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Conference Paper: Solving stochastic flexible flow shop scheduling problems with a decomposition-based approach

TitleSolving stochastic flexible flow shop scheduling problems with a decomposition-based approach
Authors
KeywordsBack Propagation Network
Decomposition
Flexible Flow Shop
Neighbouring K-Means Clustering Algorithm
Stochastic Processing Times
Issue Date2010
Citation
Aip Conference Proceedings, 2010, v. 1247, p. 374-388 How to Cite?
AbstractReal manufacturing is dynamic and tends to suffer a lot of uncertainties. Research on production scheduling under uncertainty has recently received much attention. Although various approaches have been developed for scheduling under uncertainty, this problem is still difficult to tackle by any single approach, because of its inherent difficulties. This chapter describes a decomposition-based approach (DBA) for makespan minimisation of a flexible flow shop (FFS) scheduling problem with stochastic processing times. The DBA decomposes an FFS into several machine 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 machine clusters, based on a weighted cluster validity index. A back propagation network (BPN) is then adopted to assign either the Shortest Processing Time (SPT) Algorithm or the Genetic Algorithm (GA) to generate a sub-schedule for each machine cluster. After machine grouping and approach assignment, an overall schedule is generated by integrating the sub-schedules of the machine clusters. Computation results reveal that the DBA is superior to SPT and GA alone for FFS scheduling under stochastic processing times, and that it can be easily adapted to schedule FFS under other uncertainties. © 2010 American Institute of Physics.
Persistent Identifierhttp://hdl.handle.net/10722/158824
ISBN
ISSN
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorWang, Ken_US
dc.contributor.authorChoi, SHen_US
dc.date.accessioned2012-08-08T09:03:29Z-
dc.date.available2012-08-08T09:03:29Z-
dc.date.issued2010en_US
dc.identifier.citationAip Conference Proceedings, 2010, v. 1247, p. 374-388en_US
dc.identifier.isbn9780735406223-
dc.identifier.issn0094-243Xen_US
dc.identifier.urihttp://hdl.handle.net/10722/158824-
dc.description.abstractReal manufacturing is dynamic and tends to suffer a lot of uncertainties. Research on production scheduling under uncertainty has recently received much attention. Although various approaches have been developed for scheduling under uncertainty, this problem is still difficult to tackle by any single approach, because of its inherent difficulties. This chapter describes a decomposition-based approach (DBA) for makespan minimisation of a flexible flow shop (FFS) scheduling problem with stochastic processing times. The DBA decomposes an FFS into several machine 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 machine clusters, based on a weighted cluster validity index. A back propagation network (BPN) is then adopted to assign either the Shortest Processing Time (SPT) Algorithm or the Genetic Algorithm (GA) to generate a sub-schedule for each machine cluster. After machine grouping and approach assignment, an overall schedule is generated by integrating the sub-schedules of the machine clusters. Computation results reveal that the DBA is superior to SPT and GA alone for FFS scheduling under stochastic processing times, and that it can be easily adapted to schedule FFS under other uncertainties. © 2010 American Institute of Physics.en_US
dc.languageengen_US
dc.relation.ispartofAIP Conference Proceedingsen_US
dc.subjectBack Propagation Networken_US
dc.subjectDecompositionen_US
dc.subjectFlexible Flow Shopen_US
dc.subjectNeighbouring K-Means Clustering Algorithmen_US
dc.subjectStochastic Processing Timesen_US
dc.titleSolving stochastic flexible flow shop scheduling problems with a decomposition-based approachen_US
dc.typeConference_Paperen_US
dc.identifier.emailChoi, SH:shchoi@hkucc.hku.hken_US
dc.identifier.authorityChoi, SH=rp00109en_US
dc.description.naturelink_to_subscribed_fulltexten_US
dc.identifier.doi10.1063/1.3460245en_US
dc.identifier.scopuseid_2-s2.0-77955783241en_US
dc.identifier.hkuros175683-
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-77955783241&selection=ref&src=s&origin=recordpageen_US
dc.identifier.volume1247en_US
dc.identifier.spage374en_US
dc.identifier.epage388en_US
dc.identifier.isiWOS:000282466500031-
dc.publisher.placeUnited Statesen_US
dc.identifier.scopusauthoridWang, K=35436577100en_US
dc.identifier.scopusauthoridChoi, SH=7408119615en_US

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