a novel job rotation schedule model regarding posture variety solving by a cellular genetic...

8/6/2019 A Novel Job Rotation Schedule Model Regarding Posture Variety Solving by a Cellular Genetic Algorithm

1/9

JOURNAL OF COMPUTING, VOLUME 3, ISSUE 6, 2011, ISSN 2151-9617

HTTPS://SITES.GOOGLE.COM/SITE/JOURNALOFCOMPUTING/

WWW.JOURNALOFCOMPUTING.ORG 67

A Novel Job Rotation Schedule ModelRegarding Posture Variety Solving by a

Cellular Genetic AlgorithmHossein Rajabalipour Cheshmehgaz, Habibollah Haron

Abstract Job rotation is a known method that is often used to reduce monotonous workloads on workers with repetitive

workstation-based jobs. Changes in a workers body posture can contribute to reduce the monotony; particularly, while there

exists none or only minimal external force exertion. The purpose of this research is to develop a method to incorporate posture

variety, individually, for each particular body area, into the rotation. This method can increase the possibility of having overall

posture variety during work-hours or shift-by-shift for workers. To this end, fuzzy dissimilarity magnitudes between two jobs

based on linguistic variables are defined and then used to propose new criteria. According to the criteria, an integer-

programming model for the rotation is developed. Owing to the large search space in which to find a very good solution

(approximated optimum solution), a conventional genetic algorithm and a customized cellular genetic algorithm are employed

and compared. In addition to being intuitively logical, the algorithms are examined in a simplified test case with six different

assembly jobs (performing assigned tasks repetitively), and the results indicate that the cellular genetic algorithm can efficiently

find better job rotation schedules to satisfy the criteria.

Index Terms Job rotation schedule; Posture variety; Integer programming; Cellular genetic algorithms

1 INTRODUCTION

ob rotation has been introduced as an administrativecontrol mechanism because human resources are a veryimportant factor in most manufacturing industries [1].

Job rotation has always been a key human ergonomicintervention to reduce stresses on workers [2]. In the rota-tion, the workers are employed in different jobs (or tasks)as long as they have been suitably trained (e.g., cross

training); hence, the company has flexibility in differentsituations. Alongside the general advantages of job rota-tion, its perceived disadvantages include the lack of co-operation from workers, the expense of cross-training,and the difficulties of evaluating the rotation [3].

Practical job rotation research has been directed to-wards physical ergonomic and safety factors [1]. Some ofthe cases include reducing the risk of lower back pain [4],reducing cumulative trauma disorders [3], and minimiz-ing occupational noise exposure [5]. A recent work [6] hassuggested a multi-criteria rotation strategy consideringphysical and mental criteria simultaneously.

In spite of many ergonomic interventions, still someworkers have to work in an awkward posture because ofthe repetitively restricted working environment (e.g., as-sembly lines), and they therefore endure stress to theirmusculoskeletal system [7]. Furthermore, because of mod-ern technology used in industry and the light but monoto-

nous nature of some work (e.g., light assembly tasks), mostconditions in working life, have changed significantly sincethe pre-industrial period. Insufficient activity in physicaltasks is known to have been detrimental short- and long-term effects on health and physical capacity [8-10].

Although, job rotation has been proven to be useful inpractice, but there are no definitive guidelines on how thehealth benefits of a rotation can be evaluated [6]. The effec-tiveness of the job rotations depends on how well a goodrotation strategy is justified and then designed [2]. Accord-ing to some works [2, 11-12], the severity, risk or magni-tude of hazards in jobs (or tasks) or the areas of the work-ers body that are involved in the jobs should be consid-ered in the strategy to design the rotation in the most bene-ficial way. However, the most important challenge in therotation design is still quantification of the level of the se-verity, risk or hazard resulted from the studying job or task[2].

There are many interacting variables in job rotation,which can affect how effective measuring the level of riskor hazard is [2]. According to the literature, many physical

exposure assessments have been developed to measure thelevel of risk, according to the level of repetition, duration,movement, posture and force/load required in different

jobs/tasks [13]. As a part of the job rotation strategy, Car-nahan et al. [11] and Tharmmaphornphilas and Norman[14] used Job Stress Index (JSI) assessment [15] to quantifythe risk level to the back in their own job rotation strategy.

Desai et al. [16] have developed a new rotation scheduleby adapting the results from REBA [17] for both the rightand left sides of the body. Although REBA usually is used

H. Rajabalipour Cheshmehgaz is with the faculty of computer science andinformation systems, Universiti Teknologi Malaysia, 81310, Skudai, Johor,

Malaysia. H. Haron is with the faculty of computer science and information systems,

Universiti Teknologi Malaysia, 81310, Skudai, Johor, Malaysia.

2011 Journal of Computing Press, NY, USA, ISSN 2151-9617

http://sites.google.com/site/journalofcomputing/


2/9

JOURNAL OF COMPUTING, VOLUME 3, ISSUE 6, 2011, ISSN 2151-9617

HTTPS://SITES.GOOGLE.COM/SITE/JOURNALOFCOMPUTING/

WWW.JOURNALOFCOMPUTING.ORG 68

for overall risk assessment, they customized the assessmentto a color-coded matrix to help a company's safety andhealth program generate the job rotation schedule. Theprogram guaranteed schedules with maximum intervalsbetween high-risk tasks in the particular body areas.

