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An interactive fuzzy satisficing method for multiobjective nonlinear programming problems with fuzzy parameters
Author(s) -
Sakawa Masatoshi,
Yano Hitoshi
Publication year - 1987
Publication title -
electronics and communications in japan (part i: communications)
Language(s) - English
Resource type - Journals
eISSN - 1520-6424
pISSN - 8756-6621
DOI - 10.1002/ecja.4410700204
Subject(s) - satisficing , mathematical optimization , fuzzy logic , mathematics , membership function , fuzzy number , fuzzy set , pareto principle , defuzzification , goal programming , minimax , nonlinear programming , fuzzy set operations , fuzzy classification , nonlinear system , computer science , artificial intelligence , physics , quantum mechanics
The fuzziness induced in the decision making in the actual system in general, comprises fuzziness in the formulation of the problem and that in the judgments of the decision maker (DM) as a human being. This paper proposes an interactive fuzzy satisficing method, which takes into consideration the fuzziness in problem formulation as well as that in the judgments of DM. The multiobjective nonlinear programming problem containing fuzzy parameters is transformed into the α ‐multiobjective nonlinear programming problem. After determining the fuzzy goal of DM for each objective function by eliciting the membership function, the following reference values are set: the fuzziness index α of DM for the fuzzy parameter; and the reference membership values, which are reference for the membership functions. For the specified α and the reference membership values, the corresponding augmented minimax problem is solved, and DM is supplied with the α‐Pareto optimal, together with the trade‐off ratios among membership functions and the trade‐off ratios between α and the membership functions. Obtaining the result, the DM takes the information concerning current α‐Pareto optimal solution and the trade‐off ratios into consideration, and respond by updating the reference membership values and the value of α if necessary. In this way the satisficing solution finally is derived for DM, thereby ensuring the α‐Pareto optimality. This is the proposed interactive fuzzy satisficing method. The interactive process is demonstrated for a numerical example by the interactive computer program.

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