This paper presents an interactive fuzzy satisfying method by assuming that the decision maker (DM) has fuzzy goals for each of the objective functions in multiobjective nonlinear programming problems. The fuzzy goals of the DM are quantified by eliciting the corresponding membership functions through the interaction with the DM. After determining the membership functions for each of the objective functions, in order to generate a candidate for the satisficing solution which is also a Pareto optimal, the DM selects an appropriate standing membership function and specifies his/her aspiration levels of achievement of the other membership functions, called constraint membership values. For the DM's constraint membership values, the corresponding constraint problem is solved and the DM is supplied with the Pareto optima] solution together with the trade‐off rates between a standing membership function and each of the other membership functions. Then by considering the current values of the membership functions as well as the trade‐off rates, the DM acts on this solution by updating his/her constraint membership values. In this way, the satisficing solution for the DM can be derived efficiently from among a Pareto optimal solution set by updating his/her constraint membership values. On the basis of the proposed method, a time‐sharing computer program is written and an application to regional planning is demonstrated along with the corresponding computer outputs.
SAKAWA, M. and YANO, H. (1986), "AN INTERACTIVE FUZZY SATISFICING METHOD USING CONSTRAINT PROBLEMS AND ITS APPLICATION TO REGIONAL PLANNING", Kybernetes, Vol. 15 No. 2, pp. 121-129. https://doi.org/10.1108/eb005737Download as .RIS
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