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dc.contributor.authorPullan, Wayneen_US
dc.contributor.editorLihong Xu, Erik Goodmanen_US
dc.description.abstractThe uncapacitated facility location problem is one of finding the minimum cost subset of m facilities, where each facility has an associated establishment cost, to satisfy the demands of n users where the cost of satisfying each user from all possible facilities is known. In this paper, PBS, a population based metaheuristic for the uncapacitated facility location problem is introduced. PBS uses a genetic algorithm based meta-heuristic, primarily based on cut and paste crossover and directed mutation operators, to generate new starting points for a local search. For larger uncapacitated facility location instances, PBS is able to effectively utilise a number of computer processors. It is shown empirically that PBS achieves state-of-the-art performance for a wide range of uncapacitated facility location benchmark instances.en_US
dc.publisherAssociation for Computing Machinaryen_US
dc.publisher.placeNew Yorken_US
dc.relation.ispartofconferencename2009 World Summit on Genetic and Evolutionary Computationen_US
dc.relation.ispartofconferencetitle2009 World Summit on Genetic and Evolutionary Computationen_US
dc.relation.ispartoflocationShanghai, Chinaen_US
dc.subject.fieldofresearchInformation and Computing Sciences not elsewhere classifieden_US
dc.titleA Population Based Hybrid Meta-heuristic for the Uncapacitated Facility Location Problemen_US
dc.typeConference outputen_US
dc.type.descriptionE1 - Conference Publications (HERDC)en_US
dc.type.codeE - Conference Publicationsen_US
gro.facultyGriffith Sciences, School of Information and Communication Technologyen_US
gro.rights.copyrightCopyright 2009 ACM. Self-archiving of the author-manuscript version is not yet supported by this publisher. For information about this conference please refer to the publisher's website or contact the author.en_AU
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    Contains papers delivered by Griffith authors at national and international conferences.

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