Schweizer-Sklar Aggregation Operators with Unknown Weight for Picture Fuzzy Information

Abstract

The Interval-Valued Picture fuzzy set (IV-PFS) possesses multiple advantages in various fields of research and other systematic approaches which provides an innovative method for solving multi-attribute decision-making (MADM) issues. The main purpose of this article is to define some power average aggregation operators (PA AOs) which are helpful in ameliorating some of the adverse consequences of falsified information, and the Schweizar-Sklar (SS) operational rules are used to introduce some novel AOs such as the IV-PFSSPA and IV-PFSSWPA operators. Furthermore, the distinctive characteristics of these provided AOs are discussed in detail. Moreover, we implement some of the suggested AOs to formulate a process for resolving MADM problems concerning IV-PF data. We provide an example to further explain the importance and usefulness of the tree selection scenario which is helpful to protect the environment and also enhance human lives. To demonstrate the distinctive nature and feasibility of the suggested strategy, an analysis of the comparability is carried out with the previously discussed approaches.