Abstract
Modeling to generate alternatives (MGA) has been proposed as a framework for dealing with complex problems for which there are important unmodeled issues. MGA techniques are designed to provide the analyst (or decision maker) with a set of alternatives that are good with respect to modeled objectives and different from each other. Some of these alternatives may be better than others with respect to the unmodeled issues. Furthermore, by examining a set of different alternatives the analyst may gain insight and understanding. The concept of fuzziness is demonstrated here to be applicable to the MGA framework. The fuzzy approach can increase the flexibility of targets on modeled objectives as well as the flexibility of the original constraints of the model. Illustrations are provided using a linear programming model of a land use planning problem and a mixed integer programming model of a regional wastewater treatment system planning problem.
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