Hesitant Fuzzy Generalised Bonferroni Mean Operators Based on Archimedean Copula for Multiple-Attribute Decision-Making

Abstract EN

Information fusion is an important part of multiple-attribute decision-making, and aggregation operator is an important tool of decision information fusion.

Integration operators in a variety of fuzzy information environments have a slight lack of consideration for the correlation between variables.

Archimedean copula provides information fusion patterns that rely on the intrinsic relevance of information.

This paper extends the Archimedean copula to the aggregation of hesitant fuzzy information.

Firstly, the Archimedean copula is used to generate the operation rules of the hesitant fuzzy elements.

Secondly, the hesitant fuzzy copula Bonferroni mean operator and hesitant fuzzy weighted copula Bonferroni mean operator are propounded, and several properties are proved in detail.

Furthermore, a decision-making method based on the operators is proposed, and the specific decision steps are given.

Finally, an example is presented to illustrate the practical advantages of the method, and the sensitivity analysis of the decision results with the change of parameters is carried out.