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Utilize este identificador para citar ou criar um link para este item: http://acervodigital.unesp.br/handle/11449/129769
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dc.contributor.authorPereira, Marcos Antonio-
dc.contributor.authorCoelho, Leandro Callegari-
dc.contributor.authorLorena, Luiz Antonio Nogueira-
dc.contributor.authorSouza, Ligia Correa de-
dc.date.accessioned2015-10-22T06:46:46Z-
dc.date.accessioned2016-10-25T21:16:18Z-
dc.date.available2015-10-22T06:46:46Z-
dc.date.available2016-10-25T21:16:18Z-
dc.date.issued2015-05-01-
dc.identifierhttp://www.sciencedirect.com/science/article/pii/S0305054814003220-
dc.identifier.citationComputers & Operations Research. Oxford: Pergamon-elsevier Science Ltd, v. 57, p. 51-59, 2015.-
dc.identifier.issn0305-0548-
dc.identifier.urihttp://hdl.handle.net/11449/129769-
dc.identifier.urihttp://acervodigital.unesp.br/handle/11449/129769-
dc.description.abstractThis paper presents a hybrid algorithm that combines a metaheuristic and an exact method to solve the Probabilistic Maximal Covering Location-Allocation Problem. A linear programming formulation for the problem presents variables that can be partitioned into location and allocation decisions. This model is solved to optimality for small- and medium-size instances. To tackle larger instances, a flexible adaptive large neighborhood search heuristic was developed to obtain location solutions, whereas the allocation subproblems are solved to optimality. An improvement procedure based on an integer programming method is also applied. Extensive computational experiments on benchmark instances from the literature confirm the efficiency of the proposed method. The exact approach found new best solutions for 19 instances, proving the optimality for 18 of them. The hybrid method performed consistently, finding the best known solutions for 94.5% of the instances and 17 new best solutions (15 of them optimal) for a larger dataset in one-third of the time of a state-of-the-art solver. (C) 2014 Elsevier Ltd. All rights reserved.en
dc.description.sponsorshipCIRRELT-
dc.description.sponsorshipDepartment of Operations and Decision Systems-
dc.description.sponsorshipFaculty of Administration Sciences of Universite-
dc.description.sponsorshipCanadian Natural Sciences and Engineering Research Council-
dc.description.sponsorshipConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)-
dc.format.extent51-59-
dc.language.isoeng-
dc.publisherElsevier B.V.-
dc.sourceWeb of Science-
dc.subjectFacility locationen
dc.subjectCongested systemsen
dc.subjectHybrid algorithmen
dc.subjectAdaptive large neighborhood searchen
dc.subjectExact methoden
dc.subjectQueueing maximal covering location-allocation modelen
dc.subjectPMCLAPen
dc.titleA hybrid method for the probabilistic maximal covering location-allocation problemen
dc.typeoutro-
dc.contributor.institutionUniversidade Estadual Paulista (UNESP)-
dc.contributor.institutionInteruniversity Research Centre on Enterprise Networks-
dc.contributor.institutionUniversité Laval-
dc.contributor.institutionInstituto Nacional de Pesquisas Espaciais (INPE)-
dc.description.affiliationSao Paulo State Univ, BR-12516410 Guaratingueta, Brazil-
dc.description.affiliationCIRRELT, Interuniv Res Ctr Enterprise Networks Logist &Tr, Montreal, PQ, Canada-
dc.description.affiliationUniv Laval, Fac Sci Adm, Quebec City, PQ G1K 0A6, Canada-
dc.description.affiliationInst Nacl Pesquisas Espaciais, BR-12221010 Sao Jose Dos Campos, Brazil-
dc.description.affiliationUnespSão Paulo State University, Av. Dr. Ariberto Pereira da Cunha, 333, Guaratinguetá 12516-410, Brazil-
dc.description.sponsorshipIdCanadian Natural Sciences and Engineering Research Council: 2014-05764-
dc.description.sponsorshipIdCNPq: 476862/2012-4-
dc.description.sponsorshipIdCNPq: 300692/2009-9-
dc.identifier.doihttp://dx.doi.org/10.1016/j.cor.2014.12.001-
dc.identifier.wosWOS:000350535600005-
dc.rights.accessRightsAcesso restrito-
dc.relation.ispartofComputers & Operations Research-
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