Diego-Mas et al. [6] have employed 45 measures and cri-teria to construct job rotation schedules. The criteria usedto assign the workers to the jobs were designed to obtainmaximum diversification of the jobs carried out during theworking hours. The measures of the movements wereevaluated by a simple observational method assigning ascore to the movement items.

In addition to the abovementioned criteria, physical ex-posure variation has been introduced by Winkel [18-19] .According to the work of Mathiassen and Winkel, a phys-ically varied job could consist of a number of complemen-tary work operations loading different structures and func-tions of the organism [20]. Having a high level of the vari-ety in load/force, movements, postures, duration and fre-quency can reduce the risk of jobs [21]. Later, Mathiassen[22] also explained the concepts of diversity and its bene-

fits. Finally, he has emphasized that two complementaryaspects, how much and how fast the exposure changesacross time, must be considered to evaluate the diversityand variation of a job.

A distinctive feature of this work is that the posture va-riety or diversity is considered in generating the job rota-tion schedules. First, the magnitude of dissimilarity be-tween two jobs (or tasks) in terms of elevation (angle) andfrequency of use of particular body areas (e.g., the rightupper arm) are defined. According to these magnitudes,the following objectives are considered in generating therotation schedule: (i) maximizing the total of the dissimilar-ity magnitudes among all jobs assigned to the workers dur-

ing the work hours; and (ii) maximizing the dissimilaritymagnitude between two jobs in two consecutive shifts.Although the objectives can also consider with some re-strictions such as workers capacities, learning and skills,here, no limitation is considered in this study.

In addition, job rotation is a combinational problem [23],and integer programming is a common computing tech-nique to formulate the problem [11, 14]. In this paper, wepropose an integer-programming model based on the crite-ria and the related objectives considered in this paper. Onthe other hand, the given combinatorial expression for the

job rotation scheduling problem has inclined many re-searchers to use meta-heuristic methods such as genetic

algorithms [1]. Because many different rotation scheduleshave almost the same benefits in the objectives, there is adanger of keeping the genetic algorithm in a specific zoneexploring the local optimal solutions, whereas the betterrotations are in different the search space. To deal with theeffects of locality, a new family of evolutionary algorithms,called cellular genetic algorithms [24], are employed.

The rest of paper is structured as follows. The problem

is described through a simple case in Section 2, and its for-mulation is followed in Sectione 3. The cellular genetic al-gorithm used to solve the proposed job rotation model ispresented in Section 4, and the results of a comparison witha conventional genetic algorithm are shown in Section 5.The conclusion and further works are addressed in Section6.

2 PROBLEM DESCRIPTION

Consider a simplified example with six light assemblytasks (i.e., not heavy tasks like manual lifting). Supposethat these repetitive tasks represent six different jobs, andsix workers in three shifts (for example, three hours in eachshift) are available to be part of the job rotation schedule.We assume that there is no precedence or limitation on theassignment of tasks, which means that any worker can per-form the assigned job at any time. The recorded changes in

elevation (angle) of the right upper arm (for a typicalworker) across time are illustrated in Fig. 1. The anglesrelative to the line of gravity and movements of the rightupper arm can be captured by an inclinometer device (e.g.,based on Triaxial accelerometers) [10] at a fixed samplingrate. All samplings are acquired in 400 units of time. Theelevation levels range from 0 to 1800.

Suppose that we have to assign the jobs among theworkers and their shifts in one of two candidate schedulesillustrated in Table 1. For simplification, only the rotationschedules for two workers are shown. Roughly, based onFig. 1, it might be the case that Task 2 and Task 6 are morefrequent than others (based on their signal frequencies),

and Task 3 and Task 5 are more sustainable at a high levelof elevation as compared to the rest (the most elevationlevels between 100 and 180). Thus, Worker 1 and Worker2 have been assigned to more sustainable jobs and morefrequent jobs, respectively, through Schedule 1. On theother hand, in Schedule 2, both workers have more varietyin their right upper arm use.

Fig.1. Signals of right upper arm elevations for six different jobs (or 6assembly tasks)


3/9

JOURN

HTTPS:

WWW.J

Tto thtwo

rameon thIn

tingas lo

Dithe pderstof mmethservimoveidea,phisttion.

Ohowhas bsureworkduratposu

HchanworktionsEVA

ratefor astudiCEVlevels

M

uateillust

L OF COMPUTING,

//SITES.GOOGLE.C

OURNALOFCOMPU

rough the exatwo parameifficulties: (i)

ters; and (ii) he magnitudes.the remaindef the postureg as some ergfferent jobs aosture varietnd that thisthods rangin

ods are availg the numbebetween cer

counting thecated approa

e of the mor

much and hoeen developeVariation An

time spentions) withine intervals) [2nce, an EVes according

s, Mathiassenof EVA. He(called CEV

f change in ealyzing mus

es have emplprovides th

and the mov

athiassens asthe magnitudates the use o

JOB

VOLUME 3, ISSUE 6

M/SITE/JOURNALO

ING.ORG

mple, even wters, elevatiocalculating t

ow the workeof this sectio

variety and tonomic backgnd workplace

and its chanariety can be

from ratherable [22]. Sir of times petain angle semotions durhes require e

convenientfast an exp

by Mathiasslysis (EVA)in uninterrupecified exp2].

