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Article: A hybrid Hopfield network-genetic algorithm approach to optimal process plan selection

TitleA hybrid Hopfield network-genetic algorithm approach to optimal process plan selection
Authors
Issue Date2000
PublisherTaylor & Francis Ltd. The Journal's web site is located at http://www.tandf.co.uk/journals/titles/00207543.asp
Citation
International Journal Of Production Research, 2000, v. 38 n. 8, p. 1823-1839 How to Cite?
AbstractIn the automated manufacturing environment, different sets of alternative process plans can normally be generated to manufacture each part. However, this entails considerable complexities in solving the process plan selection problem because each of these process plans demands specification of their individual and varying manufacturing costs and manufacturing resource requirements, such as machines, fixtures/jigs, and cutting tools. In this paper the problem of selecting exactly one representative from a set of alternative process plans for each part is formulated. The purpose is to minimize, for all the parts to be manufactured, the sum of both the costs of the selected process plans and the dissimilarities in their manufacturing resource requirements. The techniques of Hopfield neural network and genetic algorithm are introduced as possible approaches to solve such a problem. In particular, a hybrid Hopfield network-genetic algorithm approach is also proposed in this paper as an effective near-global optimization technique to provide a good quality solution to the process plan selection problem. The effectiveness of the proposed hybrid approach is illustrated by comparing its performance with that of some published approaches and other optimization techniques, by using several examples currently available in the literature, as well as a few randomly generated examples.
Persistent Identifierhttp://hdl.handle.net/10722/155849
ISSN
2015 Impact Factor: 1.693
2015 SCImago Journal Rankings: 1.445
References

 

DC FieldValueLanguage
dc.contributor.authorMing, XGen_US
dc.contributor.authorMak, KLen_US
dc.date.accessioned2012-08-08T08:38:01Z-
dc.date.available2012-08-08T08:38:01Z-
dc.date.issued2000en_US
dc.identifier.citationInternational Journal Of Production Research, 2000, v. 38 n. 8, p. 1823-1839en_US
dc.identifier.issn0020-7543en_US
dc.identifier.urihttp://hdl.handle.net/10722/155849-
dc.description.abstractIn the automated manufacturing environment, different sets of alternative process plans can normally be generated to manufacture each part. However, this entails considerable complexities in solving the process plan selection problem because each of these process plans demands specification of their individual and varying manufacturing costs and manufacturing resource requirements, such as machines, fixtures/jigs, and cutting tools. In this paper the problem of selecting exactly one representative from a set of alternative process plans for each part is formulated. The purpose is to minimize, for all the parts to be manufactured, the sum of both the costs of the selected process plans and the dissimilarities in their manufacturing resource requirements. The techniques of Hopfield neural network and genetic algorithm are introduced as possible approaches to solve such a problem. In particular, a hybrid Hopfield network-genetic algorithm approach is also proposed in this paper as an effective near-global optimization technique to provide a good quality solution to the process plan selection problem. The effectiveness of the proposed hybrid approach is illustrated by comparing its performance with that of some published approaches and other optimization techniques, by using several examples currently available in the literature, as well as a few randomly generated examples.en_US
dc.languageengen_US
dc.publisherTaylor & Francis Ltd. The Journal's web site is located at http://www.tandf.co.uk/journals/titles/00207543.aspen_US
dc.relation.ispartofInternational Journal of Production Researchen_US
dc.titleA hybrid Hopfield network-genetic algorithm approach to optimal process plan selectionen_US
dc.typeArticleen_US
dc.identifier.emailMak, KL:makkl@hkucc.hku.hken_US
dc.identifier.authorityMak, KL=rp00154en_US
dc.description.naturelink_to_subscribed_fulltexten_US
dc.identifier.scopuseid_2-s2.0-0034690551en_US
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-0034690551&selection=ref&src=s&origin=recordpageen_US
dc.identifier.volume38en_US
dc.identifier.issue8en_US
dc.identifier.spage1823en_US
dc.identifier.epage1839en_US
dc.publisher.placeUnited Kingdomen_US
dc.identifier.scopusauthoridMing, XG=7005300183en_US
dc.identifier.scopusauthoridMak, KL=7102680226en_US

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