An almost stochastic dominance based method for stochastic multiple attributes decision making

This paper proposes an almost stochastic dominance based method for solving the stochastic multiple attribute decision making (SMADM) problems, where the attribute values of alternatives are represented by interval number with probability distribution (INPD), a random variable with closed interval support. Firstly, we present the basic operations, set operations and weighted operators of INPD to accomplish the normalization, weight and aggregation of decision information. Then, based on the concept of almost stochastic dominance, we propose a new stochastic dominance degree (SDD) to quantify the uncertain relation between INPD. The identification of ideal solution and the measurement of separation are accomplished with SDD. On this basis, an extended TOPSIS method is proposed, including normalization method, identification of ideal solution, calculation of separation and decision making procedure. Finally, the numerical examples show that the new method is effective even under extremely uncertain environment, and it can produce more accurate result. © 2017 IOS Press and the authors. All rights reserved.