illustratesto the expohas also ad

has proposeafter this) b

posure. In gele activity d

oyed EVA fomarginal disment freque

essment, CEes of dissimilf CEVA for t

TABLEROTATION SCHE

, 2011, ISSN 2151-96

FCOMPUTING/

ith equal weigand frequene magnitude

rs must be ass

n, we focus oe frequency orounds are exs have differges; it is impquantified. Asimple to mople methodshour that thtors and a r

ing a specificuipment for

ethods that csure changes

en and Winkessesses the pted sequencesure level ca

how often tsure magnitudressed sever

a continuoused on the i

neral, EVA hring work [2

r posture rectributions of tcies simultan

A, could berity betweene six assembl

1ULES 1 AND 2

17

hts assignedy, there ares of the pa-

igned based

the compu-f its changesplained.nt levels ofrtant to un-ide variety

re advancedinclude ob-extremities

ther simpleperiod. So-ata acquisi-

ould specifyacross time

l [20]. Expo-roportion ofs (sequenceegories (ex-

e exposurede. In lateral modifica-s version ofstantaneous

s been used], but somerdings [25].he elevationously [22].

sed to eval-jobs. Fig. 2tasks men-

tioned earlielevation leTask 6 showthan the oth

(frequencies1 and Taskvation chanbe conclude

3 PROBL

3.1 Lingus

The overallare closely rfive linguistguistic valulows:

The meaover a speciCEVAs plothe linguistihave been cused depenplanner. Ontion level

(change ratevation level180 degreesof the body)

The Disvectors, Distrix. The elethe percentasponding su

Fig.2. CEVA ppresented in F

st. The expoels. Clearly,that these t

ers because th

) at different eare performee rate) in thethat they ha

M MODELIN

ic sets and

analysis of telated to huic variable ses of these v

ing of the lific range. As

is divided ic sets. Althounsidered her

ding on thethe plot, Mare respecti

) for all tasksconsidered ffor the uppe.ec matrix isVecE and Disments of thege of the poib-area.

lots for all six tig. 1

ure levels arthe plots relao tasks are

ey have diffe

levation leveld at low levelright upper ae almost the s

dissimilarity

e elevationan perspectits is appropariables are

nguistic varishown in Figto overlappih equally ov

e, other uneqody area of

ax frequencyely the ma

(or jobs) andor a particularm from th

defined, andVecF, are defimatrix (shots (dots in th

asks (repetitivel

e right uppeted to Taskore frequent

ent rates of c

s. In addition,s of frequencrm; hence, itame frequenc

definition

and the freqe. Thus, theiate [26]. Thetermined a

bles is quan. 3, the areag sub-areas

erlapping inteal intervals cinterest to th

and Max eximum freq

the maximur body areae center of g

two accumulned from thn in Fig. 4)plot) in the

y performed as

69

armand

tasksange

Task(ele-ould

ies.

encyse of

lin-s fol-

tifiedf thenderrvalsan bee jobleva-ency

ele-(e.g.,avity

ationma-

showorre-

jobs)


4/9

JOURNAL

HTTPS://

WWW.JO

Disindivitors, Ddata ifor Tas

Inshowsa partisignedvery hage ofels in t

Witmagniposed

betwearea is

Inall theand fred witthe otTask

with T2 (top-in elevsameother

3.2 Cr

The m job seThereA simthe jovery u

Fig.3. Arates (a

OF COMPUTING, V

ITES.GOOGLE.COM

URNALOFCOMPUTI

ec matricesually calcul

isVecE and Dthe rows an

k j. The vecto

ector (1), fothe percentacular job) suworker rem

igh range. Inthe time thathe medium frh these vectotudes in elehere. In (3)

n Tasks anconsidered n

ur illustrativvalues of thequency are il

h the dissimiers (top-left)in elevatio

ask 2 and 6 imiddle), Tasation as longissimilarityars is analog

iteria and o

ost importantuencing and

are some posle way is tos. Clearly, th

seful. Anothe

overlapping clfrequencies)

OLUME 3, ISSUE 6,

/SITE/JOURNALOFC

G.ORG

or all the tasted, and thesVecE, are cal

d columns ofrs are defined

r instance, te of the samph that the elain within ((2), the valuethe worker cequency (or crs, a fuzzy dation levelnd (4), the

are definew).

case, with sdissimilaritylustrated in Farity magnit, Task 1 has, whereas it

frequency. I2 has more

as Tasks 1, 3,ith Task 2.us.

jectives for

step in the joarranging thible ways to

rotate the wois method ser method mo

assification of e

011, ISSN 2151-961

OMPUTING/

s (or jobs) inthe accum

culated by suthe matrix, reas follows.

e value of 5ele points froevation levelsr fluctuateof

3f shows t

anged the exhange rate).finition of dind frequencissimilarity

d (no matter

x tasks (repetmagnitudes iig. 5. In the bde between

more dissimihas more di

the bar shoissimilarity4 and 5 havehe interpreta

job rotation

b rotation stre jobs for wodetermine therkers randomms too simpes the worke

xposure levels

volved arelated vec-

ming thespectively,

(1)

(2)

for Task jCEVA (inof the as-ithin) thee percent-osure lev-

ssimilarityare pro-

agnitudes

hat body

itive jobs),elevation

r associat-ask 1 and

larity withssimilarity

n for Taskith Task 3

almost thetion of the

tegy is therkers [12].sequence.

ly throughlistic to bes from the

nd change

first workstatin a determimovement mbeen assignefirst workstatthe most app

prevent (or ata worker to(consecutive s

In our stuing criteria in

For each wneck, trunk, eMaximizi

level betwMaximizi

between tMaximizi

in elevati

shifts.Maximizi

in frequenIt is obvio

rapidly as thincrease.

