A HARMONY SEARCH ALGORITHM FOR HYBRID FLOW SHOP SCHEDULING WITH MULTIPROCESSOR TASK PROBLEMS

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dc.contributor.authorAkkoyunlu, Mehmet Cabir
dc.contributor.authorEngin, Orhan
dc.contributor.authorBüyüközkan, Kadir
dc.date.accessioned2020-03-26T19:00:23Z
dc.date.available2020-03-26T19:00:23Z
dc.date.issued2015
dc.departmentSelçuk Üniversitesien_US
dc.description6th International Conference on Modeling, Simulation, and Applied Optimization (ICMSAO) -- MAY 27-29, 2015 -- Istanbul, TURKEYen_US
dc.description.abstractMultiprocessor task can be stated as finding a schedule for a general graph to execute on a multiprocessor system. In this paper an efficient harmony search algorithm (HSA) is proposed to solve the hybrid flow shop scheduling with multiprocessor task problems (HFSMTP). The best values of HFS's control parameters are determined by full factorial design. Computational results are compared with the genetic algorithm related to the HFSMTP at the literature. The result showed that the proposed HSA is effective for solving HFSMTP.en_US
dc.identifier.isbn978-1-4673-6601-4
dc.identifier.issn2473-4748en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.urihttps://hdl.handle.net/20.500.12395/31759
dc.identifier.wosWOS:000380551800051en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.relation.ispartof2015 6TH INTERNATIONAL CONFERENCE ON MODELING, SIMULATION, AND APPLIED OPTIMIZATION (ICMSAO)en_US
dc.relation.ispartofseriesInternational Conference on Modeling Simulation and Applied Optimization
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.selcuk20240510_oaigen_US
dc.subjectHybrid flow shopen_US
dc.subjectmultiprocessor task scheduling problemen_US
dc.subjectharmony search algorithmen_US
dc.titleA HARMONY SEARCH ALGORITHM FOR HYBRID FLOW SHOP SCHEDULING WITH MULTIPROCESSOR TASK PROBLEMSen_US
dc.typeConference Objecten_US

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