Application of an iterative method and an evolutionary algorithm in fuzzy optimization

• Autor(a) principal: Silva, Ricardo Coelho
• Data de Publicação: 2012
• Outros Autores: Cantão, Luiza A.P. [UNESP], Yamakami, Akebo
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Institucional da UNESP
• Texto Completo: http://dx.doi.org/10.1590/S0101-74382012005000018; http://hdl.handle.net/11449/73304
• topic: Cut levels; Fuzzy numbers; Fuzzy optimization; Genetic algorithms

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. © 2012 Brazilian Operations Research Society.