Novel AQM analysis approach based on similarity and dissimilarity measures of interval set for multi-expert multi-criterion decision making

Interval sets, as a particular knowledge representation, are composed of the lower set and the upper set to represent more general uncertain qualitative information. In this paper, we aim at investigating multi-expert multi-criterion decision making (MEMCDM) based on the interval set information. In the process of MEMCDM, by comparing positive, negative and boundary regions of interval sets, we firstly propose the similarity and dissimilarity measures of interval set to describe the closeness and deviation between interval sets. Secondly, with the aid of dissimilarity, we determine the criterion weight by combining the dissimilarity difference of the judgement matrix and the result of the maximum deviation of dissimilarity. For dealing with the hesitant problem of experts, we design transformation algorithm, which provides technical support for transforming experts' evaluation tables into an interval set information table. In view of the wide applicability of alternative queuing method (AQM), we extend AQM based on precedence relationship matrix to address MEMCDM. The precedence relationship matrix of AQM exists the incomparable situation, which can lead to the indiscernible alternatives. To reduce indiscernible alternatives and increase efficiency, we design a possibility degree of interval sets and deeply construct precedence relationship matrix. Further, we improve AQM by using possibility degree matrix and aggregating precedence relationship matrix on each alternative by weight arithmetic average (WAA). Finally, we use an example and simulation experiment of former e-commerce platform selection to elaborate and validate our proposed method.