Prioritized Muirhead Mean Aggregation Operators under the Complex Single-Valued Neutrosophic Settings and Their Application in Multi-Attribute Decision-Making
Keywords:complex single-valued neutrosophic sets, prioritized Muirhead mean operators, decision-making techniques
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 30 diverse aggregation operators of CSVN have been introduced. Every operator has its unmistakable qualities and can function admirably for explicit reasons. Notwithstanding, there is not yet an operator that 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 do not exist in each 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 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 their particular cases are discussed. Further, based on these operators, we presented a new method to deal with the MADM 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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