Stochastic EDAS method for multi-criteria decision-making with normally distributed data

Abstract

Discrete stochastic multi-criteria decision-making (MCDM) can be used to handle many real-life decision-making problems. The Evaluation Based on Distance from Average Solution (EDAS) is a new and efficient MCDM method. The desirability of alternatives in this method is determined based on distances of them from an average solution. Because the average solution is determined by an arithmetic mean in this method, the EDAS method can be efficient for solving stochastic problems. In this paper, a stochastic EDAS method is proposed to handle problems in which the performance values of alternatives on each criterion follow the normal distribution. Based on the proposed method, we can obtain optimistic and pessimistic appraisal scores for evaluation of alternatives and consider the uncertainty of decision-making data. We present a graphical example to illustrate the proposed method and a practical example of performance evaluation of bank branches to show the applicability of it. According to the analyses made, the proposed method is efficient and the results are valid.