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dc.contributor.authorChen, JHen_US
dc.contributor.authorHo, SYen_US
dc.date.accessioned2014-12-08T15:18:57Z-
dc.date.available2014-12-08T15:18:57Z-
dc.date.issued2005-06-01en_US
dc.identifier.issn0890-6955en_US
dc.identifier.urihttp://dx.doi.org/10.1016/j.ijmachtools.2004.10.010en_US
dc.identifier.urihttp://hdl.handle.net/11536/13615-
dc.description.abstractIn this paper, a novel approach using an efficient multi-objective genetic algorithm EMOGA is proposed to solve the problems of production planning of flexible manufacturing systems (FMSs) having four objectives: minimizing total flow time, machine workload unbalance. greatest machine workload and total tool cost. EMOGA makes use of Pareto dominance relationship to solve the problems without using relative preferences of multiple objectives. High efficiency of EMOGA arises from that multiple objectives can be optimized simultaneously Without using heuristics and a set of good non-dominated Solutions can be obtained providing additional degrees of freedom for the exploitation of resources of FMSs. Experimental results demonstrate effectiveness of the proposed approach using EMOGA to]production planning of FMSs. (c) 2004 Elsevier Ltd. All rights reserved.en_US
dc.language.isoen_USen_US
dc.subjectflexible manufacturing systemen_US
dc.subjectmulti-objective optimizationen_US
dc.subjectgenetic algorithmen_US
dc.subjectproduction planningen_US
dc.titleA novel approach to production planning of flexible manufacturing systems using an efficient multi-objective genetic algorithmen_US
dc.typeArticleen_US
dc.identifier.doi10.1016/j.ijmachtools.2004.10.010en_US
dc.identifier.journalINTERNATIONAL JOURNAL OF MACHINE TOOLS & MANUFACTUREen_US
dc.citation.volume45en_US
dc.citation.issue7-8en_US
dc.citation.spage949en_US
dc.citation.epage957en_US
dc.contributor.department生物資訊及系統生物研究所zh_TW
dc.contributor.departmentInstitude of Bioinformatics and Systems Biologyen_US
dc.identifier.wosnumberWOS:000228207300023-
dc.citation.woscount29-
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