A multi attribute decision making framework based on partitioned dual Maclaurin symmetric mean operators under Fermatean fuzzy environment

In information aggregation, the Maclaurin symmetric mean (MSM) operator has drawn a lot of interest to the researchers. And, partitioned dual MSM (PDMSM) has a precondition that all attributes are grouped into several partitions and the attributes in the same partition are relevant to other attributes in the same group, while the attributes located in different groups have no relation. The Fermatean fuzzy set (FFS), on the other hand, is a potent mathematical model that effectively manages uncertain data. The existing FFS-based multi attribute decision making (MADM) techniques fail to evaluate the partitions of the relative attributes, the interdependencies between various criteria, and the ability to mitigate the detrimental impacts of irrelevant criteria. Motivated by these issues, this paper proposes novel operators named FFPDMSM and weighted FFPDMSM to handle the scenarios where criteria are divided into distinct parts and there are interconnections among multiple criteria within the same part. The proposed operators deal not only with interrelationships between criteria but also with partitioned relationships among criteria. Some properties of the proposed operators are discussed in detail. Further, an MADM approach is developed based on the proposed operators in the FF environment. A realistic numerical illustration with sensitivity analysis is demonstrated to validate the proposed approach. Finally, the method is compared with different existing techniques to demonstrate the proposed method’s applicability and feasibility.