Multicriteria Decision-Making Approach Based on Atanassov's Intuitionistic Fuzzy Sets With Incomplete Certain Information on Weights

To handle multicriteria fuzzy decision-making problems, a new multicriteria decision-making method is proposed in which the information about criteria´s weights is not completely certain, and the criteria values of alternatives are Atanassov´s intuitionistic fuzzy sets (A-IFSs). Using evidential reasoning algorithms, the criteria values are aggregated; receiving the overall A-IFS for alternatives and the distances between each alternative and the ideal, as well as anti-ideal alternative, are computed. Combining the incomplete certain information of weights, a nonlinear programming model is developed and resolved by particle swarm optimization algorithms to obtain the optimal criteria´s weights. The corresponding decision-making procedure is given in detail. Finally, two examples are given to show the feasibility and availability of the proposed method.