A 2-tuple fuzzy linguistic representation model for computing with words
IEEE Transactions on Fuzzy Systems
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For large-group multi-criteria decision-making problems with linguistic assessment information, a new method is proposed, in which the criteria's weights are incomplete certain, and the decision-makers' preference information may takes the form of linguistic grade, or may be between two standard linguistic grade, or may be not given. By using the evidential reasoning algorithm and two-tuple, the criteria values of alternative for each decision-maker are integrated. The nonlinear programming model is constructed based on the incomplete certain information on the criteria weights, and distances between alternatives and ideal alternative. And the optimal weights on criteria are gained by using parallel genetic algorithms to solve the nonlinear programming model. The integrating values of alternatives for each decision-maker are computed. By using group coherence, the fuzzy clustering model is constructed. After solving the fuzzy clustering model, the weights of decision-makers are attained. And the ranking of the alternatives is obtained. Finally an example is also given to show the feasibility and availability of this method.