Please use this identifier to cite or link to this item: http://www.repositorio.ufop.br/jspui/handle/123456789/11352
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dc.contributor.authorRezende, Josiane da Costa Vieira-
dc.contributor.authorSouza, Marcone Jamilson Freitas-
dc.contributor.authorCoelho, Vitor Nazário-
dc.contributor.authorMartins, Alexandre Xavier-
dc.date.accessioned2019-05-28T14:32:22Z-
dc.date.available2019-05-28T14:32:22Z-
dc.date.issued2018-
dc.identifier.citationREZENDE, J. da C. V. et al. HMS : a hybrid multi-start algorithm for solving binary linear programs. Electronic Notes In Discrete Mathematics, v. 66, p. 7-14, abr. 2018. Disponível em: <https://www.sciencedirect.com/science/article/pii/S1571065318300489>. Acesso em: 19 fev. 2019.pt_BR
dc.identifier.issn1571-0653-
dc.identifier.urihttp://www.repositorio.ufop.br/handle/123456789/11352-
dc.description.abstractThis work presents a hybrid multi-start algorithm for solving generic binary linear programs. This algorithm, called HMS, is based on a Multi-Start Metaheuristic and combines exact and heuristic strategies to address the problem. The initial solutions are generated by a strategy that applies linear programming and constraint propagation for defining an optimized set of fixed variables. In order to refine them, a local search, guided by a Variable Neighborhood Descent heuristic, is called, which, in turn, uses Local Branching cuts. The algorithm was tested in a set of binary LPs from the MIPLIB 2010 library and the results pointed out its competitive performance, resulting in a promising matheuristic.pt_BR
dc.language.isoen_USpt_BR
dc.rightsrestritopt_BR
dc.subjectVariable neighborhood descentpt_BR
dc.subjectHeuristicpt_BR
dc.subjectLocal branchingpt_BR
dc.subjectBinary problemspt_BR
dc.subjectConstraint propagationpt_BR
dc.titleHMS : a hybrid multi-start algorithm for solving binary linear programs.pt_BR
dc.typeArtigo publicado em periodicopt_BR
dc.identifier.uri2https://www.sciencedirect.com/science/article/pii/S1571065318300489pt_BR
dc.identifier.doihttps://doi.org/10.1016/j.endm.2018.03.002pt_BR
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