dc.creatorCiappina J.R.
dc.creatorYamakami A.
dc.creatorSilva R.C.
dc.date2012
dc.date2015-06-26T20:29:40Z
dc.date2015-11-26T14:26:08Z
dc.date2015-06-26T20:29:40Z
dc.date2015-11-26T14:26:08Z
dc.date.accessioned2018-03-28T21:29:05Z
dc.date.available2018-03-28T21:29:05Z
dc.identifier
dc.identifierComputers And Operations Research. , v. 39, n. 12, p. 3394 - 3407, 2012.
dc.identifier3050548
dc.identifier10.1016/j.cor.2012.04.023
dc.identifierhttp://www.scopus.com/inward/record.url?eid=2-s2.0-84863008415&partnerID=40&md5=049f8b481b765d766772a6993ad18e1a
dc.identifierhttp://www.repositorio.unicamp.br/handle/REPOSIP/97117
dc.identifierhttp://repositorio.unicamp.br/jspui/handle/REPOSIP/97117
dc.identifier2-s2.0-84863008415
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/1245991
dc.descriptionWe present, in this paper, a method for solving linear programming problems with fuzzy costs based on the classical method of decompositions Dantzig-Wolfe. Methods using decomposition techniques address problems that have a special structure in the set of constraints. An example of such a problem that has this structure is the fuzzy multicommodity flow problem. This problem can be modeled by a graph whose nodes represent points of supply, demand and passage of commodities, which travel on the arcs of the network. The objective is to determine the flow of each commodity on the arcs, in order to meet demand at minimal cost while respecting the capacity constraints of the arcs and the flow conservation constraints of the nodes. Using the theory of fuzzy sets, the proposed method aims to find the optimal solution, working with the problem in the fuzzy form during the resolution procedure. © 2012 Elsevier Ltd. All rights reserved.
dc.description39
dc.description12
dc.description3394
dc.description3407
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dc.languageen
dc.publisher
dc.relationComputers and Operations Research
dc.rightsfechado
dc.sourceScopus
dc.titleDecompositions Dantzig-wolfe Applied To Fuzzy Multicommodity Flow Problems
dc.typeArtículos de revistas


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