A genetic algorithm approach for multi-objective optimization of supply chain networks

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dc.contributor.authorAltıparmak, Fulya
dc.contributor.authorGen, Mitsuo
dc.contributor.authorLin, Lin
dc.contributor.authorPaksoy, Turan
dc.date.accessioned2020-03-26T17:05:02Z
dc.date.available2020-03-26T17:05:02Z
dc.date.issued2006
dc.departmentSelçuk Üniversitesien_US
dc.description.abstractSupply chain network (SCN) design is to provide an optimal platform for efficient and effective supply chain management. It is an important and strategic operations management problem in supply chain management, and usually involves multiple and conflicting objectives such as cost, service level, resource utilization, etc. This paper proposes a new solution procedure based on genetic algorithms to find the set of Pareto-optimal solutions for multi-objective SCN design problem. To deal with multi-objective and enable the decision maker for evaluating a greater number of alternative solutions, two different weight approaches are implemented in the proposed solution procedure. An experimental study using actual data from a company, which is a producer of plastic products in Turkey, is carried out into two stages. While the effects of weight approaches on the performance of proposed solution procedure are investigated in the first stage, the proposed solution procedure and simulated annealing are compared according to quality of Pareto-optimal solutions in the second stage. © 2006 Elsevier Ltd. All rights reserved.en_US
dc.description.sponsorshipMatsumae International Foundation 17510138 Waseda Universityen_US
dc.description.sponsorshipThis research had been supported by The Matsumae International Foundation in Japan, while Dr. Fulya Altiparmak was a visiting researcher at Graduate School of Information, Production and Systems, Waseda University. Also this work was partly supported by Waseda University Grant for Special Research Projects 2004 and the Ministry of Education, Science and Culture, the Japanese Government: Grant-in-Aid for Scientific Research (No. 17510138).en_US
dc.identifier.doi10.1016/j.cie.2006.07.011en_US
dc.identifier.endpage215en_US
dc.identifier.issn0360-8352en_US
dc.identifier.issue1en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage196en_US
dc.identifier.urihttps://dx.doi.org/10.1016/j.cie.2006.07.011
dc.identifier.urihttps://hdl.handle.net/20.500.12395/20825
dc.identifier.volume51en_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.relation.ispartofComputers and 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.subjectGenetic algorithmen_US
dc.subjectMulti-objective optimizationen_US
dc.subjectSupply chain networken_US
dc.titleA genetic algorithm approach for multi-objective optimization of supply chain networksen_US
dc.typeArticleen_US

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