Please use this identifier to cite or link to this item: https://doi.org/10.1007/978-3-540-48584-1_22
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dc.titleEvolutionary algorithm for an inventory location problem
dc.contributor.authorChew, E.P.
dc.contributor.authorLee, L.H.
dc.contributor.authorRajaratnam, K.
dc.date.accessioned2014-06-17T07:00:26Z
dc.date.available2014-06-17T07:00:26Z
dc.date.issued2007
dc.identifier.citationChew, E.P.,Lee, L.H.,Rajaratnam, K. (2007). Evolutionary algorithm for an inventory location problem. Studies in Computational Intelligence 49 : 613-628. ScholarBank@NUS Repository. <a href="https://doi.org/10.1007/978-3-540-48584-1_22" target="_blank">https://doi.org/10.1007/978-3-540-48584-1_22</a>
dc.identifier.isbn3540485821
dc.identifier.issn1860949X
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/63124
dc.description.abstractThis paper deals with minimizing the cost in a joint location-inventory model with a single supplier supplying to multiple capitated distribution centers. The distribution center faces stochatic demands from multiple retailers. The problem is to determine how to assign retailers to distribution center within the service level constraints. The costs considered include the transportation cost, inventory holding cost and ordering cost. We develop an adaptive realcoded genetic algorithm to solve the problem. We conduct few experiment runs to compare the performance of the proposed method with some existing methods which include the simple genetic algorithm, the column generation method and the greedy method. For the non-capacitated case, the method shows very promising results with respect to both time and quality of the solutions. Similarly for the capacitated case, where the column generation method cannot be applied, the model is also significantly better than all the other methods, especially when the problem size is big. © Springer-Verlag Berlin Heidelberg 2007.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1007/978-3-540-48584-1_22
dc.sourceScopus
dc.typeArticle
dc.contributor.departmentINDUSTRIAL & SYSTEMS ENGINEERING
dc.description.doi10.1007/978-3-540-48584-1_22
dc.description.sourcetitleStudies in Computational Intelligence
dc.description.volume49
dc.description.page613-628
dc.identifier.isiutNOT_IN_WOS
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