A hybrid method for the probabilistic maximal covering location-allocation problem

dc.contributorUniversidade Estadual Paulista (Unesp)
dc.contributorInteruniversity Research Centre on Enterprise Networks
dc.contributorUniversité Laval
dc.contributorInstituto Nacional de Pesquisas Espaciais (INPE)
dc.creatorPereira, Marcos Antonio [UNESP]
dc.creatorCoelho, Leandro Callegari
dc.creatorLorena, Luiz Antonio Nogueira
dc.creatorSouza, Ligia Correa de
dc.date2015-10-22T06:46:46Z
dc.date2015-10-22T06:46:46Z
dc.date2015-05-01
dc.date.accessioned2023-09-12T07:02:36Z
dc.date.available2023-09-12T07:02:36Z
dc.identifierhttp://www.sciencedirect.com/science/article/pii/S0305054814003220
dc.identifierComputers & Operations Research. Oxford: Pergamon-elsevier Science Ltd, v. 57, p. 51-59, 2015.
dc.identifier0305-0548
dc.identifierhttp://hdl.handle.net/11449/129769
dc.identifier10.1016/j.cor.2014.12.001
dc.identifierWOS:000350535600005
dc.identifier9386730770147178
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/8779098
dc.descriptionThis 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.
dc.descriptionCIRRELT
dc.descriptionDepartment of Operations and Decision Systems
dc.descriptionFaculty of Administration Sciences of Universite
dc.descriptionCanadian Natural Sciences and Engineering Research Council
dc.descriptionConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
dc.descriptionSao Paulo State Univ, BR-12516410 Guaratingueta, Brazil
dc.descriptionCIRRELT, Interuniv Res Ctr Enterprise Networks Logist &Tr, Montreal, PQ, Canada
dc.descriptionUniv Laval, Fac Sci Adm, Quebec City, PQ G1K 0A6, Canada
dc.descriptionInst Nacl Pesquisas Espaciais, BR-12221010 Sao Jose Dos Campos, Brazil
dc.descriptionSão Paulo State University, Av. Dr. Ariberto Pereira da Cunha, 333, Guaratinguetá 12516-410, Brazil
dc.descriptionCanadian Natural Sciences and Engineering Research Council: 2014-05764
dc.descriptionCNPq: 476862/2012-4
dc.descriptionCNPq: 300692/2009-9
dc.format51-59
dc.languageeng
dc.publisherElsevier B.V.
dc.relationComputers & Operations Research
dc.relation2.962
dc.relation1,916
dc.rightsAcesso restrito
dc.sourceWeb of Science
dc.subjectFacility location
dc.subjectCongested systems
dc.subjectHybrid algorithm
dc.subjectAdaptive large neighborhood search
dc.subjectExact method
dc.subjectQueueing maximal covering location-allocation model
dc.subjectPMCLAP
dc.titleA hybrid method for the probabilistic maximal covering location-allocation problem
dc.typeArtigo


Este ítem pertenece a la siguiente institución