Moreover,body area haelevation leve

In this reseimum varietycover all the

Fig.5. The disin Figure 1)

Fig.4. DisVec m

ion (consideried workstatiust be contin

in the lastion [2]. Hendlicable polici

least to redutwo stressfulhifts).y, as it regar

clude the follorker and eatc.):g the dissimeen two jobsg the dissimo jobs assig

g the total on level amo

g the total ocy among all j

us that the nnumbers of

based on econvex com

l and frequenarch, the ma(MV), two o

criteria menti

imilarity values

atrix and two acc

ng that each tn) to the ne

ued until theorkstation a

erson [27] hass. This polic

ce the probabtasks (or jo

s posture varwing.h body area

ilarity magnissigned to colarity magnited to continuf the dissimilg all jobs as

f the dissimilobs assigned

mber of critbody areas, w

rgonomics ebination priocy.imum lateneerall objectiv

oned above.

between the si

mulated vectors

ask must be dt and so on.last worker

nd moved toproposed onoffers a wa

ility of) assigs) in succes

iety, the sequ

(e.g., upper a

ude in elevantinuous shifude in frequeous shifts.arity magnitsigned in all

arity magnitin all the shift

eria will incrorkers and s

periments,ity weighting

s (ML) andes, are definehe ML objec

tasks (present

70

oneThehasthe

e ofto

ingion

nc-

ms,

tions.ncy

desthe

dess.

aseifts

achfor

ax-d totive

d


5/9

JOURN

HTTPS:

WWW.J

prevsimilMVoneotherabou

3.3 I

Thetheworkthe dimpoobjecbasehandareassimilareamustTo m

progT

andLetdissitwo stion lpresethe tamoel

other

Then

and,

L OF COMPUTING,

//SITES.GOOGLE.C

OURNALOFCOMPU

nts situationr job in twobjective incrorker when

. In the nextt the objective

teger progr

odel is concL and MV

ers to be assiissimilaritiesssible to estitive function

on elevatio, the problemare considerrity must b

(e.g., neck, babe formulateodel the prob

amming moe objectives a

e show alls begin withilarity mag

uccessive shievel and freqnted in (5) aotal dissimilg the all jobsand frequ

,complement

the first objec

VOLUME 3, ISSUE 6

M/SITE/JOURNALO

ING.ORG

in which thconsecutive sases the cha

these tasks arsubsection,

s.

amming mo

erned with tobjectives. Bogned to the jdefined in themate the bes

because then level andcan grow in

ed in the rotspecialized

ck, left upperd according tlem, we prop

el.re defined inhe steps in tterms

itudes betweftsuency, respecd (6). Likew

arity magnitassigned toncy, resp

are also defiry formulas

tive is

, 2011, ISSN 2151-96

FCOMPUTING/

workers arehifts. Simultance of assigne more dissiwe present

del

o main objecth objectivesbs in shifts tprevious subrotation widissimilarity

frequency. Ocomplexity asation. Thereffor each partarm, etc.), as

the entailedose the follow

an incrementhe objective

, repren two tasks

of workertively, on boise,udes, on boorker , in elctively. Ted in (11) are as follows.

17

assigned toeously, the

ing tasks toilar to eachore details

tives, calledrequire theo maximizesection. It ish only oneis defined

n the othermore bodyre, the dis-icular bodythe problemcomplexity.ing integer-

l approach,ormulation.senting theassigned toin in eleva-y area , as

showdy area ,evation lev-o terms,d (12). The

(7)

(8)

(9)

The second

Subject to:

bjective is

UNITS FOR M

TABLE2AGENTIC PRO

ERTIES

71

(13)

(14)


6/9

JOURNAL

HTTPS://

WWW.JO

Eqdissimtwo joinvolvconveequatitotal d

for Wlated i

Eqtotal dtweenbodytion obodymagnidefine

Furshift iConstr

each wequal tshift.weightermschangin terupperchangfor disskeletthe wties/dalso eof theering t

In gsomethe poeachously.areas (the m

Fig.6vario

OF COMPUTING, V

ITES.GOOGLE.COM

URNALOFCOMPUTI

ation (7) indilarities (in elbs assigned te body area

combination for all bod

issimilarity v

rker . The f(10).

ation (13) illissimilaritiesall the jobs asrea . Equatithe values f

areas. Its valtude that is

the MV objether, Constrai

assigned toaint (17) disp

orker in oneo one, whichonstraint (1

s. The weighof elevation) are consides of the spe

arm, back, etdepending o

abled workerl disorder exorkers (e.g.,sabilities) areplains the coweights indiche different beneral, the fichedules bassture varietyorker in all tBecause it ine.g., the neckdel entails a

