criteria and alternatives by the experts inorder to get more consistent and reliable results.

4. Case studyThe method proposed for selecting maintenance strategy is based on a hierarchicalmodel composed of a set of criterion and sub-criterion as developed by Saaty. BothAHP and ANP methods are demonstrated by the following case study from thenewspaper printing industry. The use of the two methods is reported along with theirresulting solutions.

One of the most selling newspapers in Turkey, ZAMAN, is the subject of the casestudy in this research. It publishes national and international news in the elds of politics, business, economics, arts, cultures, sports, etc. It is published seven days aweek with approximately 30 pages in addition to publishing TODAYSZAMAN, themost circulated English newspaper in Turkey, and special supplements in weekendsand special occasions. It won different awards in several design competitions.

To meet its publication daily schedule, machines and equipments in its printinghouse must be kept continuously ready for production which puts high pressure onoperations and maintenance. Thus, maintenance is highly crucial for this rm andtherefore selected to be the focus of this paper. The objective of this study is to selectthe best maintenance strategy that meets the operations objectives. Three alternativemaintenance strategies are considered, these are; corrective, periodic (time-based) andpredictive (condition-based) maintenance policies. Opportunistic maintenance is notconsidered because long time shut down of equipments and machines is not expected.

Description Numerical value(s)

Equally importance 1Moderately importance 3Strongly importance 5Very strongly importance 7Extremely importance 9Intermediate values 2,4,6,8

Table I.Saatys 1-9 scale for AHPpreference

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Four selection criteria are considered. These are:(1) added value;(2) cost;

(3) safety; and(4) implementation.

Moreover, different sub-criteria are added to the model. According to the proposedmodel, problems, criteria and alternatives are found and these are described in thesteps below.

Step 1: Form a focus group composed of key managers and engineersThe purpose is to determine and examine current problems and their impact at thebusiness level of the company. In this case, a project team is established. The projectteam is composed of ve managers from production planning and control andmaintenance in addition to some experts from several universities.

Step 2: Evaluate the problemsFacilitate a focus group meeting to identify issues and problems related to maintenanceand their possible causes mapped into categories and subcategories. The group alsoconstructs maintenance method selection criteria and sub-criteria. In this case, the teamidentied several issues related to maintenance in ZAMAN printing house. Some of the issues found to be crucially related to maintenance strategy. One of the mostimportant problems is the rms image. If machines breakdown and production stops,newspaper may not be issued and this situation negatively affects the image of therm. Another problem is found to be cost. In case of shutdown, the rm may need tooutsource the printing of the newspaper and this causes an extra cost for the rm.

Step 3: Determine the alternative strategiesSome maintenance strategies might not be suitable for a certain organization. Thatstrategy can be eliminated by the focus group with more attention and analysis may beconducted for feasible strategies. Throughout the discussions held with the formedgroup members, three possible alternative maintenance strategies are identied, theseare; corrective, periodic and predictive maintenance policies. Opportunisticmaintenance is not considered because long time shut down of equipments andmachines is not expected.

Step 4: Construct a hierarchical model Using criteria, sub-criteria and alternatives, a hierarchical model is constructed toapply AHP and ANP algorithms. Then relationship among criteria and sub-criteria aredetermined and reected in the hierarchical model. Maintenance strategy selectioncriteria were determined based on the review of prior literature and semi-structuredinterviews undertaken with 22 managers from relevant departments includingpurchasing, manufacturing, quality assurance and maintenance. Figure 1 shows thehierarchical structure of the maintenance strategy selection problem, which includesfour levels. The top level of the hierarchy represents the ultimate goal of the problem,while the second level of the hierarchy consists of four main maintenance policyselection criteria, which are namely: added value, cost, safety, and implementation.


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These criteria are decomposed into various sub-criteria that may affect the managersdecision for a particular maintenance policy. Finally, the bottom level of the hierarchyrepresents the alternative maintenance policies. Each selection criterion in the treediagram is briey described below.

The sub-criteria for each main criteria are identied as follows:(1) Value adding is viewed in four possible dimensions (sub-criteria) as follows:

. On time delivery: During the production process, some machines may failcausing delays in order delivery.

