A steady-state genetic algorithm for multi-product supply chain network design

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dc.contributor.authorAltiparmak, Fulya
dc.contributor.authorGen, Mitsuo
dc.contributor.authorLin, Lin
dc.contributor.authorKaraoglan, Ismail
dc.date.accessioned2020-03-26T17:37:46Z
dc.date.available2020-03-26T17:37:46Z
dc.date.issued2009
dc.departmentSelçuk Üniversitesien_US
dc.description35th International Conference on Computers and Industrial Engineering -- JUN 19-22, 2005-2006 -- Istanbul, TURKEYen_US
dc.description.abstractSupply chain network (SCN) design is to provide an optimal platform for efficient and effective supply chain management (SCM). The problem is often an important and strategic operations management problem in SCM. The design task involves the choice of facilities (plants and distribution centers (DCs)) to be opened and the distribution network design to satisfy the customer demand with minimum cost. This paper presents it solution procedure based on steady-state genetic algorithms (ssGA) with it new encoding structure for the design of a single-source, multi-product, multi-stage SCN. The effectiveness of the ssGA has been investigated by comparing its results with those obtained by CPLEX, Lagrangean heuristic, hyrid GA and simulated annealing on a set of SCN design problems with different sizes. (C) 2007 Elsevier Ltd. All rights reserved.en_US
dc.identifier.doi10.1016/j.cie.2007.05.012en_US
dc.identifier.endpage537en_US
dc.identifier.issn0360-8352en_US
dc.identifier.issn1879-0550en_US
dc.identifier.issue2en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage521en_US
dc.identifier.urihttps://dx.doi.org/10.1016/j.cie.2007.05.012
dc.identifier.urihttps://hdl.handle.net/20.500.12395/23235
dc.identifier.volume56en_US
dc.identifier.wosWOS:000264037900006en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherPERGAMON-ELSEVIER SCIENCE LTDen_US
dc.relation.ispartofCOMPUTERS & INDUSTRIAL ENGINEERINGen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.selcuk20240510_oaigen_US
dc.subjectSupply chain network designen_US
dc.subjectGenetic algorithmsen_US
dc.subjectSimulated annealingen_US
dc.subjectLagrangean heuristicen_US
dc.titleA steady-state genetic algorithm for multi-product supply chain network designen_US
dc.typeArticleen_US

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