Please use this identifier to cite or link to this item: http://www.repositorio.ufop.br/handle/123456789/11351
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dc.contributor.authorCampos, Jean Carlos Tiburcio-
dc.contributor.authorMartins, Alexandre Xavier-
dc.contributor.authorSouza, Marcone Jamilson Freitas-
dc.date.accessioned2019-05-28T14:27:09Z-
dc.date.available2019-05-28T14:27:09Z-
dc.date.issued2018-
dc.identifier.citationCAMPOS, J. C. T.; MARTINS, A. X.; SOUZA, M. J. F. A hybrid VNS algorithm for solving the multi-level capacitated minimum spanning tree problem. Electronic notes in discrete mathematics, v. 66, p. 159-166, abr. 2018. Disponível em: <https://www.sciencedirect.com/science/article/pii/S1571065318300672>. Acesso em: 19 fev. 2019.pt_BR
dc.identifier.issn1571-0653-
dc.identifier.urihttp://www.repositorio.ufop.br/handle/123456789/11351-
dc.description.abstractThis work addresses the multi-level capacitated minimum spanning tree (MLCMST) problem. It consists of finding a minimal cost spanning tree such that the flow to be transferred from a central node (root) to the other nodes is bounded by the edge capacities. In this paper, a hybrid algorithm, combining the Variable Neighborhood Search (VNS) metaheuristic and one mathematical programming formulation of the literature, is used for solving it. The formulation is used to give an initial solution to VNS. Five neighborhoods are used for exploring the solution space. Results show that the VNS is able to improve the initial solutions and to obtain small gap solutions for all instance sets.pt_BR
dc.language.isoen_USpt_BR
dc.rightsrestritopt_BR
dc.subjectMulti-level capacitated minimum spanning treept_BR
dc.subjectVariable neighborhood searchpt_BR
dc.subjectCombinatorial optimizationpt_BR
dc.subjectNeighborhood structurespt_BR
dc.titleA hybrid VNS algorithm for solving the multi-level capacitated minimum spanning tree problem.pt_BR
dc.typeArtigo publicado em periodicopt_BR
dc.identifier.uri2https://www.sciencedirect.com/science/article/pii/S1571065318300672pt_BR
dc.identifier.doihttps://doi.org/10.1016/j.endm.2018.03.021pt_BR
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