Please use this identifier to cite or link to this item: https://hdl.handle.net/1959.11/61399
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dc.contributor.authorGuiliang Gongen
dc.contributor.authorDeng, Qianwangen
dc.contributor.authorChiong, Raymonden
dc.contributor.authorGong, Xuranen
dc.contributor.authorHuang, Hezhiyuanen
dc.contributor.authorHan, Wenwuen
dc.date.accessioned2024-07-10T01:01:28Z-
dc.date.available2024-07-10T01:01:28Z-
dc.date.issued2020-
dc.identifier.citationInternational Journal of Production Research, 58(12), p. 3781-3799en
dc.identifier.issn1366-588Xen
dc.identifier.issn0020-7543en
dc.identifier.urihttps://hdl.handle.net/1959.11/61399-
dc.description.abstract<p>Remanufacturing has been widely studied for its potential to achieve sustainable production in recent years. In the literature of remanufacturing research, process planning and scheduling are typically treated as two independent parts. However, these two parts are in fact interrelated and often interact with each other. Doing process planning without considering scheduling related factors can easily introduce contradictions or even infeasible solutions. In this work, we propose a mathematical model of integrated process planning and scheduling for remanufacturing (IPPSR), which simultaneously considers the process planning and scheduling problems. An effective hybrid multi-objective evolutionary algorithm (HMEA) is presented to solve the proposed IPPSR. For the HMEA, a multidimensional encoding operator is designed to get a high-quality initial population. A multidimensional crossover operator and a multidimensional mutation operator are also proposed to improve the convergence speed of the algorithm and fully exploit the solution space. Finally, a specific legalising method is used to 'legalise' possible infeasible solutions generated by the initialisation method and mutation operator. Extensive computational experiments carried out to compare the HMEA with some well-known algorithms confirm that the proposed HMEA is able to obtain more and better Pareto solutions for IPPSR.</p>en
dc.languageenen
dc.publisherTaylor & Francisen
dc.relation.ispartofInternational Journal of Production Researchen
dc.titleRemanufacturing-oriented process planning and scheduling: mathematical modelling and evolutionary optimisationen
dc.typeJournal Articleen
dc.identifier.doi10.1080/00207543.2019.1634848en
local.contributor.firstnameQianwangen
local.contributor.firstnameRaymonden
local.contributor.firstnameXuranen
local.contributor.firstnameHezhiyuanen
local.contributor.firstnameWenwuen
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.format.startpage3781en
local.format.endpage3799en
local.peerreviewedYesen
local.identifier.volume58en
local.identifier.issue12en
local.title.subtitlemathematical modelling and evolutionary optimisationen
local.contributor.lastnameDengen
local.contributor.lastnameChiongen
local.contributor.lastnameGongen
local.contributor.lastnameHuangen
local.contributor.lastnameHanen
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:1959.11/61399en
local.date.onlineversion2019-06-27-
dc.identifier.academiclevelAcademicen
dc.identifier.academiclevelAcademicen
dc.identifier.academiclevelAcademicen
dc.identifier.academiclevelAcademicen
dc.identifier.academiclevelAcademicen
dc.identifier.academiclevelAcademicen
local.title.maintitleRemanufacturing-oriented process planning and schedulingen
local.output.categorydescriptionC1 Refereed Article in a Scholarly Journalen
local.search.authorGuiliang Gongen
local.search.authorDeng, Qianwangen
local.search.authorChiong, Raymonden
local.search.authorGong, Xuranen
local.search.authorHuang, Hezhiyuanen
local.search.authorHan, Wenwuen
local.uneassociationNoen
dc.date.presented2020-
local.atsiresearchNoen
local.sensitive.culturalNoen
local.year.available2019en
local.year.published2020en
local.year.presented2020en
local.fileurl.closedpublishedhttps://rune.une.edu.au/web/retrieve/6b57fc78-60f4-4e6c-adb0-6e77fc38ae4ben
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.moved2024-07-23en
Appears in Collections:Journal Article
School of Science and Technology
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