Please use this identifier to cite or link to this item: https://hdl.handle.net/1959.11/61366
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dc.contributor.authorGong, Guiliangen
dc.contributor.authorChiong, Raymonden
dc.contributor.authorDeng, Qianwangen
dc.contributor.authorGong, Xuranen
dc.contributor.authorLin, Wenhuien
dc.contributor.authorHan, Wenwuen
dc.contributor.authorZhang, Likeen
dc.date.accessioned2024-07-10T00:59:50Z-
dc.date.available2024-07-10T00:59:50Z-
dc.date.issued2022-
dc.identifier.citationSwarm and Evolutionary Computation, v.75, p. 1-18en
dc.identifier.issn2210-6510en
dc.identifier.issn2210-6502en
dc.identifier.urihttps://hdl.handle.net/1959.11/61366-
dc.description.abstract<p>Machine on/off control is an effective way to achieve energy-efficient production scheduling. Turning off machines and restarting them frequently, however, would incur a considerable amount of additional energy and may even cause damage to the machines. In this paper, we propose a mathematical model based on the energyefficient flexible job shop scheduling problem (EEFJSP), aiming to minimize not just the makespan and total energy consumption but also the total number of machine restarts. Our idea here is that shifting the start time of operations on different machines appropriately can effectively decrease the number of restarts required and the total energy consumption. We present a two-stage memetic algorithm (TMA) to solve the EEFJSP. A variable neighborhood search approach is designed to improve the convergence speed and fully exploit the solution space of the TMA. An operation-block moving operator is developed to further reduce the total energy consumption as well as the total number of machine restarts without affecting the makespan. Extensive computational experiments carried out to compare the TMA with some well-known algorithms confirm that the proposed TMA can easily obtain better Pareto solutions for the EEFJSP.</p>en
dc.languageenen
dc.publisherElsevier BVen
dc.relation.ispartofSwarm and Evolutionary Computationen
dc.titleA two-stage memetic algorithm for energy-efficient flexible job shop scheduling by means of decreasing the total number of machine restartsen
dc.typeJournal Articleen
dc.identifier.doi10.1016/j.swevo.2022.101131en
local.contributor.firstnameGuiliangen
local.contributor.firstnameRaymonden
local.contributor.firstnameQianwangen
local.contributor.firstnameXuranen
local.contributor.firstnameWenhuien
local.contributor.firstnameWenwuen
local.contributor.firstnameLikeen
local.profile.schoolSchool of Science & Technologyen
local.profile.emailrchiong@une.edu.auen
local.output.categoryC1en
local.record.placeauen
local.record.institutionUniversity of New Englanden
local.publisher.placeThe Netherlandsen
local.identifier.runningnumber101131en
local.format.startpage1en
local.format.endpage18en
local.peerreviewedYesen
local.identifier.volume75en
local.contributor.lastnameGongen
local.contributor.lastnameChiongen
local.contributor.lastnameDengen
local.contributor.lastnameGongen
local.contributor.lastnameLinen
local.contributor.lastnameHanen
local.contributor.lastnameZhangen
dc.identifier.staffune-id:rchiongen
local.profile.orcid0000-0002-8285-1903en
local.profile.roleauthoren
local.profile.roleauthoren
local.profile.roleauthoren
local.profile.roleauthoren
local.profile.roleauthoren
local.profile.roleauthoren
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local.identifier.unepublicationidune:1959.11/61366en
dc.identifier.academiclevelAcademicen
dc.identifier.academiclevelAcademicen
dc.identifier.academiclevelAcademicen
dc.identifier.academiclevelAcademicen
dc.identifier.academiclevelAcademicen
dc.identifier.academiclevelAcademicen
dc.identifier.academiclevelAcademicen
local.title.maintitleA two-stage memetic algorithm for energy-efficient flexible job shop scheduling by means of decreasing the total number of machine restartsen
local.relation.fundingsourcenoteThis work was supported by the National Nature Science Foundation of China (Grant 72001217)" the Nature Science Foundation of Hunan (Grant 2021JJ41081)" the Nature Science Foundation of Changsha (Grant kq2007033)" the National Key R&D Program of China (Grant 2018YFB1701400)" the State Key Laboratory of Construction Machinery (Grant SKLCM2019-03)" and the Foshan Technological Innovation Project (Grant 1920001000041).en
local.output.categorydescriptionC1 Refereed Article in a Scholarly Journalen
local.search.authorGong, Guiliangen
local.search.authorChiong, Raymonden
local.search.authorDeng, Qianwangen
local.search.authorGong, Xuranen
local.search.authorLin, Wenhuien
local.search.authorHan, Wenwuen
local.search.authorZhang, Likeen
local.uneassociationNoen
dc.date.presented2022-
local.atsiresearchNoen
local.sensitive.culturalNoen
local.year.published2022en
local.year.presented2022en
local.fileurl.closedpublishedhttps://rune.une.edu.au/web/retrieve/8368c38b-f4aa-4cb6-9a82-f246ca23eea0en
local.subject.for20204602 Artificial intelligenceen
local.profile.affiliationtypeExternal Affiliationen
local.profile.affiliationtypeExternal Affiliationen
local.profile.affiliationtypeExternal Affiliationen
local.profile.affiliationtypeExternal Affiliationen
local.profile.affiliationtypeExternal Affiliationen
local.profile.affiliationtypeExternal Affiliationen
local.profile.affiliationtypeExternal Affiliationen
local.date.moved2024-07-23en
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School of Science and Technology
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