An application of soft computing technique in group decision making under interval-valued intuitionistic fuzzy environment

This paper investigates the group decision making problem, in which the each decision maker (DM) provides his/her preferences over alternatives with respect to attributes in interval-valued intuitionistic fuzzy number, and determines the weights of DMs.

Abstract

In this paper, we investigate the group decision making problem, in which the each decision maker (DM) provides his/her preferences over alternatives with respect to attributes in interval-valued intuitionistic fuzzy number. To determine the weights of DMs, inspired by the idea of TOPSIS technique, combining an optimistic coefficient, we first define a positive ideal decision as the average of all individual decisions and three negative ideal decisions, which have the maximum separations from the positive ideal decision. This method is suitable for cautious (avoiding risk) decision, since each negative ideal decision can effectively avoid a risk. By employing the derived weights of DMs, we aggregate all the individual decisions into a collective decision. After that, we aggregate all attribute values of each alternative of the collective decision into an overall evaluation of the alternative. Then rank all alternatives according to their score and accuracy degree and select the most desirable one. We compare this model with other methods and illustrate this method by a numerical example and a sensitivity analysis about the optimistic coefficient.