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Article: Flexible flow shop scheduling with stochastic processing times: A decomposition-based approach

TitleFlexible flow shop scheduling with stochastic processing times: A decomposition-based approach
Authors
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
Citation
Computers and Industrial Engineering, 2012, v. 63 n. 2, p. 362-373 How to Cite?
Abstract
Flexible 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.
Persistent Identifierhttp://hdl.handle.net/10722/155962
ISSN
2013 Impact Factor: 1.690
2013 SCImago Journal Rankings: 1.723
ISI Accession Number ID
References

 

Author Affiliations
  1. The University of Hong Kong
  2. Wuhan University
DC FieldValueLanguage
dc.contributor.authorChoi, SHen_US
dc.contributor.authorWang, Ken_US
dc.date.accessioned2012-08-08T08:38:38Z-
dc.date.available2012-08-08T08:38:38Z-
dc.date.issued2012en_US
dc.identifier.citationComputers and Industrial Engineering, 2012, v. 63 n. 2, p. 362-373en_US
dc.identifier.issn0360-8352en_US
dc.identifier.urihttp://hdl.handle.net/10722/155962-
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.en_US
dc.languageengen_US
dc.publisherPergamon. The Journal's web site is located at http://www.elsevier.com/locate/cieen_US
dc.relation.ispartofComputers and Industrial Engineeringen_US
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 Networken_US
dc.subjectDecompositionen_US
dc.subjectFlexible Flow Shopen_US
dc.subjectNeighbouring K-Means Clusteringen_US
dc.subjectSchedulingen_US
dc.subjectStochastic Processing Timesen_US
dc.titleFlexible flow shop scheduling with stochastic processing times: A decomposition-based approachen_US
dc.typeArticleen_US
dc.identifier.emailChoi, SH:shchoi@hkucc.hku.hken_US
dc.identifier.authorityChoi, SH=rp00109en_US
dc.description.naturepostprinten_US
dc.identifier.doi10.1016/j.cie.2012.04.001en_US
dc.identifier.scopuseid_2-s2.0-84861012619en_US
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-84861012619&selection=ref&src=s&origin=recordpageen_US
dc.identifier.volume63en_US
dc.identifier.issue2en_US
dc.identifier.spage362en_US
dc.identifier.epage373en_US
dc.identifier.isiWOS:000305869900002-
dc.publisher.placeUnited Kingdomen_US
dc.identifier.scopusauthoridChoi, SH=7408119615en_US
dc.identifier.scopusauthoridWang, K=55076580100en_US
dc.identifier.citeulike10687318-

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