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This paper provides an introduction about multiobjective optimization concepts in uncertain environment with the aid of the Fuzzy Mathematical Programming paradigm. The goal is to present methodologies based on Fuzzy Mathematical Programming to solve Multiobjective Programming problems, which model optimization problems with several objective functions and, in many cases, conflicting ones. The article also presents an extensive survey of the classic bibliography in this area, such as the Evolutionary Algorithms for Multi-Objective Optimization (MOEAs), which presents a growing development of algorithms and applications in practical problems since the beginning of this century. Besides, one of the steps in modelling practical problems is to deal with the uncertainty in the real-world data and here we show some proposals that use the Fuzzy Sets Theory for this treatment. |
