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dc.contributor.authorLow, Chinyaoen_US
dc.contributor.authorLi, Rong-Kweien_US
dc.contributor.authorChang, Chien-Minen_US
dc.date.accessioned2014-12-08T15:29:33Z-
dc.date.available2014-12-08T15:29:33Z-
dc.date.issued2013-02-01en_US
dc.identifier.issn0020-7543en_US
dc.identifier.urihttp://dx.doi.org/10.1080/00207543.2012.677071en_US
dc.identifier.urihttp://hdl.handle.net/11536/21244-
dc.description.abstractThis paper deals with an integrated scheduling problem in which orders have been processed by a distribution centre and then delivered to retailers within time windows. We propose a nonlinear mathematical model to minimise the time required to complete producing the product, delivering it to retailers and returning to the distribution centre. The optimal schedule and vehicle routes can be determined simultaneously in the model. In addition, two kinds of genetic-algorithm-based heuristics are designed to solve the large-scale problems. The conventional genetic algorithm provides the search with a high transition probability in the beginning of the search and with a low probability toward the end of the search. The adaptive genetic algorithm provides an adaptive operation rate control scheme that changes rate based on the fitness of the parents. The experimental results have shown that the solution quality of these two algorithms is not significant but that the adaptive genetic algorithm can save more time in finding the best parameter values of the genetic algorithm.en_US
dc.language.isoen_USen_US
dc.subjectintegrated schedulingen_US
dc.subjectvehicle routeen_US
dc.subjecttime windowsen_US
dc.subjectgenetic algorithmen_US
dc.subjectadaptive operationen_US
dc.titleIntegrated scheduling of production and delivery with time windowsen_US
dc.typeArticleen_US
dc.identifier.doi10.1080/00207543.2012.677071en_US
dc.identifier.journalINTERNATIONAL JOURNAL OF PRODUCTION RESEARCHen_US
dc.citation.volume51en_US
dc.citation.issue3en_US
dc.citation.spage897en_US
dc.citation.epage909en_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.identifier.wosnumberWOS:000313036700017-
dc.citation.woscount1-
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