. A 2-dimensions radiuses

OLUME 3, ISSUE 6,

/SITE/JOURNALOFC

G.ORG

icates the coevation level

Shifts an. Further, Eq

of the valy areas, and (alues betwee

irst objective

strates the co(in elevationsigned to Won (14) calcula

rom (13) in aue representavailable forctive.nt (16) guaraonly one wolays the num

shift that shomeans each) indicates ts show howlevels or f

red critical. Tcified body., that are inn the capacitior those wit

periences). Hage, sex, ansupposed tovex combina

ate the levelsody areas inval step of a j

ed on some cdiversity amhe body areaolves many

, left or rightulti-objectiv

l cellular structu

011, ISSN 2151-961

OMPUTING/

vex combinaand frequenc

of Wouation (8) calues from th9) shows then all the adja

ML is ultimat

nvex combinevel and freqrker considtes the convegeneral viewthe total di

worker . Fi

tees that eacrker. At theber of tasks

ld not be excorker has a jhe convex c

uch risk, coequency (frehe weights arreas, such asolved. The wes of workers

work-relateere, all the cahropometry

be equal. Conion weights.

of importancelved in the rb rotation is titeria, e.g., toong all jobss considered

orkers andupper arm, toptimizatio

re with 3 nodes

tion of the) betweenker thatulates the

previoussum of thecent shifts

ely formu-

tion of theuency) be-ering onlycombina-of the all

ssimilaritynally, (15)

job in oneame way,ssigned to

eded. It isb in everymbinationsidered in

quency ofe specified

the neck,ights may

(especiallymusculo-

pacities ofand abili-straint (19)The valuesof consid-tation.o generatemaximize

ssigned tosimultane-

any bodyunk, etc.),problem.

(cells) with

4CELLULA

The size ofnumber of shnumber of bacceptable sorithms [28] ar

4.1 GENETIGenetic algoreration of a scalled the inprofit valuesjectives fromcalculates thetwo operatiotwo individutime. The reand mutationthat inherit tlation. The reof generationtion does notrion is satisfigence at a splemented thtation approptheir method.the populatio

4.2 Cellular

Cellular genedecentralizedvelop in ovestructure of

(nodes) in n-selection opetion/exploitaFig. 6, a 2-di(cells) and thare shown wneighbors behave only eig

The regenside the cellneighbors. Ficells, the algothe cells nei

tion, recombination, the rethen finally tindividual inshows that thone.

The partvaries the mosolution existneously. Sevcan reduce thzation problestructed acco

GENETIC A

he solutionifts, the numbdy areas conlution is a coe especially ef

Algorithmithms (GAs) aet of solutiontial populati(fitness valuethe job rotatise values is ts (crossover

als selectedpetitive appli

generates ae best charac

generation cos has been peappear in lasted by the poecific level).

e operationsriately, and iHowever, w

n and the fitn

structure of

tic algorithmpopulation i

rlapping neigthe populati

dimensional sration. The stion to find bensional cellir neighborsith differentcause of its rht neighbors (ration proces

individuallst, in the selerithm tries tohbors and d

nation (or crosult moves te result (offsthe cell if thee new indivi

f every probst is the fitnes that maximral methods,e multi-objectm as followsrding to the

GORITHMS

search spaceer of workerssidered. In gemplex probleficient at solv

lways begins (individualsn. All solutis) calculated

on schedule.e fitness funcand mutatioy a selectio

cation of selnew populatiteristics of thntinues untilrformed, evepopulation,

pulation (e.gDiego-Mas etf selection, c

this study,have changess function.

population

(CGA) is a clwhich the

hborhoods.n to a set

pace, and obructure sharetter solution

lar structureare shown. Tradii. For exadius (=2),radius=1).s can be cycli

without anction operatiofind two suiteliver them t

ssover in GAthe mutatioring) will refitness valueual is better t

lem using Gss function. Iizes all the osuch as utili

ive problem t. The fitnessobjectives c

depends onand jobs, andneral, findinm. Genetic aing this probl

y randomly) to the problons are assigaccording tohe function

tion. Afterwa) are applieoperation

ection, crosson of individe previous pa certain nu

if the best sr a specific c

., having conal. [6] have

ossover ande simply fol

d the structu

ass of GA wirial solutionsGAs change

of adjacent

iously affectens the explrapidly [24].

with three ne neighborhomple, hahereas an

ally executey effect on tn from insideble parents fthe next op

). After recon operation,lace the prevof the new rehan the prev

s or CGAsgeneral, no

jectives simty functionso a scalar optfunction isnsidered in

72

thethean

lgo-em.

en-em,nedob-

thatrds,

toachver

ualspu-ber

olu-ite-

ver-im-

u-lowe of

th ade-theells

theora-. Indesods

24d

in-heirtheom

era-

bi-andoussultous

thatbestlta-29],imi-on-this


7/9

JOURN

HTTPS:

WWW.J

stud

4.3

Genetiongenethe c

CGAsaidCGAformupdaare fCGA(FRS(UC)row,LS pdatewithtion,pare

5R

In thstudionlyThetionTableshortonly

MAGN(TASK

MAG

L OF COMPUTING,

//SITES.GOOGLE.C

OURNALOFCOMPU

, as combine

ynchronou

rally, two polrocess in CGation cycle isycle is appli

is called to bo be asynchrs, all individlly created

ted in a sequour main up

as follows:, new rando[24]. LS policwhile otherslicy is less s

policies. For tLS update pothe results a.

