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An interactive fuzzy satisficing method for random fuzzy multiobjective linear programming problems through fractile criteria optimization with possibility
Author(s) -
Masatoshi Sakawa,
Takeshi Matsi,
Hideki Katagiri
Publication year - 2013
Publication title -
artificial intelligence research
Language(s) - English
Resource type - Journals
eISSN - 1927-6982
pISSN - 1927-6974
DOI - 10.5430/air.v2n4p75
Subject(s) - satisficing , mathematical optimization , goal programming , fuzzy logic , linear programming , pareto principle , preference , mathematics , computer science , artificial intelligence , statistics
This paper considers multiobjective linear programming problems where each coefficient of the objective functions isexpressed by a random fuzzy variable. A new decision making model is proposed by incorporating the concept of fractilecriteria optimization into a possibilistic programming model. An interactive fuzzy satisficing method is presented forderiving a satisficing solution for a decision maker efficiently by updating the reference membership levels. In theproposed method, it is shown that the transformed deterministic problems for obtaining Pareto optimal solutions can besolved by using some convex programming techniques. An illustrative numerical example is provided to clarify theproposed method.

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