Balancing and sequencing mixed-model just-in-time U-lines with multiple objectives

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dc.contributor.authorKara, Yakup
dc.contributor.authorOzcan, Ugur
dc.contributor.authorPeker, Ahmet
dc.date.accessioned2020-03-26T17:17:02Z
dc.date.available2020-03-26T17:17:02Z
dc.date.issued2007
dc.departmentSelçuk Üniversitesien_US
dc.description.abstractThis study deals with the mixed-model U-lines utilized in just-in-time (JIT) production systems. Successful implementations of mixed-model U-lines requires solutions to two important problems called line balancing and model sequencing. In terms of some balance-dependent performance measures the effectiveness of a mixed-model U-line can be increased by solving line balancing and model sequencing problems simultaneously. However, this may lead to inefficient values of sequence-dependent performance measures. Hence, increasing the effectiveness of a mixed-model U-line requires balancing and sequencing problems that be dealt with multiple objectives. Balancing and sequencing mixed-model U-lines with multiple objectives has not been considered in the literature to date. In this study, a multi-objective approach for balancing and sequencing mixed-model U-lines to simultaneously minimize the absolute deviations of workloads across workstations, part usage rate, and cost of setups is presented. To increase the performance of the proposed algorithm, a newly developed neighbourhood generation method is also employed. Since the performance measures considered in the study are conflicting with each other, the proposed algorithm suggests much flexibility and more realistic results to decision makers. Solution methodology is illustrated using an example and a two-stage comprehensive experimental study is conducted to determine the effective values of algorithm parameters and investigate the relationships between performance measures. Results show that the proposed approach is more realistic than the limited number of existing methodologies. The proposed approach is also extended to consider the stochastic completion times of tasks. (C) 2006 Elsevier Inc. All rights reserved.en_US
dc.identifier.doi10.1016/j.amc.2006.05.185en_US
dc.identifier.endpage588en_US
dc.identifier.issn0096-3003en_US
dc.identifier.issue2en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage566en_US
dc.identifier.urihttps://dx.doi.org/10.1016/j.amc.2006.05.185
dc.identifier.urihttps://hdl.handle.net/20.500.12395/21236
dc.identifier.volume184en_US
dc.identifier.wosWOS:000246431500038en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherELSEVIER SCIENCE INCen_US
dc.relation.ispartofAPPLIED MATHEMATICS AND COMPUTATIONen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.selcuk20240510_oaigen_US
dc.subjectmixed-model U-linesen_US
dc.subjectmultiple objectivesen_US
dc.subjectsimulated annealingen_US
dc.titleBalancing and sequencing mixed-model just-in-time U-lines with multiple objectivesen_US
dc.typeArticleen_US

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