Please use this identifier to cite or link to this item: https://hdl.handle.net/1959.11/61377
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dc.contributor.authorLi, Debiaoen
dc.contributor.authorWang, Jingen
dc.contributor.authorQiang, Ruien
dc.contributor.authorChiong, Raymonden
dc.date.accessioned2024-07-10T01:00:23Z-
dc.date.available2024-07-10T01:00:23Z-
dc.date.issued2021-
dc.identifier.citationInternational Journal of Production Research, 59(9), p. 2722-2738en
dc.identifier.issn1366-588Xen
dc.identifier.issn0020-7543en
dc.identifier.urihttps://hdl.handle.net/1959.11/61377-
dc.description.abstract<p>Dyeing is the most time and energy-consuming process in textile production. Motivated by a dyeing overdue problem in a lace textile factory, we study a parallel machine scheduling problem with different colour families, sequence-dependent setup times, and machine eligibility restriction. An integer programming model is formulated to minimise the total tardiness. Given that the dyeing optimisation problem is strongly NP-hard, a hybrid differential evolution (HDE) algorithm embedded with chaos theory and two local search algorithms is proposed to solve real-world instances from the textile factory. In our proposed algorithm, a special encoding and decoding scheme is designed to deal with the machine eligibility constraint, and chaos theory is adopted to determine the parameter settings of the underlying differential evolution (DE) algorithm. To speed up convergence and improve search exploitation, two local search algorithms inspired by two dominance properties are developed to determine the optimal job sequence for parallel machines, such that the decision of the entire problem is simplified to the assignment of jobs among the machines, and the computational time required is significantly reduced. Comprehensive experiments based on 36 synthetically generated small to large-scale problem instances and 20 real-world industrial data sets confirm the efficacy of our proposed HDE over other DE variants.</p>en
dc.languageenen
dc.publisherTaylor & Francisen
dc.relation.ispartofInternational Journal of Production Researchen
dc.titleA hybrid differential evolution algorithm for parallel machine scheduling of lace dyeing considering colour families, sequence-dependent setup and machine eligibilityen
dc.typeJournal Articleen
dc.identifier.doi10.1080/00207543.2020.1740341en
local.contributor.firstnameDebiaoen
local.contributor.firstnameJingen
local.contributor.firstnameRuien
local.contributor.firstnameRaymonden
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.startpage2722en
local.format.endpage2738en
local.peerreviewedYesen
local.identifier.volume59en
local.identifier.issue9en
local.contributor.lastnameLien
local.contributor.lastnameWangen
local.contributor.lastnameQiangen
local.contributor.lastnameChiongen
dc.identifier.staffune-id:rchiongen
local.profile.orcid0000-0002-8285-1903en
local.profile.roleauthoren
local.profile.roleauthoren
local.profile.roleauthoren
local.profile.roleauthoren
local.identifier.unepublicationidune:1959.11/61377en
dc.identifier.academiclevelAcademicen
dc.identifier.academiclevelAcademicen
dc.identifier.academiclevelAcademicen
dc.identifier.academiclevelAcademicen
local.title.maintitleA hybrid differential evolution algorithm for parallel machine scheduling of lace dyeing considering colour families, sequence-dependent setup and machine eligibilityen
local.output.categorydescriptionC1 Refereed Article in a Scholarly Journalen
local.search.authorLi, Debiaoen
local.search.authorWang, Jingen
local.search.authorQiang, Ruien
local.search.authorChiong, Raymonden
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/9c4a35f9-7e99-43f1-b30b-3455fcd2bbdden
local.subject.for20204602 Artificial intelligenceen
local.profile.affiliationtypeExternal Affiliationen
local.profile.affiliationtypeExternal Affiliationen
local.profile.affiliationtypeExternal Affiliationen
local.profile.affiliationtypeExternal Affiliationen
local.date.moved2024-07-26en
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School of Science and Technology
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