. Prot : Excessive failures increase maintenance cost and decrease in prot.Material paper and production time wastages are examples of direct poormaintenance costs.

. Quality: Some machine failures may cause drop in product quality showingas damaged paper or unreadable text.

. Image : The image of the rm is largely affected by production andmaintenance performance. Late deliveries, low quality printing, shortage inquantities are some examples causing image damage.

(2) The cost criterion includes the following:. Hardware: To apply predictive maintenance, the rm may need to acquire

some new machines or equipments.. Software: Different software may be required to evaluate information which

is obtained from equipments used for predictive maintenance.. Training: Technicians or managers may be required to go through special

training for effective use of equipments and software that are used inpredictive maintenance.

. Inventory of spare parts : Maintenance strategies, especially correctivemaintenance, some spare parts should be available in inventory. The cost of holding spare parts adds to the overall maintenance cost.

Figure 1.AHP and ANP scores

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. Cost of advising and consulting: For corrective and periodic maintenancestrategies, the rm may need some special maintenance experts to plan andcontrol maintenance operations.

These costs are mostly necessary regardless of the type of maintenance strategyadopted whether corrective, periodic or predictive. However, the costing elements varyin amount between strategies:

(3) The safety criterion consists of the following:. Internal environment : Safety policies and procedures maintains healthy

working environment. Interruptions in operations due to failure may form asource of hazard to people and the whole internal environment.

. External environment : Safety outside the factory is another crucial element,especially for nuclear or chemical plants. In case of re or chemical spills inthe printing house may cause unrecoverable damage to the surroundingenvironment.

. Personnel : Last, some breakdowns and/or maintenance activities maydirectly or indirectly harm workers. Therefore, it is essential to seek theiropinion about the possible maintenance practices.

(4) The implementation criterion includes the following:. Technology: Technology is an important for predictive maintenance, because

there are no special equipments for some machines to apply condition-basedmaintenance.

. Desire of workers : Some of workers may not want to predictive maintenance,because workers do some extra duties in condition-based maintenance.

. Desire of top management: Sometimes top managers do not want to applypredictive maintenance, because its setup cost which is sourced from buying

of hardware and software is high.. Decision of service company: There are lots companies which supply

maintenance service as business for other companies, so this is a criterion forrms.

Based on the identied criteria and sub-criteria, a hierarchical model is constructed andrelations are determined for our case study. The model is shown in Figure 2.

Step 5: Pair wise comparison among criteria and sub-criteriaPair wise comparisons are done among related criteria and sub-criteria following thescale suggested by Saaty. A special questionnaire form is used to complete thepair-wise comparison matrix. In this comparison, criteria, sub criterias are used forcomparing alternative maintenance strategies by experts in the eld.

Step 6: Applying AHP and ANP algorithmsThese algorithms give weight to each alternative based on which the best strategy ischosen.

In the AHP approach, the weights of the criteria and the scores of the alternatives,which are called local priorities, are considered as decision elements in the second stepof the decision process. The decision-maker is required to provide his preferences by


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pairwise comparisons, with respect to the weights and scores. The values of theweights vi and scores r ij are elicited from these comparisons and represented in adecision table. The last step of the AHP aggregates all local priorities from the decisiontable by a weighted sum of the type:

R j i

X vi r ij

The global priorities R j thus obtained are nally used for ranking of the alternativesand selection of the best one.

In the ANP approach, two matrices are calculated; the weighted super matrix andthe limit matrix. The weighted super matrix permits a resolution of theinterdependencies that exist among the components of a system. It is a partitionedmatrix where each sub-matrix is composed of a set of relationships between and withinthe levels, as represented by the model. The entries of the super matrix are importedfrom the pair-wise comparison matrices of interdependencies. Since there are 20 suchpairwise comparisons matrices, one for each interdependent criterion, the super matrixcontains 20 non-zero columns. The weighted super matrix is obtained by multiplyingall the elements in a component of the unweighted super matrix by the correspondingcluster weight. In other words, the values in the cluster matrix are used to weight the

Figure 2.The hierarchical model formaintenance strategyselection criteria

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unweighted super matrix by multiplying the value in the cell of the cluster matrixtimes the value in each cell in the component of the unweighted super matrix toproduce the weighted super matrix. The resulting weighted super matrix is shown inTable II. The limit super matrix is obtained by raising the weighted super matrix to thepower 2k 1 where k is an arbitrarily large number, allows convergence of theinterdependent relationships. When the column of numbers is the same for everycolumn, the limit matrix has been reached and the matrix multiplication process ishalted. The limit super matrix for the Model is shown in Table III.

