Metaheuristic algorithms for the hybrid flowshop scheduling problem

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dc.authoridMehmet Fatih Taşgetiren / 0000-0002-5716-575X
dc.authorscopusidMehmet Fatih Taşgetiren / 6505799356
dc.authorwosidMehmet Fatih Taşgetiren / CDL-1821-2022
dc.contributor.authorÖztop, Hande
dc.contributor.authorTaşgetiren, Mehmet Fatih
dc.contributor.authorEliiyi, Deniz Türsel
dc.contributor.authorPan, Quan-Ke
dc.date.accessioned2020-08-30T20:06:29Z
dc.date.available2020-08-30T20:06:29Z
dc.date.issued2019
dc.departmentİstinye Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Endüstri Mühendisliği Bölümüen_US
dc.description.abstractThe hybrid flowshop scheduling problem (HFSP) has been widely studied in the literature, as it has many real-life applications in industry. Even though many solution approaches have been presented for the HFSP with makespan criterion, studies on HFSP with total flow time minimization have been rather limited. This study presents a mathematical model, four variants of iterated greedy algorithms and a variable block insertion heuristic for the HFSP with total flow time minimization. Based on the well-known NEH heuristic, an efficient constructive heuristic is also proposed, and compared with NEH. A detailed design of experiment is carried out to calibrate the parameters of the proposed algorithms. The HFSP benchmark suite is used for evaluating the performance of the proposed methods. As there are only 10 large instances in the current literature, further 30 large instances are proposed as new benchmarks. The developed model is solved for all instances on CPLEX under a time limit, and the performances of the proposed algorithms are assessed through comparisons with the results from CPLEX and the two best-performing algorithms in literature. Computational results show that the proposed algorithms are very effective in terms of solution time and quality. Additionally, the proposed algorithms are tested on large instances for the makespan criterion, which reveal that they also perform superbly for the makespan objective. Especially for instances with 30 jobs, the proposed algorithms are able to find the current incumbent makespan values reported in literature, and provide three new best solutions. (C) 2019 Elsevier Ltd. All rights reserved.en_US
dc.description.sponsorshipNational Natural Science Foundation of ChinaNational Natural Science Foundation of China (NSFC) [51435009]en_US
dc.description.sponsorshipM. Fatih Tasgetiren and Quan-Ke Pan acknowledge the HUST Project in Wuhan in China. They are supported by the National Natural Science Foundation of China (grant no: 51435009). The authors would also like to thank the anonymous referees for their insightful comments on a previous version of the paper.en_US
dc.identifier.citationÖztop, H., Tasgetiren, M. F., Eliiyi, D. T., & Pan, Q. K. (2019). Metaheuristic algorithms for the hybrid flowshop scheduling problem. Computers & Operations Research, 111, 177-196.en_US
dc.identifier.doi10.1016/j.cor.2019.06.009en_US
dc.identifier.endpage196en_US
dc.identifier.issn0305-0548en_US
dc.identifier.issn1873-765Xen_US
dc.identifier.scopus2-s2.0-85068125455en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage177en_US
dc.identifier.urihttps://doi.org/10.1016/j.cor.2019.06.009
dc.identifier.urihttps://hdl.handle.net/20.500.12713/532
dc.identifier.volume111en_US
dc.identifier.wosWOS:000483411600013en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorTaşgetiren, Mehmet Fatihen_US
dc.language.isoenen_US
dc.publisherPergamon-Elsevier Science Ltden_US
dc.relation.ispartofComputers & Operations Researchen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectHybrid Flowshop Schedulingen_US
dc.subjectBlock Insertion Heuristicen_US
dc.subjectIterated Greedy Algorithmen_US
dc.subjectMakespanen_US
dc.subjectTotal Flow Timeen_US
dc.titleMetaheuristic algorithms for the hybrid flowshop scheduling problemen_US
dc.typeArticleen_US

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