Prioritized Muirhead Mean Aggregation Operators under the Complex Single-Valued Neutrosophic Settings and Their Application in Multi-Attribute Decision-Making

Abstract

Two critical tasks in multi-attribute decision making (MADM) are to describe criterion values and to aggregate the described information to generate a ranking of alternatives. A flexible and superior tool for the first task is complex single-valued neutrosophic (CSVN) setting and, a powerful device for the subsequent assignment is aggregation operator. Up until this point, almost thirty diverse aggregation operators of CSVN have been introduced. Every operator has its unmistakable qualities and can function admirably for explicit reason. Notwithstanding, there isn't yet an operator which can give helpful consensus and adaptability in conglomerating rule esteems, managing the heterogeneous interrelationships among models, and decreasing the impact of outrageous basis esteems. In genuine decision-making interaction, there are cases that the interrelationships of contentions don't exist in every one of the contentions, however in piece of the contentions. Subsequently, there is a need to parcel the contentions into various parts. For this, the technique of prioritized Muirhead mean (PMM) aggregation operator is massive dominant and more flexible is to investigate the interrelationships between any numbers of objects. The goal of this study is to initiate the CSVN setting and to determine their important algebraic laws. Moreover, to provide such an aggregation operator, the principle of CSVN PMM (CSVNPMM) operator and CSVN prioritized dual Muirhead mean (CSVNPDMM) operator is elaborated and discussed their particular cases. Further, based on these operators, we presented a new method to deal with the multi-attribute decision making problems under the fuzzy environment. Finally, we used some practical examples to illustrate the validity and superiority of the proposed method by comparing with other existing methods.

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