Step 7: PrioritizationIt simply means listing alternatives in descending order of their weights according toboth AHP and ANP algorithms.

The AHP algorithm resulted in the following ranking (best to worst) of maintenancestrategies; predictive, periodic and corrective maintenance respectively and using theANP algorithm; the resulting strategy ranking (best to worst) is; predictive, periodic

and corrective maintenance, respectively as shown in Figure 1.

Step 8: Compare results and make the decisionThe two solutions, AHP and ANP, are compared and evaluated by experts to make thebest decision.

The analysis clearly shows that predictive maintenance is to be the best strategy byboth AHP and ANP methods. However, in the real situation, ZAMAN is using periodicmaintenance for maintaining its printing house. In fact, predictive maintenance is shownby both methods to cause unnecessary expenditure for ZAMAN. This is notrecognized by the rm since the maintenance effectiveness is quite high on the expenseof efciency in resource utilization. Furthermore, technicians and experts areoccasionally interfering with the maintenance operations based on their intuitionsbefore the time of periodic maintenance. This can be seen as predictive maintenance,hence, in fact, both predictive and periodic maintenance are used in ad-hoc basis.

Currently there are plans underway in ZAMAN to adopt an ERP system forplanning and controlling maintenance operations and at the same time be used toestimate and report the cost of maintenance.

5. ConclusionIn this research, some criteria are determined about maintenance selection andaccording to these criteria, AHP and ANP models are constituted. Three maintenancepolicies are considered; these are corrective, periodic (time-based) and predictive(condition-based) maintenance. Moreover, with the help of experts and engineers, theseAHP and ANP models are used for machines in printing house of the daily newspaper,ZAMAN. At the end of these analyses, weights of three different maintenancepolicies are determined. This research shows that predictive maintenance is the mostsuitable maintenance policy for this newspaper rm in both AHP and ANP analyses.In the future, AHP and ANP models can be used with fuzzy logic for maintenanceselection and new models can be done for rms that are in other sectors. Furtherresearch on testing other decision making tools including fuzzy logic, may be done.This study can also be extended by adding a new selection factor to the existing model.


