Maximum completion time under a learning effect in the permutation flowshop scheduling problem

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dc.contributor.authorMustu, Settar
dc.contributor.authorEren, Tamer
dc.date.accessioned2020-06-25T18:30:06Z
dc.date.available2020-06-25T18:30:06Z
dc.date.issued2018
dc.departmentKırıkkale Üniversitesi
dc.description.abstractThe permutation flowshop scheduling problem under a position-based learning effect is addressed in this study. Minimization of the maximum completion time (make span) is considered for the identified problem. The mathematical programming model is established to find optimal solutions for small-sized problems. Furthermore, meta-heuristics are developed to achieve effective solutions for large-sized problems encountered in real applications. These meta-heuristics are the genetic algorithm which is a population-based solution approach, the kangaroo and the variable neighborhood search algorithms which both are single-solution-based solution approaches. In addition, different solution methods, which are in the literature for similar problem structures, are also used. Improved heuristics are evaluated according to optimal results for small-sized problems and according to performance differences between each other for large-sized problems.en_US
dc.identifier.citationclosedAccessen_US
dc.identifier.endpage174en_US
dc.identifier.issn1943-670X
dc.identifier.issue2en_US
dc.identifier.scopus2-s2.0-85046005800
dc.identifier.scopusqualityQ3
dc.identifier.startpage156en_US
dc.identifier.urihttps://hdl.handle.net/20.500.12587/7558
dc.identifier.volume25en_US
dc.identifier.wosWOS:000431222700003
dc.identifier.wosqualityQ4
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherUniv Cincinnati Industrial Engineeringen_US
dc.relation.ispartofInternational Journal Of Industrial Engineering-Theory Applications And Practice
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectlearning effecten_US
dc.subjectflowshopen_US
dc.subjectmake spanen_US
dc.subjectgenetic algorithmen_US
dc.subjectkangaroo algorithmen_US
dc.subjectvariable neighborhood search algorithmen_US
dc.titleMaximum completion time under a learning effect in the permutation flowshop scheduling problemen_US
dc.typeArticle

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