Maclaurin Symmetric Mean Aggregation Operators Based on Novel Frank T-Norm and T-Conorm for Picture Fuzzy Multiple-Attribute Group Decision-Making

Abstract

The proposed study introduces innovative Maclaurin symmetrical mean aggregation operators (MSMAO) in picture fuzzy multiple-attribute group decision-making. These operators are built upon a newly developed Frank t-norm (FTN) and t-conorm (TCN). Utilizing these novel operators aims to enhance the effectiveness of decision-making processes in scenarios characterized by picture-fuzzy information. The study explores these aggregation operators' potential applications and advantages within the context of multiple-attribute group decision-making under uncertainty. As a novel method to handle MAGDM difficulties, we build these aggregation operators (AOs) on PFs in this paper by utilizing the FTN and TCN under PFs information. In addition, PF values (PFVs) and their fundamental operations are created and shown. Picture fuzzy Frank weighted Maclaurin symmetrical mean (MFFWMSM) and Picture fuzzy Frank Maclaurin symmetrical mean (MFFMSM) operators are two AOs introduced and studied based on these operations. The induction approach theoretically and quantitatively verifies the newly created AOs' accuracy and dependability. The problem of project evaluation utilizing these proposed operators is thoroughly examined to provide further applications and investigate the sensitivity of these PS Frank (PFF) operators. The outcomes of employing these PFF operators are contrasted with a few AOs of PFVs that were previously in existence. The suggested strategy is dependable and effective based on comparison outcomes.