An efficient genetic algorithm for hybrid flow shop scheduling with multiprocessor task problems

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dc.contributor.authorEngin, Orhan
dc.contributor.authorCeran, Gulsad
dc.contributor.authorYilmaz, Mustafa K.
dc.date.accessioned2020-03-26T18:13:44Z
dc.date.available2020-03-26T18:13:44Z
dc.date.issued2011
dc.departmentSelçuk Üniversitesien_US
dc.description.abstractThe hybrid flow shop scheduling with multiprocessor task (HFSMT) problem is a substantial production scheduling problem for minimizing the makespan, and there exist many difficulties in solving large scale HFSMT problems which include many jobs, machines and tasks. The HFSMT problems known as NP-hard and the proposal of an efficient genetic algorithm (GA) were taken into consideration in this study. The numerical results prove that the computational performance of a GA depends on the factors of initial solution, reproduction, crossover, and mutation operators and probabilities. The implementation details, including a new mutation operator, were described and a full factorial experimental design was determined with our GA program by using the best values of the control parameters and the operators. After a comparison was made with the studies of Oguz [1], Oguz and Ercan [2] and Kahraman et al. [3] related to the HFSMT problems, the computational results indicated that the proposed genetic algorithm approach is very effective in terms of reduced total completion time or makespan (C-max) for the attempted problems. (C) 2010 Elsevier B. V. All rights reserved.en_US
dc.identifier.doi10.1016/j.asoc.2010.12.006en_US
dc.identifier.endpage3065en_US
dc.identifier.issn1568-4946en_US
dc.identifier.issn1872-9681en_US
dc.identifier.issue3en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage3056en_US
dc.identifier.urihttps://dx.doi.org/10.1016/j.asoc.2010.12.006
dc.identifier.urihttps://hdl.handle.net/20.500.12395/26110
dc.identifier.volume11en_US
dc.identifier.wosWOS:000287479200011en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherELSEVIERen_US
dc.relation.ispartofAPPLIED SOFT COMPUTINGen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.selcuk20240510_oaigen_US
dc.subjectHybrid flow shopen_US
dc.subjectMultiprocessor task scheduling problemsen_US
dc.subjectGenetic algorithmen_US
dc.subjectDesign of experimenten_US
dc.titleAn efficient genetic algorithm for hybrid flow shop scheduling with multiprocessor task problemsen_US
dc.typeArticleen_US

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