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A two-level method or production planning ana scheduling tor Bi-objective reentrant hybrid flow shops

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dc.contributor.authorCho, Hang-Min-
dc.contributor.authorJeong, In Jae-
dc.date.accessioned2022-07-14T07:57:02Z-
dc.date.available2022-07-14T07:57:02Z-
dc.date.issued2017-04-
dc.identifier.issn0360-8352-
dc.identifier.issn1879-0550-
dc.identifier.urihttps://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/152591-
dc.description.abstractThis research deals with a two-level method of production planning and scheduling problems of the reentrant hybrid flow shops. The hybrid flow shop has serial stages where each stage consists of identical parallel machines. Products can be processed at any one of the parallel machines at a stage in the hybrid flow shop. Also a product may have reentrant operations which require revisits of some stages several times. We consider a two-level hierarchical process on production planning and scheduling of the reentrant hybrid flow shop with the bi-objective function to improve productivity and customer satisfaction. Computational experiments show that the combination of preemptive goal programming based production planning algorithms and Pareto genetic based scheduling algorithms outperforms other two-level algorithms. Also we provide results of the application of the proposed method to both randomly generated problems and a real world Thin Film Transistor and Liquid Crystal Display (TFT-LCD) industry.-
dc.format.extent8-
dc.language영어-
dc.language.isoENG-
dc.publisherPergamon Press Ltd.-
dc.titleA two-level method or production planning ana scheduling tor Bi-objective reentrant hybrid flow shops-
dc.typeArticle-
dc.publisher.locationUnited Kingdom-
dc.identifier.doi10.1016/j.cie.2017.02.010-
dc.identifier.scopusid2-s2.0-85013175279-
dc.identifier.wosid000397820300012-
dc.identifier.bibliographicCitationComputers and Industrial Engineering, v.106, pp 174 - 181-
dc.citation.titleComputers and Industrial Engineering-
dc.citation.volume106-
dc.citation.startPage174-
dc.citation.endPage181-
dc.type.docTypeArticle-
dc.description.isOpenAccessN-
dc.description.journalRegisteredClassscie-
dc.description.journalRegisteredClassscopus-
dc.relation.journalResearchAreaComputer Science-
dc.relation.journalResearchAreaEngineering-
dc.relation.journalWebOfScienceCategoryComputer Science, Interdisciplinary Applications-
dc.relation.journalWebOfScienceCategoryEngineering, Industrial-
dc.subject.keywordPlusSEMICONDUCTOR FABRICATION-
dc.subject.keywordPlusGENETIC ALGORITHM-
dc.subject.keywordPlusOPTIMIZATION-
dc.subject.keywordPlusBALANCE-
dc.subject.keywordPlusLINE-
dc.subject.keywordAuthorBi-objective-
dc.subject.keywordAuthorReentrant hybrid flow shop-
dc.subject.keywordAuthorGenetic algorithm-
dc.subject.keywordAuthorDelayed customer demand-
dc.subject.keywordAuthorHierarchical planning-
dc.identifier.urlhttps://www.sciencedirect.com/science/article/pii/S0360835217300712?via%3Dihub-
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