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Article: A decomposition-based approach to flexible flow shop scheduling under machine breakdown

TitleA decomposition-based approach to flexible flow shop scheduling under machine breakdown
Authors
KeywordsBack propagation network
Decomposition
Flexible flow shop
Machine breakdown
Neighbouring k-means clustering
Issue Date2012
PublisherTaylor & Francis Ltd. The Journal's web site is located at http://www.tandf.co.uk/journals/titles/00207543.asp
Citation
International Journal of Production Research, 2012, v. 50 n. 1, p. 215-234 How to Cite?
AbstractManufacturing systems in real-world production are generally dynamic and often subject to a wide range of uncertainties. Recently, research on production scheduling under uncertainty has attracted substantial attention. Although some methods have been developed to address this problem, scheduling under uncertainty remains inherently difficult to solve by any single approach. This article considers makespan optimisation of a flexible flow shop (FFS) scheduling problem under machine breakdown. It proposes a novel decomposition-based approach to decompose an FFS scheduling problem into several cluster scheduling problems which can be solved more easily by different approaches. A neighbouring K-means clustering algorithm is developed to first group the machines of an FFS into an appropriate number of machine clusters, based on a proposed machine allocation algorithm and weighted cluster validity indices. Two optimal back propagation networks, corresponding to the scenarios of simultaneous and non-simultaneous job arrivals, are then selectively adopted to assign either the shortest processing time (SPT) or the genetic algorithm (GA) to each machine cluster to solve cluster scheduling problems. If two neighbouring machine 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 under machine breakdown.
Persistent Identifierhttp://hdl.handle.net/10722/139294
ISSN
2014 Impact Factor: 1.477
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorWang, Ken_US
dc.contributor.authorChoi, SHen_US
dc.date.accessioned2011-09-23T05:48:05Z-
dc.date.available2011-09-23T05:48:05Z-
dc.date.issued2012en_US
dc.identifier.citationInternational Journal of Production Research, 2012, v. 50 n. 1, p. 215-234en_US
dc.identifier.issn0020-7543en_US
dc.identifier.urihttp://hdl.handle.net/10722/139294-
dc.description.abstractManufacturing systems in real-world production are generally dynamic and often subject to a wide range of uncertainties. Recently, research on production scheduling under uncertainty has attracted substantial attention. Although some methods have been developed to address this problem, scheduling under uncertainty remains inherently difficult to solve by any single approach. This article considers makespan optimisation of a flexible flow shop (FFS) scheduling problem under machine breakdown. It proposes a novel decomposition-based approach to decompose an FFS scheduling problem into several cluster scheduling problems which can be solved more easily by different approaches. A neighbouring K-means clustering algorithm is developed to first group the machines of an FFS into an appropriate number of machine clusters, based on a proposed machine allocation algorithm and weighted cluster validity indices. Two optimal back propagation networks, corresponding to the scenarios of simultaneous and non-simultaneous job arrivals, are then selectively adopted to assign either the shortest processing time (SPT) or the genetic algorithm (GA) to each machine cluster to solve cluster scheduling problems. If two neighbouring machine 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 under machine breakdown.-
dc.languageengen_US
dc.publisherTaylor & Francis Ltd. The Journal's web site is located at http://www.tandf.co.uk/journals/titles/00207543.aspen_US
dc.relation.ispartofInternational Journal of Production Researchen_US
dc.subjectBack propagation network-
dc.subjectDecomposition-
dc.subjectFlexible flow shop-
dc.subjectMachine breakdown-
dc.subjectNeighbouring k-means clustering-
dc.titleA decomposition-based approach to flexible flow shop scheduling under machine breakdownen_US
dc.typeArticleen_US
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=1366â  588X online&volume=1-20, iFirst&spage=1&epage=21&date=2011&atitle=A+decomposition-based+approach+to+flexible+flow+shop+scheduling+under+machine+breakdownen_US
dc.identifier.emailChoi, SH: shchoi@hku.hken_US
dc.identifier.authorityChoi, SH=rp00109en_US
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1080/00207543.2011.571456en_US
dc.identifier.scopuseid_2-s2.0-84863283344-
dc.identifier.hkuros192525en_US
dc.identifier.volume50-
dc.identifier.issue1-
dc.identifier.spage215-
dc.identifier.epage234-
dc.identifier.isiWOS:000301951700013-
dc.publisher.placeUnited Kingdom-

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