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Conference Paper: Machine requirements planning and workload assignment using genetic algorithms

TitleMachine requirements planning and workload assignment using genetic algorithms
Authors
Porter, BMak, KLWong, YS
KeywordsComputers
Artificial intelligence
Issue Date1995
PublisherIEEE.
Citation
Proceedings Of The Ieee Conference On Evolutionary Computation, 1995, v. 2, p. 711-715 How to Cite?
DOI: http://dx.doi.org/10.1109/ICEC.1995.487472
AbstractThis paper presents a genetic approach to determining the optimal number of machines required in a manufacturing system for meeting a specified production schedule. This use of genetic algorithms is illustrated by solving a typical machine requirements planning problem. Comparison of the respective results obtained by using the proposed approach and a standard mixed-integer programming package shows that the proposed approach is indeed an effective means for optimal manufacturing systems design.
Persistent Identifierhttp://hdl.handle.net/10722/46570

 

DC FieldValueLanguage
dc.contributor.authorPorter, Ben_HK
dc.contributor.authorMak, KLen_HK
dc.contributor.authorWong, YSen_HK
dc.date.accessioned2007-10-30T06:53:09Z-
dc.date.available2007-10-30T06:53:09Z-
dc.date.issued1995en_HK
dc.identifier.citationProceedings Of The Ieee Conference On Evolutionary Computation, 1995, v. 2, p. 711-715en_HK
dc.identifier.urihttp://hdl.handle.net/10722/46570-
dc.description.abstractThis paper presents a genetic approach to determining the optimal number of machines required in a manufacturing system for meeting a specified production schedule. This use of genetic algorithms is illustrated by solving a typical machine requirements planning problem. Comparison of the respective results obtained by using the proposed approach and a standard mixed-integer programming package shows that the proposed approach is indeed an effective means for optimal manufacturing systems design.en_HK
dc.format.extent431222 bytes-
dc.format.extent4653 bytes-
dc.format.extent2656 bytes-
dc.format.mimetypeapplication/pdf-
dc.format.mimetypetext/plain-
dc.format.mimetypetext/plain-
dc.languageengen_HK
dc.publisherIEEE.en_HK
dc.relation.ispartofProceedings of the IEEE Conference on Evolutionary Computationen_HK
dc.rights©1995 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.-
dc.subjectComputersen_HK
dc.subjectArtificial intelligenceen_HK
dc.titleMachine requirements planning and workload assignment using genetic algorithmsen_HK
dc.typeConference_Paperen_HK
dc.identifier.emailMak, KL:makkl@hkucc.hku.hken_HK
dc.identifier.authorityMak, KL=rp00154en_HK
dc.description.naturepublished_or_final_versionen_HK
dc.identifier.doi10.1109/ICEC.1995.487472en_HK
dc.identifier.scopuseid_2-s2.0-0029520855en_HK
dc.identifier.hkuros20994-
dc.identifier.volume2en_HK
dc.identifier.spage711en_HK
dc.identifier.epage715en_HK
dc.identifier.scopusauthoridPorter, B=7201565386en_HK
dc.identifier.scopusauthoridMak, KL=7102680226en_HK
dc.identifier.scopusauthoridWong, YS=26637607500en_HK

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