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dc.contributor.CRUESPUNIVERSIDADE DE ESTADUAL DE CAMPINASpt_BR
dc.identifier.isbn1097-0037pt
dc.typeArtigo de Periódicopt_BR
dc.titleHeuristics For A Hub Location-routing Problempt_BR
dc.contributor.authorLopespt_BR
dc.contributor.authorMC; de Andradept_BR
dc.contributor.authorCE; de Queirozpt_BR
dc.contributor.authorTA; Resendept_BR
dc.contributor.authorMGC; Miyazawapt_BR
dc.contributor.authorFKpt_BR
unicamp.author.emailcea@research.att.compt_BR
dc.subjectHub Location-routing Problempt_BR
dc.subjectHeuristicspt_BR
dc.subjectVariable Neighborhood Descentpt_BR
dc.subjectBiased Random-key Genetic Algorithmpt_BR
dc.subjectInteger Formulationpt_BR
dc.description.abstractWe investigate a variant of the many-to-many hub location-routing problem which consists in partitioning the set of nodes of a graph into routes containing exactly one hub each, and determining an extra route interconnecting all hubs. A variable neighborhood descent with neighborhood structures based on remove/add, swap and exchange moves nested with routing and location operations is used as a local search procedure in a multistart algorithm. We also consider a sequential version of this local search in the multistart. In addition, a biased random-key genetic algorithm working with a local search routine, which also considers routing and location operations, is applied to the problem. To compare the heuristic solutions, we develop an integer programming formulation which is solved with a branch-andcut algorithm. Capacity and path elimination constraints are added in a cutting plane fashion. The separation algorithms are based on the computation of min-cut trees and on the connected components of a support graph. Computational experiments were conducted on several benchmark instances of routing problems and show that the heuristics are effective on medium to large-sized instances, while the branch-and-cut algorithm solves small to medium sized problems to optimality. These algorithms were also compared with a commercial hybrid solver showing that the heuristics are quite competitive. (C) 2016 Wiley Periodicals, Inc.en
dc.relation.ispartofNetworkspt_BR
dc.publisher.cityHOBOKENpt_BR
dc.publisherWILEY-BLACKWELLpt_BR
dc.date.issued2016pt_BR
dc.identifier.citationNetworks. WILEY-BLACKWELL, n. 68, n. 1, p. 54 - 90.pt_BR
dc.language.isoEnglishpt_BR
dc.description.volume68pt_BR
dc.description.firstpage54pt_BR
dc.description.lastpage90pt_BR
dc.rightsfechadopt_BR
dc.sourceWOSpt_BR
dc.identifier.issn1097-0037pt_BR
dc.identifier.wosidWOS:000379914900005pt_BR
dc.identifier.doi10.1002/net.21685pt_BR
dc.identifier.urlhttp://onlinelibrary.wiley.com/doi/10.1002/net.21685/fullpt_BR
dc.description.sponsorshipCNPqpt_BR
dc.description.sponsorshipFAPESPpt_BR
dc.description.sponsorshipFAPEGpt_BR
dc.description.sponsorship1Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)pt_BR
dc.description.sponsorship1Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)pt_BR
dc.date.available2016-12-06T18:29:26Z-
dc.date.accessioned2016-12-06T18:29:26Z-
dc.description.provenanceMade available in DSpace on 2016-12-06T18:29:26Z (GMT). No. of bitstreams: 1 000379914900005.pdf: 3266362 bytes, checksum: ae18b24da4319472f8ccc42ea2e22926 (MD5) Previous issue date: 2016en
dc.identifier.urihttp://repositorio.unicamp.br/jspui/handle/REPOSIP/319776-
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