Affiliations: School of Computing and Information Engineering, Henan University of Economics and Law, Zhengzhou, Henan, China
Correspondence:
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Corresponding author. YanLi Cao, School of Computing and Information Engineering, Henan University of Economics and Law, Zhengzhou 450046, Henan, China. E-mail: [email protected].
Abstract: In order to solve the problems of weight solving and information aggregation in the Vague multi-attribute group decision-making, this paper first solves the weight of Vague evaluation value, and then fuses the information of Vague sets through evidence theory, and obtains an information aggregation algorithm for Vague multi-attribute group decision-making. Firstly, The algorithm draws on the idea of solving the weight of evidence in the improved evidence theory algorithm, and calculates the weight of Vague evaluation value, and revises the original evaluation information after obtaining the weight of each Vague evaluation value. Secondly, this algorithm analyzes the mathematical relationship between the Vague sets and the evidence theory, and uses the evidence theory to fuse the evaluation information to obtain the final Vague evaluation value of each alternative. Finally, this algorithm uses a score function to calculate the score of each alternative to determine the best alternative. The algorithm given in the paper enables decision-makers to make rational decisions in uncertain environments, and then select the best alternative.
Keywords: Vague sets, evidence theory, multi-attribute group decision-making, score function