SULTS

is section, theed. Six workeone body arevaluation ofevel and fres 3 and 4. Fodecimal nu

Jobs 1 and 3

ITUDES OF THE

) IN FREQUEN

ITUDES OF TH

(TASKS) IN ELE

VOLUME 3, ISSUE 6

M/SITE/JOURNALO

ING.ORG

in (20).

and Async

icies exist in tAs [24]. The

applied to td to all the

e synchronouonous, Asyn-als in the celt the same tntially way i

date policiesine sweep (m sweep (N

sequentiallyupdate themochastic thahis research,licy are emplttained from

problem intrs with six sa, the right uthe dissimilauency produsimplicity, abers. It is i

have similar

TABLE4DISSIMILARITI

Y (CHANGE R

TABLE3DISSIMILARITI

VATION LEVEL

, 2011, ISSN 2151-96

FCOMPUTING/

ronous CG

he cycles of tolicies show

he cells as incells simulta

s, Syn-CGA,CGA. . Althols for next geime, the indn Asyn-CGAof individuaS), fixed ranRS) and uniupdates the

with uniformother async

Syn-CGA andoyed and in tboth and G

oduced in Seifts in one wper arm, areity magnitudced the resulll values wereteresting tofrequencies,

S BETWEEN TH

TE) OF RIGHT U

S BETWEEN T

F RIGHT UPPE

17

e regenera-how the re-

dividuals. Ifeously, the

therwise, isgh, in Syn-neration areviduals are[30]. There

ls in Asyn-dom sweeporm choiceells, row byprobability.ronous up-Asyn-CGA

he next sec-, are com-

ction 2 wasorkday, andconsidered.es in eleva-ts shown inrounded to

remark thatut they are

E SIX JOBS

PPER ARM

E SIX JOBS

ARM

entirely disstable.

All the call the corresize of theprobabilitiesSyn-CGA, aly, the popstructures. Ta generatiouatedany limitati

Some ofshown in Trespondingsolutions obtions are shments of wtives imposdissimilaritworker. Th

elevation leshifts, as lo jobs assigneules. The laMVObjectivsuggested b

To evaluexecuted uption consiste(based on thduring the aation (SD) vgeneration

7.SynCGA

given set ofinding betSynCGA anabove), thethis observa

6CONCLU

As the implbeen explicishould be trepetitive jworkers muas (e.g., uptures (e.g. rthe same frliterature, bvariety andvelopmentorders (espeposture andpolicy alongenvironmen

imilar in ele

iteria are givsponding weopulations, tfor three al

e fixed at 100ulations in bhe regeneratinumber of 1job rotatio

ns.the results oble 5, whichto GA, Asyntained fromown in the rerkers to jobsd on the pro

between tworkers do

el or frequeng as the ma

d to each worgest value oe (eq. 103.27

the Syn-CGte the algorito 100 individ of 100 genee fitness valull executions.alues of the ay generation

is better to fif objectives.ter solutionsd AsynCGAD value of S

tion.

IONS AND F

ementation,tly addresseaken in intebs (e.g., assst usually maer arms, necised, bended,equencies. Bady postures,diversity duf risks and wcially for fewits diversitywith other v

tal and ment

ation level,

n the same iights set to the crossovergorithms, G, 0.9 and 0.1,oth CGAs hon is permitte00. Each of t

schedules,

btained by tconsists of th-CGA and Sach algorithspective sub-(tasks) mostllem and incro consecutinot perform

cy magnitudimum dissimrker are avaiMLObjectiv

14) appeared.

thms further,ual executio

rations. Then) of the popuThe averagell fitness valu, and they ar

d better solulthough thein the ne

are the nearlnCGA (Fig.

URTHER RES

olicies of jobin the liter

preting theseembly-line jintain or chak, trunk, etc.etc.) in the s

sed on thechange rate (fing a job caork-related mer heavy jobsare effectiveariables suchal factors. In

s is shown i

mportance, se value of 1sand the mut, Asyn-CGAespectively.

ave 2-dimensd to proceede algorithmswithout imp

e generationree sub-tableyn-CGA. The

by 100 regetables. The as

satisfy theease the maxie shifts forthe same jo

s) in two adjilarities amoable in the s(eq. 39.4353in the sche

all algorithms, and each e

, the best sollations were snd standardes were calcu

presented i

tions quicklyimprovemet generatiothe same (F

, bottom) sup

EARCH

rotation havature; special

results. Inbs), the assige their bod

) and relatedme positionsuman ergonrequency), po

influence thusculoskeleta). Therefore,in the job rotas force, durthis researc

73

n the

that. Theation

andlear-ionalup toeval-osing

s arecor-best

nera-sign-bjec-

mumeachs (in

acentg allhed-

) anddules

s arexecu-tionsaveddevi-lated

Fig.

