Please use this identifier to cite or link to this item: https://hdl.handle.net/1959.11/61387
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dc.contributor.authorGong, Guiliangen
dc.contributor.authorChiong, Raymonden
dc.contributor.authorDeng, Qianwangen
dc.contributor.authorHan, Wenwuen
dc.contributor.authorZhang, Likeen
dc.contributor.authorHuang, Danen
dc.date.accessioned2024-07-10T01:00:50Z-
dc.date.available2024-07-10T01:00:50Z-
dc.date.issued2021-
dc.identifier.citationEngineering Applications of Artificial Intelligence, v.104, p. 1-15en
dc.identifier.issn1873-6769en
dc.identifier.issn0952-1976en
dc.identifier.urihttps://hdl.handle.net/1959.11/61387-
dc.description.abstract<p>This paper studies an important extension of energy-efficient production scheduling research, where machine on/off control and machine maintenance are considered simultaneously. The inspiration of this extension is that a machine must be turned off if it needs to be maintained, and an already-turned-off machine can be maintained without needing to be restarted. We therefore formulate an energy-efficient production scheduling problem with machine maintenance through machine on/off control, aiming to optimise three objectives – the makespan, total number of machine restarts, and energy consumption – at the same time. Four rules are designed to set the machine on/off criteria, maintenance periods and predefined maintenance windows, based on solutions of the job shop scheduling problem (JSP) as a test case. Three heuristics are proposed to insert the maintenance activities into the solutions and move their maintenance-operation blocks to optimise the objectives. The effectiveness of the first rule and the moving of maintenance-operation blocks have been proven mathematically. Our proposed heuristics, unlike traditional heuristic algorithms, are expected to be applicable and effective even if we change the objectives and constraints, require minimal computational time (only a few seconds) to optimise a scheduling solution, and can solve different types of scheduling problems without needing any modification. Experiments undertaken indicate promising performance of the proposed heuristics based on 182 JSP benchmark instances.</p>en
dc.languageenen
dc.publisherElsevier Ltden
dc.relation.ispartofEngineering Applications of Artificial Intelligenceen
dc.titleEnergy-efficient production scheduling through machine on/off control during preventive maintenanceen
dc.typeJournal Articleen
dc.identifier.doi10.1016/j.engappai.2021.104359en
local.contributor.firstnameGuiliangen
local.contributor.firstnameRaymonden
local.contributor.firstnameQianwangen
local.contributor.firstnameWenwuen
local.contributor.firstnameLikeen
local.contributor.firstnameDanen
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.placeUnited Kingdomen
local.identifier.runningnumber104359en
local.format.startpage1en
local.format.endpage15en
local.peerreviewedYesen
local.identifier.volume104en
local.contributor.lastnameGongen
local.contributor.lastnameChiongen
local.contributor.lastnameDengen
local.contributor.lastnameHanen
local.contributor.lastnameZhangen
local.contributor.lastnameHuangen
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
local.identifier.unepublicationidune:1959.11/61387en
dc.identifier.academiclevelAcademicen
dc.identifier.academiclevelAcademicen
dc.identifier.academiclevelAcademicen
dc.identifier.academiclevelAcademicen
dc.identifier.academiclevelAcademicen
dc.identifier.academiclevelAcademicen
local.title.maintitleEnergy-efficient production scheduling through machine on/off control during preventive maintenanceen
local.output.categorydescriptionC1 Refereed Article in a Scholarly Journalen
local.search.authorGong, Guiliangen
local.search.authorChiong, Raymonden
local.search.authorDeng, Qianwangen
local.search.authorHan, Wenwuen
local.search.authorZhang, Likeen
local.search.authorHuang, Danen
local.uneassociationNoen
dc.date.presented2021-
local.atsiresearchNoen
local.sensitive.culturalNoen
local.year.published2021en
local.year.presented2021en
local.fileurl.closedpublishedhttps://rune.une.edu.au/web/retrieve/5735274b-4999-4fd4-b6b5-1e9b8dbd6515en
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.date.moved2024-08-23en
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School of Science and Technology
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