Solving energy-efficient fuzzy hybrid flow-shop scheduling problem at a variable machine speed using an extended NSGA-II

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dc.contributor.authorWang, Yi-Jian
dc.contributor.authorWang, Gai-Ge
dc.contributor.authorTian, Fang-Ming
dc.contributor.authorGong, Dun-Wei
dc.contributor.authorPedrycz, Witold
dc.date.accessioned2024-05-19T14:41:16Z
dc.date.available2024-05-19T14:41:16Z
dc.date.issued2023
dc.departmentİstinye Üniversitesien_US
dc.description.abstractAs environmental problems are increasingly challenging and sustainable development win support among the people, the energy-efficient hybrid flow-shop scheduling problem (HFSP), as a scheduling problem with great application value, has been widely concerned. However, most existing research has focused on deterministic cases and uncertainty is rarely considered in energy-efficient HFSP (EHFSP), especially with various machine speed constraints. Uncertainty is often caused by some uncontrollable factors, such as human factors and ignoring uncertainty will greatly reduce the application value of the problem solutions. In this study, an energy-efficient fuzzy HFSP (EFHFSP) at a variable machine speed is considered and the existing non-dominated sorting genetic algorithm-II (NSGA-II) is extended to minimize fuzzy make-span and total fuzzy energy consumption simultaneously. The computation of total fuzzy energy consumption is given and reverse learning is proposed to produce the initial population. ENSGA-II adopts an effective genetic operator and its parameters (Pc and Pm) are adjustive. A novel strategy based on history information is also used to produce high-quality solutions. Extensive experiments are conducted to test the performance of ENSGA-II. ENSGA-II can provide promising results for EFHFSP.en_US
dc.description.sponsorshipNational Natural Science Foundation of China [62076182]en_US
dc.description.sponsorshipAcknowledgments This work was supported in part by the National Natural Science Foundation of China under Grant 62076182 (W. Pedrycz) .en_US
dc.identifier.doi10.1016/j.engappai.2023.105977
dc.identifier.issn0952-1976
dc.identifier.issn1873-6769
dc.identifier.scopus2-s2.0-85148329931en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org10.1016/j.engappai.2023.105977
dc.identifier.urihttps://hdl.handle.net/20.500.12713/5087
dc.identifier.volume121en_US
dc.identifier.wosWOS:000945929700001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherPergamon-Elsevier Science Ltden_US
dc.relation.ispartofEngineering Applications of Artificial Intelligenceen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmz20240519_kaen_US
dc.subjectEnergy-Efficienten_US
dc.subjectFuzzy Theoryen_US
dc.subjectHybrid Flow-Shop Scheduling Problemen_US
dc.subjectNsga-Iien_US
dc.titleSolving energy-efficient fuzzy hybrid flow-shop scheduling problem at a variable machine speed using an extended NSGA-IIen_US
dc.typeArticleen_US

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