Fuzzy random multi-attribute group decision-making method based on corrected deviation entropy and dual-objective optimization weighting

This paper proposes a novel fuzzy random multi-attribute group decision-making (MAGDM) method in a fuzzy random environment based on corrected deviation entropy and a dual-objective optimization weighting of the generalized improved weighted similarity (GIWS) coefficient. Firstly, to address the ambiguous interpretability and asymmetry problems of the existing weighted similarity (WS) coefficient, this paper introduces GIWS coefficient. Based on this, considering the respective advantages of subjective and objective weights, a dual-objective optimization weighting model based on the GIWS coefficient is proposed to determine the combination attribute weights. Secondly, to solve the problem that the deviation entropy model ignores the order relations of attributes between individual and group evaluations, a corrected deviation entropy model is adopted to determine the expert weights. Thirdly, the comprehensive evaluations are aggregated using the fuzzy random weighted average operator, and schemes are ranked by comparing their fuzzy random coefficients of variation. Finally, this paper verifies the rationality of the proposed method in fuzzy random environment through the analysis of project selection examples.