for ats ats inig. 7,ports

e notcareany

gnedare-pos-withmicssturee de-l dis-bodyationtion,, we


8/9

JOURNAL

HTTPS://

WWW.JO

addedposturvariettweenered.tationobjectithe intflect adissimshiftssimila

Theexamptions awas aspace;initial

solutiosolutiogenetione ceperforand st

Asprobleresearbly linical extion m

Fig.7.in eac

OF COMPUTING, V

ITES.GOOGLE.COM

URNALOFCOMPUTI

5-scale lingue assessment

and diversijobs in theirhrough thestrategy of thves, the ML aeger prograpolicy that gilarity magnor workers aities among tnew model

le involving snd frequencisimple test chence, gene

test and its re

ns resultingns. New fa

algorithms,llular geneti

ance in terndard deviat

a first approms, our worh lines. (i) Ex

e balancing; (posures (e.g.,odel; and (iii

verage and SDiteration

OLUME 3, ISSUE 6,

/SITE/JOURNALOFC

G.ORG

istic variableand defined

ty, the dissidemands onagnitudes ane job rotationnd MV objectming appro

enerates scheitudes betwend also thee jobs assignas presente

ix assembly js in the rightase, the probic algorithmsults demons

rom convergilies of genwere used.

algorithm,s of the m

ion, in findinch to this newill continu

tending thei) the applicaforce/load) t) improving

of best solution

011, ISSN 2151-961

OMPUTING/

sets into a co, based on tilarity magnthe body are

d the general, we specifiedives, in conjuch. These objdules with then jobs inaximal total

ed to each woand tested

bs with diffeupper arm.lem had a la

were emplrated the nee

ence to globatic algorithhe results shyn-CGA, hast appropriasolutions.

w aspect of je with threeodel for manion of some

o the proposeome experi

by 100 executio

nventionale postureitudes be-as consid-implemen-

two mainction withectives re-e maximalontinuousof the dis-rker.hrough anrent eleva-lthough it

rge searchyed. This

d of better

l optimums, cellularowed thatd the bestte average

b rotationinterestingual assem-ther phys-d job rota-ental heu-

ns shown

ristic rules to

REFERENCE

[1] E.J. Lodree,

and h

Intern

1, 20

[2] D. Rodrick,

hand

[3] D.D. Triggs

strate

2000,

[4] M.B. Frazer,

report

pp. 9

[5] W. Tharmm

model

occup

Hygie

[6] J.A. Diego-M

gener

of Ind

[7] M.K. Chung,

drivin

Journ

pp. 18

THREE BEST

adapt the reg

S

et al., Taxono

uman factors:

ational Journal

9, pp. 39-51.

et al., Job rota

ook, Second ed

and P.M. Kin

gy for hazard c

pp. 32-34.

et al., The ef

ing low back pai

4-919; DOI Doi

aphornphilas,

ling to create jo

ational noise

ne Association

as, et al., A m

ation of job rotat

ustrial Ergonomi

et al., Physiol

tasks in auto

al of Industrial

1-188.

TAJOB ROTATION

GENE

neration cycl

y for integratin

eview and res

f Industrial Erg

tion, The occu

., Taylor&Franci

, Job rotation

ntrol, Professi

fects of job rot

in, Ergonomics,

10.1080/001401

et al., Appl

b rotation sche

exposure.,

ournal, vol. 64,

ulti-criteria gen

ion schedules,

cs, vol. 39, no. 1

ogical workload

obile assembl

Ergonomics, vol

LE5SCHEDULES O

RATION

e in CGAs.

g scheduling th

arch opportuni

nomics, vol. 39

pational ergono

Groups, 2006.

: an administr

onal Safety, vol

ation on the ri

vol. 46, no. 9, 2

303000090161.

ing mathemat

dules for minim

merican Indu

003, pp. 401-4

tic algorithm fo

International Jo

, 2009, pp. 23-3

evaluation of s

jobs, Internat

. 28, no. 3-4, 2

TAINED BY 10

74

eory

ies,

, no.

mics

ative

. 45,

k of

003,

hical

izing

strial

5.

r the

urnal

3.

crew

ional

001,


9/9

JOURN

HTTPS:

WWW.J

[8] L.

Pii 91

[9] I.

[10]

[11]

[12]

[13]

[14]

[15]

[16]

[17]

[18]

[19]

[20]

[21]

[22]

L OF COMPUTING,

//SITES.GOOGLE.C

OURNALOFCOMPU

traker and S.E.

modern w

performanc

1215-1225;

278894.

Balogh, et al.,

production

workload, Ivol. 36,

10.1016/j.er

I. Arvidsson

implementa

air traffic

Ergonomics

10.1016/j.er

B.J. Carna

schedules

Ergonomics

W. Tharmm

method for

Annals of

pp. 251-266

G.C. David,

to risk facto

Occupation

190-199; D

W. Tharm

methodolog

Annals of O

339-360.

D.H. Liles,

and control

pp. 683-693

D. Desai, e

Schedules:

Proc. ASS

Exposition,

L. Mcatamn

for the In

Disorders,

91-99.

J. Winkel,

pilot study,

R.H. Westg

occupationa

Physiology,

S.E. Mathia

Physical

Ergonomics

S.E. Mathia

within and

constrained

8, 200

10.1080/001

S.E. Mathia

exposure:

Applied Er

DOI DOI 10

VOLUME 3, ISSUE 6

M/SITE/JOURNALO

ING.ORG

Mathiassen, In

rk - a nece

?, Ergonomic

DOI Doi 10.108

Increasing the

system: Cons

nternational Jono. 4, 2006,

gon.2004.09.00

, et al., Chan

ion of mouse-b

control, Intern

, vol. 36, no. 7,

gon.2006.03.00

han, et al.,

using optimiza

, vol. 43, no. 4,

phornphilas an

determining p

perations Rese

.

