A variable weight-based hybrid approach for multi-attribute group decision making under interval-valued intuitionistic fuzzy sets

This article aims to develop a novel hybrid multi-attribute group decision-making approach under interval-valued intuitionistic fuzzy sets (IVIFS) by integrating variable weight, correlation coefficient, and technique for order performance by similarity to an ideal solution (TOPSIS). First, experts give their evaluation in IVIFS, and then the weighting evaluation matrix is computed based on interval-valued intuitionistic fuzzy weighted averaging operator with the subjective attribute weights given in advance. Second, a simple and useful weighting approach on the basis of correlation coefficient is put forward to obtain the experts weights. Third, we treat the attribute weights as a varying vector, and then propose a variable weighting approach for its acquisition. Fourth, an individual decision can be converted to an alternative decision by considering the experts and attributes weights together. At last, the integrated assessment value of each alternative is computed by TOPSIS, and then the most appropriate alternative is chosen. Two illustrative examples dealt with the problem by the method presented in this article demonstrate the usefulness of this approach, compared with those by the other methods.

Automated summary

This article presents a novel hybrid multi-attribute group decision-making approach under IVIFS, incorporating variable weight, correlation coefficient, and TOPSIS. It computes the weighting evaluation matrix based on experts' evaluation in IVIFS and proposes a weighting approach based on correlation coefficient. The method treats attribute weights as a varying vector and uses a variable weighting approach for their acquisition. Ultimately, the integrated assessment value of each alternative is calculated by TOPSIS to determine the most appropriate alternative.