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Conference Paper: A decomposition-based algorithm for flexible flow shop scheduling with stochastic processing times

TitleA decomposition-based algorithm for flexible flow shop scheduling with stochastic processing times
Authors
KeywordsBack propagation network
Decomposition
Flexible flow shop
Neighbouring K-means clustering algorithm
Stochastic processing times
Issue Date2009
PublisherInternational Association of Engineers.
Citation
The International Conference on Systems Engineering and Engineering Management 2009 of the World Congress on Engineering and Computer Science (WCECS 2009), San Francisco, CA., 20-22 October 2009. In Proceedings of WCECS, 2009, v. 2, p. 1050-1060 How to Cite?
AbstractSince real manufacturing is dynamic and tends to suffer a wide range of uncertainties, research on production scheduling with uncertainty has received much more attention recently. Although various approaches have been investigated on the scheduling problem with uncertainty, this problem is still difficult to be solved optimally by any single approach, because of its inherent difficulties. This paper considers makespan optimization of a flexible flow shop (FFS) scheduling problem with stochastic processing times. It proposes a novel decomposition-based algorithm (DBA) to decompose an FFS into several clusters which can be solved more easily by different approaches. A neighbouring K-means clustering algorithm is developed to firstly group the machines of an FFS into an appropriate number of clusters, based on weighted cluster validity indices. A back propagation network (BPN) is then adopted to assign either the shortest processing time (SPT) or the genetic algorithm (GA) to generate a sub-schedule for each cluster. If two neighbouring clusters are allocated with the same approach, they are subsequently merged. After machine grouping and approach assignment, an overall schedule is generated by integrating the sub-schedules of the clusters. Computation results reveal that the proposed approach is superior to SPT and GA alone for FFS scheduling with stochastic processing times.
DescriptionBest Student Paper Award of International Conference on Systems Engineering and Engineering Management 2009: Mr. Kai Wang
Persistent Identifierhttp://hdl.handle.net/10722/126230
ISBN

 

DC FieldValueLanguage
dc.contributor.authorWang, Ken_HK
dc.contributor.authorChoi, SHen_HK
dc.date.accessioned2010-10-31T12:16:56Z-
dc.date.available2010-10-31T12:16:56Z-
dc.date.issued2009en_HK
dc.identifier.citationThe International Conference on Systems Engineering and Engineering Management 2009 of the World Congress on Engineering and Computer Science (WCECS 2009), San Francisco, CA., 20-22 October 2009. In Proceedings of WCECS, 2009, v. 2, p. 1050-1060en_HK
dc.identifier.isbn978-988-18210-2-7-
dc.identifier.urihttp://hdl.handle.net/10722/126230-
dc.descriptionBest Student Paper Award of International Conference on Systems Engineering and Engineering Management 2009: Mr. Kai Wang-
dc.description.abstractSince real manufacturing is dynamic and tends to suffer a wide range of uncertainties, research on production scheduling with uncertainty has received much more attention recently. Although various approaches have been investigated on the scheduling problem with uncertainty, this problem is still difficult to be solved optimally by any single approach, because of its inherent difficulties. This paper considers makespan optimization of a flexible flow shop (FFS) scheduling problem with stochastic processing times. It proposes a novel decomposition-based algorithm (DBA) to decompose an FFS into several clusters which can be solved more easily by different approaches. A neighbouring K-means clustering algorithm is developed to firstly group the machines of an FFS into an appropriate number of clusters, based on weighted cluster validity indices. A back propagation network (BPN) is then adopted to assign either the shortest processing time (SPT) or the genetic algorithm (GA) to generate a sub-schedule for each cluster. If two neighbouring clusters are allocated with the same approach, they are subsequently merged. After machine grouping and approach assignment, an overall schedule is generated by integrating the sub-schedules of the clusters. Computation results reveal that the proposed approach is superior to SPT and GA alone for FFS scheduling with stochastic processing times.-
dc.languageengen_HK
dc.publisherInternational Association of Engineers.en_HK
dc.relation.ispartofProceedings of the World Congress on Engineering and Computer Science, WCECS 2009en_HK
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.subjectBack propagation network-
dc.subjectDecomposition-
dc.subjectFlexible flow shop-
dc.subjectNeighbouring K-means clustering algorithm-
dc.subjectStochastic processing times-
dc.titleA decomposition-based algorithm for flexible flow shop scheduling with stochastic processing timesen_HK
dc.typeConference_Paperen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=978-988-17012-6-8&volume=II&spage=1050&epage=1060&date=2009&atitle=A+decomposition-based+algorithm+for+flexible+flow+shop+scheduling+with+stochastic+processing+times-
dc.identifier.emailChoi, SH: shchoi@hkucc.hku.hken_HK
dc.description.naturepostprint-
dc.identifier.hkuros175291en_HK
dc.identifier.volume2en_HK
dc.identifier.spage1050en_HK
dc.identifier.epage1060en_HK
dc.publisher.placeUnited States-
dc.description.otherThe International Conference on Systems Engineering and Engineering Management 2009 of the World Congress on Engineering and Computer Science (WCECS 2009), San Francisco, CA., 20-22 October 2009. In Proceedings of WCECS, 2009, v. 2, p. 1050-1060-

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