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Article: A decomposition-based approach to flexible flow shop scheduling under machine breakdown
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TitleA decomposition-based approach to flexible flow shop scheduling under machine breakdown
 
AuthorsWang, K1
Choi, SH1
 
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
 
CitationInternational Journal of Production Research, 2012, v. 50 n. 1, p. 215-234 [How to Cite?]
DOI: http://dx.doi.org/10.1080/00207543.2011.571456
 
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.
 
ISSN0020-7543
2013 Impact Factor: 1.323
 
DOIhttp://dx.doi.org/10.1080/00207543.2011.571456
 
ISI Accession Number IDWOS:000301951700013
 
DC FieldValue
dc.contributor.authorWang, K
 
dc.contributor.authorChoi, SH
 
dc.date.accessioned2011-09-23T05:48:05Z
 
dc.date.available2011-09-23T05:48:05Z
 
dc.date.issued2012
 
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.description.natureLink_to_subscribed_fulltext
 
dc.identifier.citationInternational Journal of Production Research, 2012, v. 50 n. 1, p. 215-234 [How to Cite?]
DOI: http://dx.doi.org/10.1080/00207543.2011.571456
 
dc.identifier.doihttp://dx.doi.org/10.1080/00207543.2011.571456
 
dc.identifier.epage234
 
dc.identifier.hkuros192525
 
dc.identifier.isiWOS:000301951700013
 
dc.identifier.issn0020-7543
2013 Impact Factor: 1.323
 
dc.identifier.issue1
 
dc.identifier.openurl
 
dc.identifier.scopuseid_2-s2.0-84863283344
 
dc.identifier.spage215
 
dc.identifier.urihttp://hdl.handle.net/10722/139294
 
dc.identifier.volume50
 
dc.languageeng
 
dc.publisherTaylor & Francis Ltd. The Journal's web site is located at http://www.tandf.co.uk/journals/titles/00207543.asp
 
dc.publisher.placeUnited Kingdom
 
dc.relation.ispartofInternational Journal of Production Research
 
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 breakdown
 
dc.typeArticle
 
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Author Affiliations
  1. The University of Hong Kong