Displaying similar documents to “Some practical problems in fuzzy sets-based decision support systems.”

Fuzzy linear programming via simulated annealing

Rita Almeida Ribeiro, Fernando Moura Pires (1999)

Kybernetika

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This paper shows how the simulated annealing (SA) algorithm provides a simple tool for solving fuzzy optimization problems. Often, the issue is not so much how to fuzzify or remove the conceptual imprecision, but which tools enable simple solutions for these intrinsically uncertain problems. A well-known linear programming example is used to discuss the suitability of the SA algorithm for solving fuzzy optimization problems.

Fuzzy termination criteria in Knapsack Problem algorithms.

José Luis Verdegay, Edmundo Vergara-Moreno (2000)

Mathware and Soft Computing

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Fuzzy rule based termination criteria are introduced in two conventional and exact algorithms solving Knapsack Problems. As a consequence two new solution algorithms are obtained. These algorithms are heuristic ones with a high performance. The efficiency of the algorithms obtained is illustrated by solving some numerical examples.

Evolutionary algorithms and fuzzy sets for discovering temporal rules

Stephen G. Matthews, Mario A. Gongora, Adrian A. Hopgood (2013)

International Journal of Applied Mathematics and Computer Science

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A novel method is presented for mining fuzzy association rules that have a temporal pattern. Our proposed method contributes towards discovering temporal patterns that could otherwise be lost from defining the membership functions before the mining process. The novelty of this research lies in exploring the composition of fuzzy and temporal association rules, and using a multi-objective evolutionary algorithm combined with iterative rule learning to mine many rules. Temporal patterns...

Solving a possibilistic linear program through compromise programming.

Mariano Jiménez López, María Victoria Rodríguez Uría, María del Mar Arenas Parra, Amelia Bilbao Terol (2000)

Mathware and Soft Computing

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In this paper we propose a method to solve a linear programming problem involving fuzzy parameters whose possibility distributions are given by fuzzy numbers. To address the above problem we have used a preference relationship of fuzzy numbers that leads us to a solving method that produces the so-called α-degree feasible solutions. It must be pointed out that the final solution of the problem depends critically on this degree of feasibility, which is in conflict with the optimal value...