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dc.contributor.authorChung, Yu-Hsiangen_US
dc.contributor.authorTong, Lee-Ingen_US
dc.date.accessioned2014-12-08T15:23:05Z-
dc.date.available2014-12-08T15:23:05Z-
dc.date.issued2012-08-01en_US
dc.identifier.issn0360-8352en_US
dc.identifier.urihttp://hdl.handle.net/11536/16243-
dc.description.abstractIn traditional scheduling problems, the processing time for the given job is assumed to be a constant regardless of whether the job is scheduled earlier or later. However, the phenomenon named "learning effect" has extensively been studied recently, in which job processing times decline as workers gain more experience. This paper discusses a bi-criteria scheduling problem in an m-machine permutation flowshop environment with varied learning effects on different machines. The objective of this paper is to minimize the weighted sum of the total completion time and the makespan. A dominance criterion and a lower bound are proposed to accelerate the branch-and-bound algorithm for deriving the optimal solution. In addition, the near-optimal solutions are derived by adapting two well-known heuristic algorithms. The computational experiments reveal that the proposed branch-and-bound algorithm can effectively deal with problems with up to 16 jobs, and the proposed heuristic algorithms can yield accurate near-optimal solutions. (C) 2012 Elsevier Ltd. All rights reserved.en_US
dc.language.isoen_USen_US
dc.subjectSchedulingen_US
dc.subjectLearning effecten_US
dc.subjectFlowshopen_US
dc.subjectTotal completion timeen_US
dc.subjectMakespanen_US
dc.titleBi-criteria minimization for the permutation flowshop scheduling problem with machine-based learning effectsen_US
dc.typeArticleen_US
dc.identifier.journalCOMPUTERS & INDUSTRIAL ENGINEERINGen_US
dc.citation.volume63en_US
dc.citation.issue1en_US
dc.citation.epage302en_US
dc.contributor.department工業工程與管理學系zh_TW
dc.contributor.departmentDepartment of Industrial Engineering and Managementen_US
dc.identifier.wosnumberWOS:000304687100028-
dc.citation.woscount1-
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