A genetic algorithm approach for optimising a closed-loop supply chain network with crisp and fuzzy objectives

Basit Öğe Kaydı
dc.contributor.authorDemirel, Neslihan
dc.contributor.authorOzceylan, Eren
dc.contributor.authorPaksoy, Turan
dc.contributor.authorGokcen, Hadi
dc.date.accessioned2020-03-26T18:49:09Z
dc.date.available2020-03-26T18:49:09Z
dc.date.issued2014
dc.departmentSelçuk Üniversitesien_US
dc.description.abstractThis paper proposes a mixed integer programming model for a closed-loop supply chain (CLSC) network with multi-periods and multi-parts under two main policies as secondary market pricing and incremental incentive policies. In the first policy, customers order and receive products from distribution centres, but at next period, they can trade among themselves with used products that are returned in a secondary market. Financial incentives are offered to the customers to influence the returns, and the correct amount of collections at different prices is determined by the second policy. In addition to the base case (crisp) formulation, a fuzzy multi-objective extension is applied to solve CLSC network problem with fuzzy objectives to represent vagueness in real-world problems. Then, developed genetic algorithm approach is applied to solve real size crisp and fuzzy CLSC problems. The effectiveness of the proposed meta-heuristic approach is investigated and illustrated by comparing its results with GAMS-CPLEX on a set of crisp/fuzzy problems with different sizes.en_US
dc.description.sponsorshipSelcuk University Scientific Research Project Fund (BAP)Selcuk University [12401048]; Scientific and Technological Research Council of Turkey (TUBITAK)Turkiye Bilimsel ve Teknolojik Arastirma Kurumu (TUBITAK) [111M040]en_US
dc.description.sponsorshipIn carrying out this research, the second and the third authors have been supported by the Selcuk University Scientific Research Project Fund (BAP) [grant number 12401048]; the Scientific and Technological Research Council of Turkey (TUBITAK) [grant number 111M040]. These funds are gratefully acknowledged.en_US
dc.identifier.doi10.1080/00207543.2013.879616en_US
dc.identifier.endpage3664en_US
dc.identifier.issn0020-7543en_US
dc.identifier.issn1366-588Xen_US
dc.identifier.issue12en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage3637en_US
dc.identifier.urihttps://dx.doi.org/10.1080/00207543.2013.879616
dc.identifier.urihttps://hdl.handle.net/20.500.12395/30539
dc.identifier.volume52en_US
dc.identifier.wosWOS:000333885200012en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherTAYLOR & FRANCIS LTDen_US
dc.relation.ispartofINTERNATIONAL JOURNAL OF PRODUCTION RESEARCHen_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.subjectincremental incentiveen_US
dc.subjectsecondary marketen_US
dc.subjectmixed integer programmingen_US
dc.subjectclosed-loop supply chainen_US
dc.subjectfuzzy multi-objectiveen_US
dc.titleA genetic algorithm approach for optimising a closed-loop supply chain network with crisp and fuzzy objectivesen_US
dc.typeArticleen_US

Dosyalar

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu