A simulation-based differential evolution algorithm for stochastic parallel machine scheduling with operational considerations

We consider a parallel machine scheduling problem with the objective of minimizing two types of costs: the cost related to production operations and the cost related to due date performances. The former could be reduced by reasonable settings of the operational variables (e.g., the number of workers...

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Bibliographic Details
Published in:International transactions in operational research Vol. 20; no. 4; pp. 533 - 557
Main Authors: Zhang, Rui, Song, Shiji, Wu, Cheng
Format: Journal Article
Language:English
Published: Oxford Blackwell Publishing Ltd 01.07.2013
Pergamon
Subjects:
Algorithms
Computer simulation
Costs
differential evolution
Job shops
Mathematical analysis
Mathematical models
operational cost
Operations research
Optimization
parallel machine scheduling
Production scheduling
Scheduling
Searching
Simulation
simulation optimization
Studies
ISSN:0969-6016, 1475-3995
Online Access:Get full text
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Summary:We consider a parallel machine scheduling problem with the objective of minimizing two types of costs: the cost related to production operations and the cost related to due date performances. The former could be reduced by reasonable settings of the operational variables (e.g., the number of workers, the frequency of maintenance), while the latter could be reduced by appropriate scheduling of the production process. However, the optimization of both targets is significantly complicated by the influence of human factors that play a dominant role in real‐world manufacturing systems. To cope with this issue, a simulation‐based optimization framework is adopted in this paper for obtaining high‐quality robust solutions to the integrated scheduling problem. Meanwhile, differential evolution, a metaheuristic algorithm based on swarm intelligence, is applied for a systematic search of the huge solution space. Finally, numerical computations are conducted to verify the effectiveness of the proposed approach. Sensitivity analysis and practical implications are also presented.
Bibliography:ArticleID:ITOR12011
National Natural Science Foundation of China - No. 61104176; No. 61273233
Social Sciences Research Project of Jiangxi Provincial Education Department - No. GL1236
ark:/67375/WNG-B91NBTHQ-4
Science and Technology Project of Jiangxi Provincial Education Department - No. GJJ12131
istex:DCDB7C6898A60C1F309074E28009B0B1EE2FC5A4
Educational Science Research Project of Jiangxi Province - No. 12YB114
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ISSN:0969-6016
1475-3995
DOI:10.1111/itor.12011