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Article: A multi-criterion genetic algorithm for order distribution in a demand driven supply chain

TitleA multi-criterion genetic algorithm for order distribution in a demand driven supply chain
Authors
Issue Date2004
PublisherTaylor & Francis Ltd. The Journal's web site is located at http://www.tandf.co.uk/journals/titles/0951192X.asp
Citation
International Journal Of Computer Integrated Manufacturing, 2004, v. 17 n. 4, p. 339-351 How to Cite?
AbstractThis paper develops a multi-criterion genetic optimization procedure, specifically designed for solving optimization problems in supply chain management. The proposed algorithm is discussed with an order distribution problem in a demand driven supply chain network. It combines the analytic hierarchy process (AHP) with genetic algorithms. AHP is utilized to evaluate the fitness values of chromosomes. The proposed algorithm allows decision-makers to give weighting for criteria using a pair-wise comparison approach. The numerical results obtained from the proposed algorithm are compared with the one obtained from the multi-objective mixed integer programming approach. The comparison shows that the proposed algorithm is reliable and robust. In addition, it provides more control and information for the decision-makers to gain a better insight of the supply chain network.
Persistent Identifierhttp://hdl.handle.net/10722/74581
ISSN
2023 Impact Factor: 3.7
2023 SCImago Journal Rankings: 0.987
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorChan, FTSen_HK
dc.contributor.authorChung, SHen_HK
dc.date.accessioned2010-09-06T07:02:45Z-
dc.date.available2010-09-06T07:02:45Z-
dc.date.issued2004en_HK
dc.identifier.citationInternational Journal Of Computer Integrated Manufacturing, 2004, v. 17 n. 4, p. 339-351en_HK
dc.identifier.issn0951-192Xen_HK
dc.identifier.urihttp://hdl.handle.net/10722/74581-
dc.description.abstractThis paper develops a multi-criterion genetic optimization procedure, specifically designed for solving optimization problems in supply chain management. The proposed algorithm is discussed with an order distribution problem in a demand driven supply chain network. It combines the analytic hierarchy process (AHP) with genetic algorithms. AHP is utilized to evaluate the fitness values of chromosomes. The proposed algorithm allows decision-makers to give weighting for criteria using a pair-wise comparison approach. The numerical results obtained from the proposed algorithm are compared with the one obtained from the multi-objective mixed integer programming approach. The comparison shows that the proposed algorithm is reliable and robust. In addition, it provides more control and information for the decision-makers to gain a better insight of the supply chain network.en_HK
dc.languageengen_HK
dc.publisherTaylor & Francis Ltd. The Journal's web site is located at http://www.tandf.co.uk/journals/titles/0951192X.aspen_HK
dc.relation.ispartofInternational Journal of Computer Integrated Manufacturingen_HK
dc.titleA multi-criterion genetic algorithm for order distribution in a demand driven supply chainen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=0951-192X&volume=17&issue=4&spage=339&epage=351&date=2004&atitle=A+multi-criterion+genetic+algorithm+for+order+distribution+in+a+demand+driven+supply+chainen_HK
dc.identifier.emailChan, FTS: ftschan@hkucc.hku.hken_HK
dc.identifier.authorityChan, FTS=rp00090en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1080/09511920310001617022en_HK
dc.identifier.scopuseid_2-s2.0-2542486712en_HK
dc.identifier.hkuros90528en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-2542486712&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume17en_HK
dc.identifier.issue4en_HK
dc.identifier.spage339en_HK
dc.identifier.epage351en_HK
dc.identifier.isiWOS:000221450900005-
dc.publisher.placeUnited Kingdomen_HK
dc.identifier.scopusauthoridChan, FTS=7202586517en_HK
dc.identifier.scopusauthoridChung, SH=36023203100en_HK
dc.identifier.issnl0951-192X-

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