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0 . 0 4

2

0 . 1

4 4

0 . 0

0 0

0 . 0

0 0

0 . 0

0 0

0 . 0 0 0

0 . 0

0 0

0 . 0

0 0

0 . 0

0 0

0 . 0

0 0

0 . 0

0 0

0 . 0

0 0

0 . 0

0 0

0 . 0

0 0

0 . 0

0 0

0 . 0

0 0

0 . 0

0 0

C 5

0 . 0

1 5

0 . 0

9 7

0 . 2 8

6

0 . 0

0 0

0 . 0

0 0

0 . 0

0 0

0 . 0

0 0

0 . 0 0 0

0 . 0

0 0

0 . 0

0 0

0 . 0

0 0

0 . 0

0 0

0 . 0

0 0

0 . 0

0 0

0 . 0

0 0

0 . 0

0 0

0 . 0

0 0

0 . 0

0 0

0 . 0

0 0

I 1

0 . 1

0 0

0 . 0

0 0

0 . 0 0

0

0 . 0

0 0

0 . 0

0 0

0 . 0

0 0

0 . 0

0 0

0 . 0 0 0

0 . 0

0 0

0 . 0

0 0

0 . 0

0 0

0 . 0

0 0

0 . 0

0 0

0 . 0

0 0

0 . 0

0 0

0 . 0

0 0

0 . 0

0 0

0 . 0

0 0

0 . 0

0 0

I 2

0 . 3

0 0

0 . 0

0 0

0 . 0 0

0

0 . 1

7 8

0 . 0

0 0

0 . 0

0 0

0 . 0

0 0

0 . 0 0 0

0 . 0

0 0

0 . 0

0 0

0 . 0

0 0

0 . 0

0 0

0 . 0

0 0

0 . 0

0 0

0 . 0

0 0

0 . 0

0 0

0 . 0

0 0

0 . 0

0 0

0 . 0

0 0

I 3

0 . 1

0 0

0 . 0

0 0

0 . 0 0

0

0 . 0

0 0

0 . 0

0 0

0 . 0

0 0

0 . 0

0 0

0 . 0 0 0

0 . 0

0 0

0 . 0

0 0

0 . 0

0 0

0 . 0

0 0

0 . 0

0 0

0 . 0

0 0

0 . 0

0 0

0 . 0

0 0

0 . 0

0 0

0 . 0

0 0

0 . 0

0 0

Table II.Weighted super matrix

JQME18,1

26


12/14

P 1

P 2

P 3

G

S 1

S 2

S 3

V 1

V 2

V 3

V 4

C 1

C 2

C 3

C 4

C 5

I 1

I 2

I 3

P 1

0 . 2

2 7

0 . 2

2 7

0 . 2 2

7

0 . 2

2 7

0 . 2

2 7

0 . 2

2 7

0 . 2

2 7

0 . 2 2 7

0 . 2

2 7

0 . 2

2 7

0 . 2

2 7

0 . 2

2 7

0 . 2

2 7

0 . 2

2 7

0 . 2

2 7

0 . 2

2 7

0 . 2

2 7

0 . 2

2 7

0 . 2

2 7

P 2

0 . 1

4 6

0 . 1

4 6

0 . 1 4

6

0 . 1

4 6

0 . 1

4 6

0 . 1

4 6

0 . 1

4 6

0 . 1 4 6

0 . 1

4 6

0 . 1

4 6

0 . 1

4 6

0 . 1

4 6

0 . 1

4 6

0 . 1

4 6

0 . 1

4 6

0 . 1

4 6

0 . 1

4 6

0 . 1

4 6

0 . 1

4 6

P 3

0 . 1

2 7

0 . 1

2 7

0 . 1 2

7

0 . 1

2 7

0 . 1

2 7

0 . 1

2 7

0 . 1

2 7

0 . 1 2 7

0 . 1

2 7

0 . 1

2 7

0 . 1

2 7

0 . 1

2 7

0 . 1

2 7

0 . 1

2 7

0 . 1

2 7

0 . 1

2 7

0 . 1

2 7

0 . 1

2 7

0 . 1

2 7

G

0 . 0

0 0

0 . 0

0 0

0 . 0 0

0

0 . 0

0 0

0 . 0

0 0

0 . 0

0 0

0 . 0

0 0

0 . 0 0 0

0 . 0

0 0

0 . 0

0 0

0 . 0

0 0

0 . 0

0 0

0 . 0

0 0

0 . 0

0 0

0 . 0

0 0

0 . 0

0 0

0 . 0

0 0

0 . 0

0 0

0 . 0

0 0

S 1

0 . 0

0 0

0 . 0

0 0

0 . 0 0

0

0 . 0

0 0

0 . 0

0 0

0 . 0

0 0

0 . 0

0 0

0 . 0 0 0

0 . 0

0 0

0 . 0

0 0

0 . 0

0 0

0 . 0

0 0

0 . 0

0 0

0 . 0

0 0

0 . 0

0 0

0 . 0

0 0

0 . 0

0 0

0 . 0

0 0

0 . 0

0 0

S 2

0 . 0

0 0

0 . 0

0 0

0 . 0 0

0

0 . 0

0 0

0 . 0

0 0

0 . 0

0 0

0 . 0

0 0

0 . 0 0 0

0 . 0

0 0

0 . 0

0 0

0 . 0

0 0

0 . 0

0 0

