Group Decision Making Based on Generalized Intuitionistic Fuzzy Yager Weighted Heronian Mean Aggregation Operator

Intuitionistic fuzzy (IF) sets are valuable tools for describing uncertain information in Multi-Criteria Group Decision Making (MCGDM), where the elements have degrees of membership and non-membership. IF aggregation operator is a popular data processing method that can be used for data dimensionality reduction, feature extraction, data compression, and so on. Some existing MCGDM techniques based on IF aggregation operators have been criticized for reasons that include disregarding the comprehensive correlations of the criteria and ignoring the monotonicity of the decision information. This paper aims to construct some IF aggregation operators based on Yager's triangular norms and Heronian mean to shed light on decision-making issues. At first, some novel IF operations such as Yager sum, Yager product, and Yager scalar multiplication on IFSs are presented. Based on these new operations, the generalized IF Yager Heronian average (GIFYHA) operator and the generalized IF Yager weighted Heronian average (GIFYWHA) operator are proposed and their corresponding properties are also proved in detail. Then, an improved MCGDM algorithm is constructed that relies on suggested operators. Its effectiveness and applicability are verified by applying it to select the best location for a company. In addition, the sensitivity of the parameters in the proposed operator to decision findings is also discussed. Finally, the comparative analysis of the proposed operator with the existing operators shows that the proposed operator is suitable for aggregating IF information with correlations both on "non-empty lattice" and total orders on IF values.