Affiliations: [a] School of Business Administration, Zhongnan University of Economics and Law, Wuhan, China
| [b] Department of Applied Mathematics and Statistics, Stony Brook University, New York, USA
Correspondence:
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Corresponding author. Shengbao Yao, School of Business Administration, Zhongnan University of Economics and Law, Wuhan 430073, China. Tel.: +86 2788386757; E-mail: [email protected].
Abstract: In multi-attribute group decision making (MAGDM) problems, the information about attribute weights and the performance ratings of alternatives usually cannot be accurately quantified. This issue has motivated the development of various MAGDM models based on the fuzzy sets theory. However, these fuzzy MAGDM models mostly rely on using the extreme or expected values, but ignore the intermediate occurrences in determining the best alternatives. In order to provide a complete understanding of decision makers’ preference structure, this paper takes a stochastic perspective and proposes a simulation-based approach to facilitate MAGDM under uncertainty when both quantitative and qualitative attributes are involved. The approach not only accounts for the incomplete information about the attribute weights during decision making, but also allows for the use of comparative linguistic expressions to better capture the decision makers’ hesitancy about linguistic expressions. We apply the proposed approach to electric vehicle charging station site selection problem and highlight its effectiveness and advantages through an in-depth comparative analysis with some of the existing methods.
Keywords: Multi-attribute group decision making, comparative linguistic expression, incomplete weights, random preference, Monte Carlo simulation
