Energy-efficient distributed permutation flow shop scheduling problem using a multi-objective whale swarm algorithm

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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.authorWang G.
dc.contributor.authorGao L.
dc.contributor.authorLi X.
dc.contributor.authorLi P.
dc.contributor.authorTaşgetiren, Mehmet Fatih
dc.date.accessioned2020-08-30T20:01:33Z
dc.date.available2020-08-30T20:01:33Z
dc.date.issued2020
dc.departmentİstinye Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Endüstri Mühendisliği Bölümüen_US
dc.descriptionTaşgetiren, Mehmet Fatih (isu author)
dc.description.abstractProduction scheduling is of great significance in improving production effectiveness while the energy-efficient problem is one of most concerned problems for researchers and manufacturers. Thus, this study investigates the energy-efficient distributed permutation flow shop scheduling problem (DPFSP) with the objectives of makespan and energy consumption. The DPFSP is an extension of permutation flow shop problem (PFSP) considering a set of identical factories. This paper presents a multi-objective mixed integer programming model based on the three sub-problems: allocating jobs among factories, scheduling the jobs in each factory and determining speed upon each job. A multi-objective whale swarm algorithm (MOWSA) is proposed to solve this energy-efficient DPFSP. A new problem-dependent local search is developed to improve the exploitation capability of MOWSA. Moreover, the updating exploitation mechanism is presented to enhance energy efficiency without affecting production efficiency. Finally, the extensive comparison experiments are designed to demonstrate the effectiveness of proposed MOWSA, problem-dependent local search and updating exploitation mechanism. The results indicate the effectiveness of MOWSA and the superior performance over NSGA-II, SPEA2, PAES and MDEA, and also demonstrate that the proposed algorithm can significantly reduce the energy consumption compared with other algorithms. © 2020 Elsevier B.V.en_US
dc.description.sponsorshipNatural Science Foundation of Hubei Province: 2018CFA078 National Science Fund for Distinguished Young Scholars: 51825502 National Natural Science Foundation of China: 51775216 2017QYTD04en_US
dc.description.sponsorshipThis work was supported by National Natural Science Foundation for Distinguished Young Scholars of China (Grant No. 51825502 ), National Natural Science Foundation of China (Grant No. 51775216 ), Natural Science Foundation of Hubei Province (Grant No. 2018CFA078 ) and Program for HUST Academic Frontier Youth Team (Grant No. 2017QYTD04 ).en_US
dc.identifier.citationWang, G., Gao, L., Li, X., Li, P., & Tasgetiren, M. F. (2020). Energy-efficient distributed permutation flow shop scheduling problem using a multi-objective whale swarm algorithm. Swarm and Evolutionary Computation, 100716.en_US
dc.identifier.doi10.1016/j.swevo.2020.100716en_US
dc.identifier.issn2210-6502en_US
dc.identifier.scopus2-s2.0-85085726495en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1016/j.swevo.2020.100716
dc.identifier.urihttps://hdl.handle.net/20.500.12713/278
dc.identifier.volume57en_US
dc.identifier.wosWOS:000564923700010en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorTaşgetiren, Mehmet Fatihen_US
dc.language.isoenen_US
dc.publisherElsevier B.V.en_US
dc.relation.ispartofSwarm and Evolutionary Computationen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectDistributed Permutation Flow Shopen_US
dc.subjectEnergy-Efficient Schedulingen_US
dc.subjectSetup Timesen_US
dc.subjectWhale Swarm Algorithmen_US
dc.titleEnergy-efficient distributed permutation flow shop scheduling problem using a multi-objective whale swarm algorithmen_US
dc.typeArticleen_US

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