An Enhanced Intuitionistic Fuzzy TOPSIS approach Based on Yager’s Aggregation Operators and the Application in Selecting Green Suppliers

Abstract

The theory of intuitionistic fuzzy sets (IFS) is ideally suited to handle uncertainty and haziness. In this study, an enhanced fuzzy TOPSIS-based method for dealing with multi-attribute group decision making (MAGDM) problems under intuitionistic fuzzy information, where the weights of the decision-makers (DMs) and criteria are completely unknown. Firstly, the Yager operational rules are initiated for intuitionistic fuzzy numbers (IFNs) constructed on Yager T-norm (TN) and T-conorm and various core properties of these operational rules are investigated. Secondly, utilizing these operational various weighted aggregation operators such as, intuitionistic fuzzy Yager weighted averaging (IFYWA) operator, intuitionistic fuzzy Yager weighted ordered weighted averaging (IFYOWA) operator, intuitionistic fuzzy Yager weighted hybrid averaging (IFYWHA) operator, intuitionistic fuzzy Yager weighted geometric (IFYWG) operator, intuitionistic fuzzy Yager ordered weighted geometric (IFOWG) operator, intuitionistic fuzzy Yager hybrid weighted geometric (IFYHWG) operator are initiated. Thirdly, a few characteristics of the intended aggregation operators are investigated. Fourthly, a novel MAGDM model is constructed based on the intended aggregation operators to handle IF information. Finally, a numerical example related to the selection of suitable green suppliers is provided to show the efficacy and practicality of the initiated MAGDM approach, along with the comparison to some existing MAGDM approaches.