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Conference Paper: A decomposition based algorithm for flexible flow shop scheduling with machine breakdown

TitleA decomposition based algorithm for flexible flow shop scheduling with machine breakdown
Authors
KeywordsBack propagation network
Decomposition based approach
Flexible flow shop
Machine breakdown
Neighbouring K-means clustering algorithm
Issue Date2009
PublisherIEEE.
Citation
The IEEE International Conference on Computational Intelligence for Measurement Systems and Applications (CIMSA 2009), Hong Kong, 11-13 May 2009. In Proceedings of CIMSA, 2009, p. 134-139 How to Cite?
AbstractResearch on flow shop scheduling generally ignores uncertainties in real-world production because of the inherent difficulties of the problem. Scheduling problems with stochastic machine breakdown are difficult to solve optimally by a single approach. This paper considers makespan optimization of a flexible flow shop (FFS) scheduling problem with machine breakdown. It proposes a novel decomposition based approach (DBA) to decompose a problem into several sub-problems which can be solved more easily, while the neighbouring K-means clustering algorithm is employed to group the machines of an FFS into a few clusters. A back propagation network (BPN) is then adopted to assign either the shortest processing time (SPT) or the genetic algorithm (GA) to each cluster to solve the sub-problems. 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 solutions to the sub-problems. Computation results reveal that the proposed approach is superior to SPT and GA alone for FFS scheduling with machine breakdown. © 2009 IEEE.
Persistent Identifierhttp://hdl.handle.net/10722/126220
ISBN
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorWang, Ken_HK
dc.contributor.authorChoi, SHen_HK
dc.date.accessioned2010-10-31T12:16:23Z-
dc.date.available2010-10-31T12:16:23Z-
dc.date.issued2009en_HK
dc.identifier.citationThe IEEE International Conference on Computational Intelligence for Measurement Systems and Applications (CIMSA 2009), Hong Kong, 11-13 May 2009. In Proceedings of CIMSA, 2009, p. 134-139en_HK
dc.identifier.isbn978-1-4244-3819-8-
dc.identifier.urihttp://hdl.handle.net/10722/126220-
dc.description.abstractResearch on flow shop scheduling generally ignores uncertainties in real-world production because of the inherent difficulties of the problem. Scheduling problems with stochastic machine breakdown are difficult to solve optimally by a single approach. This paper considers makespan optimization of a flexible flow shop (FFS) scheduling problem with machine breakdown. It proposes a novel decomposition based approach (DBA) to decompose a problem into several sub-problems which can be solved more easily, while the neighbouring K-means clustering algorithm is employed to group the machines of an FFS into a few clusters. A back propagation network (BPN) is then adopted to assign either the shortest processing time (SPT) or the genetic algorithm (GA) to each cluster to solve the sub-problems. 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 solutions to the sub-problems. Computation results reveal that the proposed approach is superior to SPT and GA alone for FFS scheduling with machine breakdown. © 2009 IEEE.en_HK
dc.languageengen_HK
dc.publisherIEEE.-
dc.relation.ispartofProceedings of the IEEE International Conference on Computational Intelligence for Measurement Systems and Applications, CIMSA 2009en_HK
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.rightsIEEE International Conference on Computational Intelligence for Measurement Systems and Applications. Copyright © IEEE.-
dc.rights©2009 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.-
dc.subjectBack propagation networken_HK
dc.subjectDecomposition based approachen_HK
dc.subjectFlexible flow shopen_HK
dc.subjectMachine breakdownen_HK
dc.subjectNeighbouring K-means clustering algorithmen_HK
dc.titleA decomposition based algorithm for flexible flow shop scheduling with machine breakdownen_HK
dc.typeConference_Paperen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=978-1-4244-3819-8&volume=&spage=134&epage=139&date=2009&atitle=A+decomposition+based+algorithm+for+flexible+flow+shop+scheduling+with+machine+breakdown-
dc.identifier.emailChoi, SH:shchoi@hkucc.hku.hken_HK
dc.identifier.authorityChoi, SH=rp00109en_HK
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1109/CIMSA.2009.5069933en_HK
dc.identifier.scopuseid_2-s2.0-77950840255en_HK
dc.identifier.hkuros175700en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-77950840255&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.spage134en_HK
dc.identifier.epage139en_HK
dc.identifier.isiWOS:000270710800028-
dc.description.otherThe IEEE International Conference on Computational Intelligence for Measurement Systems and Applications (CIMSA 2009), Hong Kong, 11-13 May 2009. In Proceedings of CIMSA, 2009, p. 134-139-
dc.identifier.scopusauthoridWang, K=35436577100en_HK
dc.identifier.scopusauthoridChoi, SH=7408119615en_HK

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