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Flow shop scheduling with no-wait flexible lot streaming using adaptive genetic algorithm

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dc.contributor.authorKim, Kwanwoo-
dc.contributor.authorJeong, In Jae-
dc.date.accessioned2022-12-21T06:52:49Z-
dc.date.available2022-12-21T06:52:49Z-
dc.date.created2022-09-16-
dc.date.issued2007-08-
dc.identifier.issn0000-0000-
dc.identifier.urihttps://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/179728-
dc.description.abstractIn this paper, we propose a flow shop scheduling problem with no-wait flexible lot streaming. The problem involves the splitting of order quantities of different products into sublots and the consideration of alternative machines with different processing times. Sublots of a particular product are not allowed to intermingle, that is sublots of different products must be no-preemptive. The objective of the problem is the minimization of makespan. An adaptive genetic algorithm is proposed which is composed of three main steps: first step is a position-based crossover of products and four kinds of local search-based mutations to generate better generations. Second step is an iterative hill-climbing to improve the current generation. The last step is the adaptive regulation of crossover and mutation rates. Experimental results are presented for various sizes of problems to describe the performance of the proposed four local search-based mutations in adaptive algorithm-
dc.language영어-
dc.language.isoen-
dc.publisherICCSA-
dc.titleFlow shop scheduling with no-wait flexible lot streaming using adaptive genetic algorithm-
dc.typeArticle-
dc.contributor.affiliatedAuthorJeong, In Jae-
dc.identifier.doi10.1109/ICCSA.2007.45-
dc.identifier.scopusid2-s2.0-48049090895-
dc.identifier.bibliographicCitationProceedings - The 2007 International Conference on Computational Science and its Applications, ICCSA 2007, pp.474 - 479-
dc.relation.isPartOfProceedings - The 2007 International Conference on Computational Science and its Applications, ICCSA 2007-
dc.citation.titleProceedings - The 2007 International Conference on Computational Science and its Applications, ICCSA 2007-
dc.citation.startPage474-
dc.citation.endPage479-
dc.type.rimsART-
dc.type.docTypeConference Paper-
dc.description.journalClass1-
dc.description.isOpenAccessN-
dc.description.journalRegisteredClassscopus-
dc.subject.keywordPlusAlgorithms-
dc.subject.keywordPlusDiesel engines-
dc.subject.keywordPlusGenetic algorithms-
dc.subject.keywordPlusMachine shop practice-
dc.subject.keywordPlusOptimization-
dc.subject.keywordPlusScheduling-
dc.subject.keywordPlusStream flow-
dc.subject.keywordPlusAdaptive genetic algorithm-
dc.subject.keywordPlusAdaptive regulation-
dc.subject.keywordPlusComputational sciences-
dc.subject.keywordPlusCrossover and mutation-
dc.subject.keywordPlusCurrent generation-
dc.subject.keywordPlusFlow-shop scheduling-
dc.subject.keywordPlusFlowshop-scheduling problems-
dc.subject.keywordPlusHill-climbing-
dc.subject.keywordPlusInternational conferences-
dc.subject.keywordPlusLocal search-
dc.subject.keywordPlusLot streaming-
dc.subject.keywordPlusMakespan-
dc.subject.keywordPlusNo-wait-
dc.subject.keywordPlusProcessing times-
dc.subject.keywordPlusAdaptive algorithms-
dc.identifier.urlhttps://ieeexplore.ieee.org/document/4301184-
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