0 . 0

0 0

0 . 0

0 0

0 . 0

0 0

0 . 0

0 0

0 . 0

0 0

0 . 0

0 0

0 . 0

0 0

S 3

0 . 0

0 0

0 . 0

0 0

0 . 0 0

0

0 . 0

0 0

0 . 0

0 0

0 . 0

0 0

0 . 0

0 0

0 . 0 0 0

0 . 0

0 0

0 . 0

0 0

0 . 0

0 0

0 . 0

0 0

0 . 0

0 0

0 . 0

0 0

0 . 0

0 0

0 . 0

0 0

0 . 0

0 0

0 . 0

0 0

0 . 0

0 0

V 1

0 . 0

0 0

0 . 0

0 0

0 . 0 0

0

0 . 0

0 0

0 . 0

0 0

0 . 0

0 0

0 . 0

0 0

0 . 0 0 0

0 . 0

0 0

0 . 0

0 0

0 . 0

0 0

0 . 0

0 0

0 . 0

0 0

0 . 0

0 0

0 . 0

0 0

0 . 0

0 0

0 . 0

0 0

0 . 0

0 0

0 . 0

0 0

V 2

0 . 0

0 0

0 . 0

0 0

0 . 0 0

0

0 . 0

0 0

0 . 0

0 0

0 . 0

0 0

0 . 0

0 0

0 . 0 0 0

0 . 0

0 0

0 . 0

0 0

0 . 0

0 0

0 . 0

0 0

0 . 0

0 0

0 . 0

0 0

0 . 0

0 0

0 . 0

0 0

0 . 0

0 0

0 . 0

0 0

0 . 0

0 0

V 3

0 . 0

0 0

0 . 0

0 0

0 . 0 0

0

0 . 0

0 0

0 . 0

0 0

0 . 0

0 0

0 . 0

0 0

0 . 0 0 0

0 . 0

0 0

0 . 0

0 0

0 . 0

0 0

0 . 0

0 0

0 . 0

0 0

0 . 0

0 0

0 . 0

0 0

0 . 0

0 0

0 . 0

0 0

0 . 0

0 0

0 . 0

0 0

V 4

0 . 0

0 0

0 . 0

0 0

0 . 0 0

0

0 . 0

0 0

0 . 0

0 0

0 . 0

0 0

0 . 0

0 0

0 . 0 0 0

0 . 0

0 0

0 . 0

0 0

0 . 0

0 0

0 . 0

0 0

0 . 0

0 0

0 . 0

0 0

0 . 0

0 0

0 . 0

0 0

0 . 0

0 0

0 . 0

0 0

0 . 0

0 0

C 1

0 . 0

5 7

0 . 0

5 7

0 . 0 5

7

0 . 0

5 7

0 . 0

5 7

0 . 0

5 7

0 . 0

5 7

0 . 0 5 7

0 . 0

5 7

0 . 0

5 7

0 . 0

5 7

0 . 0

5 7

0 . 0

5 7

0 . 0

5 7

0 . 0

5 7

0 . 0

5 7

0 . 0

5 7

0 . 0

5 7

0 . 0

5 7

C 2

0 . 0

3 6

0 . 0

3 6

0 . 0 3

6

0 . 0

3 6

0 . 0

3 6

0 . 0

3 6

0 . 0

3 6

0 . 0 3 6

0 . 0

3 6

0 . 0

3 6

0 . 0

3 6

0 . 0

3 6

0 . 0

3 6

0 . 0

3 6

0 . 0

3 6

0 . 0

3 6

0 . 0

3 6

0 . 0

3 6

0 . 0

3 6

C 3

0 . 1

7 8

0 . 1

7 8

0 . 1 7

8

0 . 1

7 8

0 . 1

7 8

0 . 1

7 8

0 . 1

7 8

0 . 1 7 8

0 . 1

7 8

0 . 1

7 8

0 . 1

7 8

0 . 1

7 8

0 . 1

7 8

0 . 1

7 8

0 . 1

7 8

0 . 1

7 8

0 . 1

7 8

0 . 1

7 8

0 . 1

7 8

C 4

0 . 0

6 2

0 . 0

6 2

0 . 0 6

2

0 . 0

6 2

0 . 0

6 2

0 . 0

6 2

0 . 0

6 2

0 . 0 6 2

0 . 0

6 2

0 . 0

6 2

0 . 0

6 2

0 . 0

6 2

0 . 0

6 2

0 . 0

6 2

0 . 0

6 2

0 . 0

6 2

0 . 0

6 2

0 . 0

6 2

0 . 0

6 2

C 5

0 . 0

5 4

0 . 0

5 4

0 . 0 5

4

0 . 0

5 4

0 . 0

5 4

0 . 0

5 4

0 . 0

5 4

0 . 0 5 4

0 . 0

5 4

0 . 0

5 4

0 . 0

5 4

0 . 0

5 4

0 . 0

5 4

0 . 0

5 4

0 . 0

5 4

0 . 0

5 4

0 . 0

5 4

0 . 0

5 4

0 . 0

5 4

I 1

0 . 0

2 3

0 . 0

2 3

0 . 0 2

3

0 . 0

2 3

0 . 0

2 3

0 . 0

2 3

0 . 0

2 3

0 . 0 2 3

0 . 0

2 3

0 . 0

2 3

0 . 0

2 3

0 . 0

2 3

0 . 0

2 3

0 . 0

2 3

0 . 0

2 3

0 . 0

2 3

0 . 0

2 3

0 . 0

2 3

0 . 0

2 3

I 2

