Please use this identifier to cite or link to this item: https://doi.org/10.1023/A:1024653810491
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dc.titleA modified genetic algorithm for distributed scheduling problems
dc.contributor.authorJia, H.Z.
dc.contributor.authorNee, A.Y.C.
dc.contributor.authorFuh, J.Y.H.
dc.contributor.authorZhang, Y.F.
dc.date.accessioned2014-06-16T09:30:57Z
dc.date.available2014-06-16T09:30:57Z
dc.date.issued2003-06
dc.identifier.citationJia, H.Z., Nee, A.Y.C., Fuh, J.Y.H., Zhang, Y.F. (2003-06). A modified genetic algorithm for distributed scheduling problems. Journal of Intelligent Manufacturing 14 (3-4) : 351-362. ScholarBank@NUS Repository. https://doi.org/10.1023/A:1024653810491
dc.identifier.issn09565515
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/54423
dc.description.abstractGenetic algorithms (GAs) have been widely applied to the scheduling and sequencing problems due to its applicability to different domains and the capability in obtaining near-optimal results. Many investigated GAs are mainly concentrated on the traditional single factory or single job-shop scheduling problems. However, with the increasing popularity of distributed, or globalized production, the previously used GAs are required to be further explored in order to deal with the newly emerged distributed scheduling problems. In this paper, a modified GA is presented, which is capable of solving traditional scheduling problems as well as distributed scheduling problems. Various scheduling objectives can be achieved including minimizing makespan, cost and weighted multiple criteria. The proposed algorithm has been evaluated with satisfactory results through several classical scheduling benchmarks. Furthermore, the capability of the modified GA was also tested for handling the distributed scheduling problems.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1023/A:1024653810491
dc.sourceScopus
dc.subjectDistributed production
dc.subjectDistributed scheduling
dc.subjectGenetic algorithms
dc.typeArticle
dc.contributor.departmentMECHANICAL ENGINEERING
dc.description.doi10.1023/A:1024653810491
dc.description.sourcetitleJournal of Intelligent Manufacturing
dc.description.volume14
dc.description.issue3-4
dc.description.page351-362
dc.description.codenJIMNE
dc.identifier.isiut000184041000008
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