Ergonomic m

rs for work-relat

l Medicine-Oxf

I DOI 10.1093/

maphornphilas

to create ro

perations Rese

t al., A job se

of lifting injury,

.

t al., Using R

Perspective of

Professional

merican Societ

ey and E.N. Co

vestigation of

Applied Ergono

Swelling of the

Human Ergolog

ard, Mwasure

l work situation,

vol. 52, 1988, p

ssen and J. Wi

Load Using

, vol. 34, no. 12,

sen, et al., Var

between indi

industrial work

3, pp.

401303100009

sen, Diversity

hat is it, and

onomics, vol. 3

.1016/j.apergo.2

, 2011, ISSN 2151-96

FCOMPUTING/

creased physica

sity for bette

, vol. 52, no.

/001401309030

degree of au

equences for

rnal of Industripp. 353-36

.

es in physical

sed information

ational Journal

2006, pp. 613-

.

Designing safe

tion and heur

000, pp. 543-56

B.A. Norman,

roper job rotat

arch, vol. 128,

thods for asses

ed musculoskel

rd, vol. 55, no

ccmed/kqi082.

and B.A.

bust job rotatio

rch, vol. 155, n

verity index for

Human Factor,

LA to Generate

Assembly Line

evelopment C

y of Safety Engi

rlett, Rula - a

Work-Related

ics, vol. 24, n

lower leg in sed

, vol. 10, 1981,

ent and evalu

European Jou

. 291-304.

nkel, Quantifyi

Exposure-Vs-

1991, pp. 1455-

iability in mecha

iduals perform

ask, Ergonomi

800-824;

125.

and variation in

hy would we li

7, no. 4, 2006,

006.04.006.

17

l work loads in

health and

10, 2009, pp.

39101

omation in a

the physical

l Ergonomics,; DOI DOI

workload with

technology in

of Industrial

622; DOI DOI

job rotation

istic search,

0.

A quantitative

ion intervals,

no. 1-4, 2004,

sing exposure

tal disorders,

. 3, 2005, pp.

Norman, A

n schedules,

. 1, 2007, pp.

the evaluation

vol. 26, 1984,

Job Rotation

Supervisors,

nference and

eers, 2006.

urvey Method

Upper Limb

. 2, 1993, pp.

entary work-a

pp. 139-149.

tion of load in

nal of Applied

g Variation in

ime Data,

1468.

nical exposure

ing a highly

s, vol. 46, no.

DOI Doi

biomechanical

ke to know?,

pp. 419-427;

[23] C.H.

Opti

Publ

[24] E.

Spri

[25] N.J.

Tool

200[26] H.S.

wor

wor

Erg

[27] C.J.

proc

[28] J.H.

Univ

[29] S.S.

Joh

[30] E.

Algo

App

Hall

ting and intelfaculty of coTeknologi Masome related

new applicatiply chain and

Hbibollah Hatrial computininformation sinterest is softand also CAD

. Papadimitriou

mization: Alg

lications Inc., 19

lba and B. Dor

nger Science+B

Delleman, et al

s for Evaluatio

.Jung and H

load assessme

places, Inte

nomics, vol. 28,

Henderson,

essing, Taylor &

Holland, Adapt

ersity of Michig

Rao, Engineeri

Wiley & Sons,

Alba, et al.,

rithms, Handb

lication, S. Olavi

CRC, 2006, pp.

Hosseinin Mathfrom thehas grasoftwaresity of Tin 2003.mizationtions. Th

ligent businessputer science a

laysia since Junapers in evolut

ns in assemblyhuman factors i

ron is currentlyg and modelingystems, Univercomputing appl/CAM.

and K. St

rithms and

82.

ronsoro, Cellul

usiness Media,

l., Working Pos

and Engineer

. Jung, Esta

nt technique f

rnational Jou

2001, pp. 341-

rgonomics job

Francis, 1992.

tion in Natural

n Press, 1975.

ng Optimization,

Inc., 2009.

Decentralized

ook of Bioins

u and A. Y. Zom

103-120.

Rajabalipour Cematics & CoUiniversity ofduated withengineering fro

echnology (TehHis interests ar

problems witerefore, he has jgroups as a send informatione 2008. Meanwionary computi

line balancing,volved in manu

the head of thein the faculty ofiti Teknologi

ications in robot

iglitz, Combin

Complexity,

r Genetic Algo

LC, 2008.

ures and Move

ing, CRC Press

lishment od

r various task

rnal of In

53.

rotation in

and Artificial Sy

, Theory and Pr

Cellular Evolut

ired Algorithm

aya, eds., Chap

heshmehgaz hapueter Applic

erman, Iran, an.Sc. in Com

m Amirkabir Uan Polytechnic)

multi-objectivh industrial aoined the soft cior researchersystems of Uniile, he has pub

ng methods an

logistic networfacturing proble

department ofcomputer scien

alaysia (UTMic and manufact

75

ational

Dover

ithms,

ents,

LLC,

verall

s and

ustrial

oultry

tems,

actice,

ionary

and

man &

s B.Sc.ationsd thenputer-niver-, Iran,

opti-plica-mpu-

in theversitilished

their

, sup-ms.

indus-e and

). Hisuring,

a novel job rotation schedule model regarding posture variety solving by a cellular genetic...

Documents