Interval-Valued Intuitionistic Fuzzy Generalised Bonferroni Mean Operators for Multi-attribute Decision Making

Interval-valued intuitionistic fuzzy set (IVIFS) is a potent tool to capture the vagueness and uncertainty of information. Archimedean copula and co-copula (ACC) are crucial implement utilized to generate operational rules of interval-valued intuitionistic fuzzy numbers (IVIFNs). Meanwhile, Bonferroni mean (BM) has a distinct merit that it can take into consideration the interconnection of diverse attributes. In the paper, generalized BM operators based on ACC are utilized to aggregate IVIFNs. Firstly, several novel operational rules based on ACC are proposed. Secondly, the interval-valued intuitionistic fuzzy copula BM (IVIFCBM) operator and the interval-valued intuitionistic fuzzy weighted copula BM (IVIFWCBM) operator are propounded. Moreover, several desired properties and particular instances of these operators are investigated in detail. Thirdly, a new methodology based upon the IVIFWCBM operator is presented to tackle multi-attribute decision making (MADM) problems under interval-valued intuitionistic fuzzy context. Ultimately, the efficacy of the proposed approach is substantiated through an empirical example and a comparison is implemented to display the predominance of the approach.