Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/63990
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dc.contributor.authorIbrahimov, M.-
dc.contributor.authorWagner, N.-
dc.contributor.authorMohais, A.-
dc.contributor.authorSchellenberg, S.-
dc.contributor.authorMichalewicz, Z.-
dc.date.issued2010-
dc.identifier.citationProceedings of the IEEE Congress on Evolutionary Computation, part of the 2010 IEEE World Congress on Computational Intelligence, held in Barcelona, Spain 18-23 July 2010: pp.1-8-
dc.identifier.isbn9781424481262-
dc.identifier.urihttp://hdl.handle.net/2440/63990-
dc.description.abstractThis paper discusses global optimisation from a business perspective in the context of the supply chain operations. A two-silo supply chain was built for experimentation and two approaches were used for global optimisation: a classical evolutionary approach and a cooperative coevolutionary approach. The latter approach produced higher quality solutions due to its use of communication between silos. Additionally, a second problem was presented involving an existing Australian multi-factory sheet steel business.-
dc.description.statementofresponsibilityMaksud Ibrahimov, Neal Wagner, Arvind Mohais, Sven Schellenberg and Zbigniew Michalewicz-
dc.language.isoen-
dc.publisherIEEE-
dc.relation.ispartofseriesIEEE Congress on Evolutionary Computation-
dc.rights©2010 IEEE-
dc.source.urihttp://dx.doi.org/10.1109/cec.2010.5586150-
dc.titleComparison of cooperative and classical evolutionary algorithms for global supply chain optimisation-
dc.typeConference paper-
dc.contributor.conferenceIEEE World Congress on Computational Intelligence (2010 : Barcelona, Spain)-
dc.identifier.doi10.1109/CEC.2010.5586150-
dc.publisher.placeUSA-
dc.relation.granthttp://purl.org/au-research/grants/arc/DP0985723-
dc.relation.granthttp://purl.org/au-research/grants/arc/DP0985723-
pubs.publication-statusPublished-
Appears in Collections:Aurora harvest 5
Computer Science publications

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