Academic Journals Database
Disseminating quality controlled scientific knowledge

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

Author(s): Ricardo Coelho Silva | Luiza A.P. Cantão | Akebo Yamakami

Journal: Pesquisa Operacional
ISSN 0101-7438

Volume: 32;
Issue: 2;
Start page: 315;
Date: 2012;
Original page

Keywords: fuzzy numbers | cut levels | 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.
RPA Switzerland

Robotic Process Automation Switzerland


Tango Jona
Tangokurs Rapperswil-Jona