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An efficient genetic method for multi-objective continuous production scheduling in industrial internet of things

dc.contributor.authorShen, Ke
dc.contributor.authorDavid, Joachim
dc.contributor.authorDe Pessemier, Toon
dc.contributor.authorMartens, Luc
dc.contributor.authorJoseph, Wout
dc.date.accessioned2021-10-27T18:05:06Z
dc.date.available2021-10-27T18:05:06Z
dc.date.issued2019
dc.identifier.urihttps://imec-publications.be/handle/20.500.12860/33984
dc.sourceIIOimport
dc.titleAn efficient genetic method for multi-objective continuous production scheduling in industrial internet of things
dc.typeProceedings paper
dc.contributor.imecauthorShen, Ke
dc.contributor.imecauthorDavid, Joachim
dc.contributor.imecauthorDe Pessemier, Toon
dc.contributor.imecauthorMartens, Luc
dc.contributor.imecauthorJoseph, Wout
dc.contributor.orcidimecDe Pessemier, Toon::0000-0002-3920-7346
dc.contributor.orcidimecMartens, Luc::0000-0001-9948-9157
dc.contributor.orcidimecJoseph, Wout::0000-0002-8807-0673
dc.date.embargo9999-12-31
dc.source.peerreviewyes
dc.source.beginpage1119
dc.source.endpage1126
dc.source.conferenceProceedings of the 24th IEEE Conference on Emerging Technologies and Factory Automation (ETFA 2019)
dc.source.conferencedate10/09/2019
dc.source.conferencelocationZaragoza Spain
dc.identifier.urlhttps://ieeexplore.ieee.org/document/8869049
imec.availabilityPublished - open access


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