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An enhanced model for SDBR in a random reentrant flow shop environment

dc.citation.epage1826en_US
dc.citation.issue6en_US
dc.citation.spage1808en_US
dc.citation.volume52en_US
dc.citation.woscount0
dc.contributor.authorChang, Yung-Chiaen_US
dc.contributor.authorHuang, Wen-Tsoen_US
dc.contributor.department工業工程與管理學系zh_TW
dc.contributor.departmentDepartment of Industrial Engineering and Managementen_US
dc.date.accessioned2014-12-08T15:35:25Z
dc.date.available2014-12-08T15:35:25Z
dc.date.issued2014-03-19en_US
dc.description.abstractThis study proposed an enhanced simplified drum-buffer-rope (SDBR) model to be applied in a reentrant flow shop (RFS) in which job processing times are generated from a discrete uniform distribution and machine breakdowns are subject to an exponential distribution. In this enhanced SDBR model, the due-date assignment method, order release rule and dispatching rule were improved. The due dates and release dates of orders were determined by considering the total planned load of the capacity-constrained resource (CCR) in a random RFS. The deviation rate of buffer status is used as a dispatching rule to eliminate the influence of machine breakdowns. Simulations based on a real case company are used to evaluate the effective of the proposed model. The experimental results showed that our approach yields better performance than the other methods in terms of six due-date-related indexes when the product mix is with a large proportion of multi-reentrant orders and when the utilisation of CCR increases from 60 to 90%.en_US
dc.identifier.doi10.1080/00207543.2013.848491en_US
dc.identifier.issn0020-7543en_US
dc.identifier.journalINTERNATIONAL JOURNAL OF PRODUCTION RESEARCHen_US
dc.identifier.urihttp://dx.doi.org/10.1080/00207543.2013.848491en_US
dc.identifier.urihttps://ir.lib.nycu.edu.tw/handle/11536/23973
dc.identifier.wosnumberWOS:000331044400015
dc.language.isoen_USen_US
dc.subjectsimplified drum-buffer-ropeen_US
dc.subjectdue-date assignmenten_US
dc.subjectreentrant flow shopen_US
dc.subjectrandom environmentsen_US
dc.titleAn enhanced model for SDBR in a random reentrant flow shop environmenten_US
dc.typeArticleen_US

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