Abstract
A common problem confronting a designer is selecting the best alternative from a set of feasible alternatives. In many situations this is not a difficult task as the best course of action is well defined, namely the alternative that minimizes (or maximizes) a well defined scalar-valued objective function. But in many decision making scenarios, no single objecfive function can adequately serve to compare the difference in desirability among feasible solutions. The theory of multiple objective optimization (MOO) deals with such problems and first arose in mathematical economics. In the framework of mathematical programming, the MOO problem was first mentioned by Kuhn and Tucker (1951) and in engineering literature by Zadeh (1963). For a comprehensive review of various aspects of the MOO problem, see Stadler (1984), Evans (1984), Stadler and Dauer (1992), and Koski (1993).KeywordsMembership FunctionFuzzy OptimizationSingle Objective Optimization ProblemFuzzy ConstraintMultiple Objective OptimizationThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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