Please use this identifier to cite or link to this item: https://doi.org/10.1023/A:1011253011638
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dc.titleDynamic scheduling of manufacturing job shops using genetic algorithms
dc.contributor.authorChryssolouris, G.
dc.contributor.authorSubramaniam, V.
dc.date.accessioned2014-06-17T06:18:12Z
dc.date.available2014-06-17T06:18:12Z
dc.date.issued2001-06
dc.identifier.citationChryssolouris, G., Subramaniam, V. (2001-06). Dynamic scheduling of manufacturing job shops using genetic algorithms. Journal of Intelligent Manufacturing 12 (3) : 281-293. ScholarBank@NUS Repository. https://doi.org/10.1023/A:1011253011638
dc.identifier.issn09565515
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/60013
dc.description.abstractMost job shop scheduling methods reported in the literature usually address the static scheduling problem. These methods do not consider multiple criteria, nor do they accommodate alternate resources to process a job operation. In this paper, a scheduling method based on genetic algorithms is developed and it addresses all the shortcomings mentioned above. The genetic algorithms approach is a schedule permutation approach that systematically permutes an initial pool of randomly generated schedules to return the best schedule found to date. A dynamic scheduling problem was designed to closely reflect a real job shop scheduling environment. Two performance measures, namely mean job tardiness and mean job cost, were used to demonstrate multiple criteria scheduling. To span a varied job shop environment, three factors were identified and varied between two levels each. The results of this extensive simulation study indicate that the genetic algorithms scheduling approach produces better scheduling performance in comparison to several common dispatching rules.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1023/A:1011253011638
dc.sourceScopus
dc.subjectGenetic algorithms
dc.subjectJob shop
dc.subjectManufacturing
dc.subjectScheduling
dc.typeArticle
dc.contributor.departmentMECHANICAL ENGINEERING
dc.description.doi10.1023/A:1011253011638
dc.description.sourcetitleJournal of Intelligent Manufacturing
dc.description.volume12
dc.description.issue3
dc.description.page281-293
dc.description.codenJIMNE
dc.identifier.isiut000169223900004
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