We present, in this paper, a method for solving linear programming problems with fuzzy costs based on the classical method of decomposition's 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.
ResumoEste trabalho demonstra de forma analítica e numérica a relação entre dois métodos, Trappey et al. (1988) e Xu (1989), da literatura que resolvem problemas de programação não-linear com incertezas no conjunto de restrições. Uma análise comparativa entre o desempenho para a obtenção da solução ótima dos métodos de otimização não-linear clássicos e dos métodos de otimização não-linear nebulosos, apresentados também neste trabalho. Para tal comparação, serão apresentados dois problemas que foram modelados em termos de programação não-linear clássico, os quais permitem a introdução de incertezas nas restrições. Com base na análise dos problemas propostos em Xu (1989), verificou-se que os dois métodos descritos fornecem resultados similares, conforme algumas condições.Palavras-chave: conjuntos nebulosos; otimização não-linear; programação matemática nebulosa.
AbstractIn this work, we demonstrate analytical and numerically the relation between two methods, Trappey et al. (1988) and Xu (1989), found in the literature. These methods were developed to solve nonlinear programming problems with uncertainties in the set of constraints. A comparative analysis of the performance between classic and fuzzy nonlinear optimization methods are presented too in the work. For the comparison, two problems are modeled that had been shaped in terms of classic nonlinear programming, which allow the introduction of uncertainties in its formularizations. Based on the analysis of the problem proposed in Xu (1989), we verified that the two described methods provide similar results, as per some conditions.
ABSTRACT. This work develops two approaches based on the fuzzy set theory to solve a class of fuzzy mathematical optimization problems with uncertainties in the objective function and in the set of constraints. The first approach is an adaptation of an iterative method that obtains cut levels and later maximizes the membership function of fuzzy decision making using the bound search method. The second one is a metaheuristic approach that adapts a standard genetic algorithm to use fuzzy numbers. Both approaches use a decision criterion called satisfaction level that reaches the best solution in the uncertain environment. Selected examples from the literature are presented to compare and to validate the efficiency of the methods addressed, emphasizing the fuzzy optimization problem in some import-export companies in the south of Spain.
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