Abstract
The aggregation of fuzzy information is a new area of "intuitionistic fuzzy set (IFS)" theory that has piqued the attention of scholars in the past few years. This study aims to construct intuitionistic fuzzy (IF) aggregation operators as a result of Aczel-Alsina (AA) operations in order to shed light on decision-making issues. To begin with, some novel IFS operations are provided, such as AA sum, AA product, and AA scalar multiplication. Based on these new operations, a number of novel aggregation operators for IF information has been suggested. These operators include the IF AA weighted geometric operator, the IF AA ordered weighted geometric operator, and the IF AA hybrid geometric operator. Different properties of these operators are established. Concerns about IF "multi-attribute decision-making (MADM)"has led to the creation of a new strategy that depends on such operators. Two case studies are examined to demonstrate the applicability and efficacy of the developed method in real-world applications. The first case study focuses on the selection of the most influential worldwide supplier for one of the most critical components used in the assembly process of a manufacturing company. The second case study focuses on selecting the most effective method for health-care waste (HCW) disposal. The suggested aggregation operators have a single variable parameter that has an impact on the decision-making method. The sensitivity of the proposed aggregation operators to decision-making findings is investigated. A sensitivity analysis of the criteria weights is presented to ensure the permanence of the introduced framework. In addition, a detailed comparison of existing models is made to show how the new framework is better than them. Finally, the findings show that the new strategy is more effective and reliable than the existing models.