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Article: A Hybrid Estimation of Distribution Algorithm for Simulation-Based Scheduling in a Stochastic Permutation Flowshop

TitleA Hybrid Estimation of Distribution Algorithm for Simulation-Based Scheduling in a Stochastic Permutation Flowshop
Authors
Issue Date2015
Citation
Computers & Industrial Engineering, 2015, v. 90, p. 186-196 How to Cite?
AbstractThe permutation flowshop scheduling problem (PFSP) is NP-complete and tends to be more complicated when considering stochastic uncertainties in the real-world manufacturing environments. In this paper, a two-stage simulation-based hybrid estimation of distribution algorithm (TSSB-HEDA) is presented to schedule the permutation flowshop under stochastic processing times. To deal with processing time uncertainty, TSSB-HEDA evaluates candidate solutions using a novel two-stage simulation model (TSSM). This model first adopts the regression-based meta-modelling technique to determine a number of promising candidate solutions with less computation cost, and then uses a more accurate but time-consuming simulator to evaluate the performance of these selected ones. In addition, to avoid getting trapped into premature convergence, TSSB-HEDA employs both the probabilistic model of EDA and genetic operators of genetic algorithm (GA) to generate the offspring individuals. Enlightened by the weight training process of neural networks, a self-adaptive learning mechanism (SALM) is employed to dynamically adjust the ratio of offspring individuals generated by the probabilistic model. Computational experiments on Taillard’s benchmarks show that TSSB-HEDA is competitive in terms of both solution quality and computational performance.
Persistent Identifierhttp://hdl.handle.net/10722/220154

 

DC FieldValueLanguage
dc.contributor.authorWang, K-
dc.contributor.authorChoi, SH-
dc.contributor.authorLu, H-
dc.date.accessioned2015-10-16T06:30:57Z-
dc.date.available2015-10-16T06:30:57Z-
dc.date.issued2015-
dc.identifier.citationComputers & Industrial Engineering, 2015, v. 90, p. 186-196-
dc.identifier.urihttp://hdl.handle.net/10722/220154-
dc.description.abstractThe permutation flowshop scheduling problem (PFSP) is NP-complete and tends to be more complicated when considering stochastic uncertainties in the real-world manufacturing environments. In this paper, a two-stage simulation-based hybrid estimation of distribution algorithm (TSSB-HEDA) is presented to schedule the permutation flowshop under stochastic processing times. To deal with processing time uncertainty, TSSB-HEDA evaluates candidate solutions using a novel two-stage simulation model (TSSM). This model first adopts the regression-based meta-modelling technique to determine a number of promising candidate solutions with less computation cost, and then uses a more accurate but time-consuming simulator to evaluate the performance of these selected ones. In addition, to avoid getting trapped into premature convergence, TSSB-HEDA employs both the probabilistic model of EDA and genetic operators of genetic algorithm (GA) to generate the offspring individuals. Enlightened by the weight training process of neural networks, a self-adaptive learning mechanism (SALM) is employed to dynamically adjust the ratio of offspring individuals generated by the probabilistic model. Computational experiments on Taillard’s benchmarks show that TSSB-HEDA is competitive in terms of both solution quality and computational performance.-
dc.languageeng-
dc.relation.ispartofComputers & Industrial Engineering-
dc.titleA Hybrid Estimation of Distribution Algorithm for Simulation-Based Scheduling in a Stochastic Permutation Flowshop-
dc.typeArticle-
dc.identifier.emailChoi, SH: shchoi@hkucc.hku.hk-
dc.identifier.authorityChoi, SH=rp00109-
dc.identifier.doi10.1016/j.cie.2015.09.007-
dc.identifier.hkuros256072-
dc.identifier.volume90-
dc.identifier.spage186-
dc.identifier.epage196-

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