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8/6/2019 A Novel Job Rotation Schedule Model Regarding Posture Variety Solving by a Cellular Genetic Algorithm
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JOURNAL OF COMPUTING, VOLUME 3, ISSUE 6, 2011, ISSN 2151-9617
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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/
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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)
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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
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applied to td to all the
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have similar
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he cycles of tolicies show
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E SIX JOBS
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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
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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,
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cy magnitudimum dissimrker are avaiMLObjectiv
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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
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sets into a co, based on tilarity magnthe body are
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