Dual Hesitant Fuzzy Soft Aggregation Operators and Their Application in Decision-Making

Soft set theory acts as a fundamental tool for handling the uncertainty in the data by adding a parameterization factor during the process as compared to fuzzy and intuitionistic fuzzy set theories. Under this environment, dual hesitant fuzzy soft set (DHFSS) is one of the most successful extensions of a fuzzy soft set in which preferences are represented in terms of a set of possible values than a single number. In this paper, some new aggregation operators, namely, dual hesitant fuzzy soft weighted averaging and geometric operators proposed along with their proofs to aggregate the different preferences of the decision-makers. Various desirable properties of its are also investigated in details. Further, these aggregation operators are extended to its generalized operator by incorporating the attitude character of the decision-maker towards the data. In this study, multicriteria decision-making approach is presented based on the proposed operators for solving the decision-making problems. An illustrative example present to demonstrate the approach under DHFSS environment and compared their results with some of the existing approaches results. The proposed measures illustrate with case studies along with the effect of the different parameters on the ordering of the objects which makes the proposed operators more flexible and offers the various choices to the decision-maker for assessing the decisions. From the study, it is concluded that the proposed approach provides a more practical nature to the decision-maker during the aggregation process and hence demonstrate that they place an alternative way for solving the decision-making problems.