A fractile optimisation approach for possibilistic programming problem in fuzzy random environment Online publication date: Sat, 12-Jul-2014
by Nureize Arbaiy; Junzo Watada
International Journal of Artificial Intelligence and Soft Computing (IJAISC), Vol. 3, No. 4, 2013
Abstract: Real-life applications face simultaneously hybrid uncertainty namely fuzziness and randomness, or ambiguous and vague information that makes the existing decision-making model incapable of handling such uncertainties. This paper presents the possibilistic programming for decision-making using a fractile approach. Some real world problems are formulated as a necessity measure model to deal with the uncertainties, which come from vague aspiration and ambiguous coefficients. Thus, the proposed methodology is important in building the model and finding the solution. The vagueness and ambiguity are properly treated in the paper and the fractile approach is used to solve fuzzy linear programming problem. An illustrative example explains the proposed model. The analytical results of the proposed method reveal the improvement of conventional decision-making approaches to appropriately handle inherent uncertainties contained in the real world situation.
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