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Article: Flexible flow shop scheduling with stochastic processing times: A decomposition-based approach
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TitleFlexible flow shop scheduling with stochastic processing times: A decomposition-based approach
 
AuthorsChoi, SH1
Wang, K2
 
KeywordsBack Propagation Network
Decomposition
Flexible Flow Shop
Neighbouring K-Means Clustering
Scheduling
Stochastic Processing Times
 
Issue Date2012
 
PublisherPergamon. The Journal's web site is located at http://www.elsevier.com/locate/cie
 
CitationComputers and Industrial Engineering, 2012, v. 63 n. 2, p. 362-373 [How to Cite?]
DOI: http://dx.doi.org/10.1016/j.cie.2012.04.001
 
AbstractFlexible flow shop scheduling problems are NP-hard and tend to become more complex when stochastic uncertainties are taken into consideration. Although some methods have been developed to address such problems, they remain inherently difficult to solve by any single approach. This paper presents a novel decomposition-based approach (DBA), which combines both the shortest processing time (SPT) and the genetic algorithm (GA), to minimizing the makespan of a flexible flow shop (FFS) with stochastic processing times. In the proposed DBA, 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 their stochastic nature. Two optimal back propagation networks (BPN), corresponding to the scenarios of simultaneous and non-simultaneous job arrivals, are then selectively adopted to assign either SPT or GA to each machine cluster for sub-schedule generation. Finally, an overall schedule is generated by integrating the sub-schedules of machine clusters. Computation results show that the DBA outperforms SPT and GA alone for FFS scheduling with stochastic processing times. © 2012 Elsevier Ltd. All rights reserved.
 
ISSN0360-8352
2012 Impact Factor: 1.516
2012 SCImago Journal Rankings: 1.712
 
DOIhttp://dx.doi.org/10.1016/j.cie.2012.04.001
 
ISI Accession Number IDWOS:000305869900002
 
ReferencesReferences in Scopus
 
DC FieldValue
dc.contributor.authorChoi, SH
 
dc.contributor.authorWang, K
 
dc.date.accessioned2012-08-08T08:38:38Z
 
dc.date.available2012-08-08T08:38:38Z
 
dc.date.issued2012
 
dc.description.abstractFlexible flow shop scheduling problems are NP-hard and tend to become more complex when stochastic uncertainties are taken into consideration. Although some methods have been developed to address such problems, they remain inherently difficult to solve by any single approach. This paper presents a novel decomposition-based approach (DBA), which combines both the shortest processing time (SPT) and the genetic algorithm (GA), to minimizing the makespan of a flexible flow shop (FFS) with stochastic processing times. In the proposed DBA, 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 their stochastic nature. Two optimal back propagation networks (BPN), corresponding to the scenarios of simultaneous and non-simultaneous job arrivals, are then selectively adopted to assign either SPT or GA to each machine cluster for sub-schedule generation. Finally, an overall schedule is generated by integrating the sub-schedules of machine clusters. Computation results show that the DBA outperforms SPT and GA alone for FFS scheduling with stochastic processing times. © 2012 Elsevier Ltd. All rights reserved.
 
dc.description.naturepostprint
 
dc.identifier.citationComputers and Industrial Engineering, 2012, v. 63 n. 2, p. 362-373 [How to Cite?]
DOI: http://dx.doi.org/10.1016/j.cie.2012.04.001
 
dc.identifier.citeulike10687318
 
dc.identifier.doihttp://dx.doi.org/10.1016/j.cie.2012.04.001
 
dc.identifier.epage373
 
dc.identifier.isiWOS:000305869900002
 
dc.identifier.issn0360-8352
2012 Impact Factor: 1.516
2012 SCImago Journal Rankings: 1.712
 
dc.identifier.issue2
 
dc.identifier.scopuseid_2-s2.0-84861012619
 
dc.identifier.spage362
 
dc.identifier.urihttp://hdl.handle.net/10722/155962
 
dc.identifier.volume63
 
dc.languageeng
 
dc.publisherPergamon. The Journal's web site is located at http://www.elsevier.com/locate/cie
 
dc.publisher.placeUnited Kingdom
 
dc.relation.ispartofComputers and Industrial Engineering
 
dc.relation.referencesReferences in Scopus
 
dc.rightsNOTICE: this is the author’s version of a work that was accepted for publication in Computers and Industrial Engineering. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Computers and Industrial Engineering, 2012, v. 63 n. 2, p. 362-373. DOI: 10.1016/j.cie.2012.04.001
 
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License
 
dc.subjectBack Propagation Network
 
dc.subjectDecomposition
 
dc.subjectFlexible Flow Shop
 
dc.subjectNeighbouring K-Means Clustering
 
dc.subjectScheduling
 
dc.subjectStochastic Processing Times
 
dc.titleFlexible flow shop scheduling with stochastic processing times: A decomposition-based approach
 
dc.typeArticle
 
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Author Affiliations
  1. The University of Hong Kong
  2. Wuhan University