0 . 0

6 8

0 . 0

6 8

0 . 0 6

8

0 . 0

6 8

0 . 0

6 8

0 . 0

6 8

0 . 0

6 8

0 . 0 6 8

0 . 0

6 8

0 . 0

6 8

0 . 0

6 8

0 . 0

6 8

0 . 0

6 8

0 . 0

6 8

0 . 0

6 8

0 . 0

6 8

0 . 0

6 8

0 . 0

6 8

0 . 0

6 8

I 3

0 . 0

2 3

0 . 0

2 3

0 . 0 2

3

0 . 0

2 3

0 . 0

2 3

0 . 0

2 3

0 . 0

2 3

0 . 0 2 3

0 . 0

2 3

0 . 0

2 3

0 . 0

2 3

0 . 0

2 3

0 . 0

2 3

0 . 0

2 3

0 . 0

2 3

0 . 0

2 3

0 . 0

2 3

0 . 0

2 3

0 . 0

2 3

Table III.Limit matrix


27


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In addition, other well known multi-criteria methods such as TOPSIS, ELECTRE canbe used to compare the results of this work.

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Saaty, T.L. (1980), The Analytic Hierarchy Process , McGraw-Hill, New York, NY.Sharma, R.K., Kumar, D. and Kumar, P. (2005), FLM to select suitable maintenance strategy in

process industries using MISO model, Journal of Quality in Maintenance Engineering ,Vol. 11 No. 4, pp. 359-74.

Shorrocks, P. (2000), Selection of the most appropriate maintenance model using a decisionsupport framework, unpublished report, UMIST, Manchester.

Shorrocks, P. and Labib, A.W. (2000), Towards a multimedia based decision support system forword class maintenance, Proceedings of the 14th ARTS (Advances in ReliabilityTechnology Symposium), IMechE, University of Manchester, Manchester .

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Triantaphyllou, E., Kovalerchuk, B., Mann, L. and Knapp, G.M. (1997), Determining the mostimportant criteria in maintenance decision making, Journal of Quality in Maintenance Engineering , Vol. 3 No. 1, pp. 16-28.

Waeyenbergh, G. and Pintelon, L. (2002), A framework for maintenance concept development, International Journal of Production Economics , Vol. 77, pp. 299-313.

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Wang, L., Chu, J. and Wu, J. (2007), Selection of optimum maintenance strategies based on afuzzy analytical hierarchy process, International Journal of Production Economics ,Vol. 107, pp. 151-63.

Further readingBhushan, N. and Rai, K. (2004), Strategic Decision Making: Applying the Analytical Hierarchy

Process , Springer-Verlag, London.Saaty, T.L. (2001), Decision Making with Dependence and Feedback: Analytic Network Process ,

RWS Publications, Pittsburgh, PA.Waeyenbergh, G. and Pintelon, L. (2004), Maintenance concept development: a case study,

International Journal of Production Economics , Vol. 89, pp. 395-405.

Corresponding authorOmer F. Demirel can be contacted at: [email protected]


29

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