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Article: Flexible flow shop scheduling with stochastic processing times: A decomposition-based approach
Title | Flexible flow shop scheduling with stochastic processing times: A decomposition-based approach |
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Authors | |
Keywords | Back Propagation Network Decomposition Flexible Flow Shop Neighbouring K-Means Clustering Scheduling Stochastic Processing Times |
Issue Date | 2012 |
Publisher | Pergamon. 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 Identifier | http://hdl.handle.net/10722/155962 |
ISSN | 2023 Impact Factor: 6.7 2023 SCImago Journal Rankings: 1.701 |
ISI Accession Number ID | |
References |
DC Field | Value | Language |
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dc.contributor.author | Choi, SH | en_US |
dc.contributor.author | Wang, K | en_US |
dc.date.accessioned | 2012-08-08T08:38:38Z | - |
dc.date.available | 2012-08-08T08:38:38Z | - |
dc.date.issued | 2012 | en_US |
dc.identifier.citation | Computers and Industrial Engineering, 2012, v. 63 n. 2, p. 362-373 | en_US |
dc.identifier.issn | 0360-8352 | en_US |
dc.identifier.uri | http://hdl.handle.net/10722/155962 | - |
dc.description.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. | en_US |
dc.language | eng | en_US |
dc.publisher | Pergamon. The Journal's web site is located at http://www.elsevier.com/locate/cie | en_US |
dc.relation.ispartof | Computers and Industrial Engineering | en_US |
dc.rights | NOTICE: 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.rights | This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. | - |
dc.subject | Back Propagation Network | en_US |
dc.subject | Decomposition | en_US |
dc.subject | Flexible Flow Shop | en_US |
dc.subject | Neighbouring K-Means Clustering | en_US |
dc.subject | Scheduling | en_US |
dc.subject | Stochastic Processing Times | en_US |
dc.title | Flexible flow shop scheduling with stochastic processing times: A decomposition-based approach | en_US |
dc.type | Article | en_US |
dc.identifier.email | Choi, SH:shchoi@hkucc.hku.hk | en_US |
dc.identifier.authority | Choi, SH=rp00109 | en_US |
dc.description.nature | postprint | en_US |
dc.identifier.doi | 10.1016/j.cie.2012.04.001 | en_US |
dc.identifier.scopus | eid_2-s2.0-84861012619 | en_US |
dc.relation.references | http://www.scopus.com/mlt/select.url?eid=2-s2.0-84861012619&selection=ref&src=s&origin=recordpage | en_US |
dc.identifier.volume | 63 | en_US |
dc.identifier.issue | 2 | en_US |
dc.identifier.spage | 362 | en_US |
dc.identifier.epage | 373 | en_US |
dc.identifier.isi | WOS:000305869900002 | - |
dc.publisher.place | United Kingdom | en_US |
dc.identifier.scopusauthorid | Choi, SH=7408119615 | en_US |
dc.identifier.scopusauthorid | Wang, K=55076580100 | en_US |
dc.identifier.citeulike | 10687318 | - |
dc.identifier.issnl | 0360-8352 | - |