A Fuzzy Stochastic Multi-Attribute Group Decision-Making Approach for Selection Problems

The purpose of this paper is to demonstrate how the fuzzy and stochastic approaches as well as multi-attribute group decision-making techniques can be effectively utilized for selection problems in different kinds of uncertainty situations. A group of the decision makers describes a value for an alternative with respect to an attribute by the use of linguistic variables. The rating of each alternative is aggregated, which can be expressed as triangular fuzzy numbers. Then Monte Carlo simulation is applied to generate the performance distribution of alternatives with respect to different attributes in an uncertain environment. Finally, a stochastic compromise ranking technique, known as the VIKOR technique, is developed to evaluate probability distributions for each alternative on each attribute and to prioritize them. The proposed fuzzy stochastic approach is suitable to compromise a solution among the conflicting tangible and intangible attributes under the group decision-making process. In addition, an application of the proposed approach is provided to solve the important risk selection problem in a highway project. The results illustrate its real-life applicability and suitability in discrete group decision-making under uncertainty.