Intuitionistic Fuzzy Hamacher Aggregation Operators with Entropy Weight and Their Applications to Multi-criteria Decision-Making Problems

The theme of this work is to present some new operational laws for intuitionistic fuzzy numbers and their averaging and geometric aggregation operators under the completely unknown attribute weights. To accomplish this, firstly the shortcoming of the existing operations has been highlighted, and then, they have been mitigated by defining intuitionistic fuzzy Hamacher interaction weighted averaging and geometric aggregation operators by considering the pairs of membership functions. Some of the desirable properties of the proposed operators are stated. The attribute weight vector used for aggregating the decision maker's preferences has been computed by using the entropy function. Finally, a decision-making approach has been presented and illustrated with a numerical example to demonstrate the superiority of the approach over the existing operators.