Complex fermatean fuzzy N-soft sets: a new hybrid model with applications

Abstract

Decision-making methods play an important role in the real-life of human beings and consist of choosing the best options from a set of possible choices. This paper proposes the notion of complex Fermatean fuzzy N-soft set (hbox {CFFNS}_fS) which, by means of ranking parameters, is capable of handling two-dimensional information related to the degree of satisfaction and dissatisfaction implicit in the nature of human decisions. We define the fundamental set-theoretic operations of hbox {CFFNS}_fS and elaborate the hbox {CFFS}_fS associated with threshold. The algebraic and Yager operations on hbox {CFFNS}_f numbers are also defined. Several algorithms are proposed to demonstrate the applicability of hbox {CFFNS}_fS to multi-attribute decision making. The advanced algorithms are described and accomplished by several numerical examples. Then, a comparative study manifests the validity, feasibility, and reliability of the proposed model. This method is compared with the Fermatean fuzzy Yager weighted geometric (hbox {FFY}_wG) and the Fermatean fuzzy Yager weighted average (hbox {FFY}_wA) operators. Further, we developed a remarkable hbox {CFFNS}_f-TOPSIS approach by applying innovative hbox {CFFNS}_f weighted average operator and distance measure. The presented technique is fantastically designed for the classification of the most favorable alternative by examining the closeness of all available choices from particular ideal solutions. Afterward, we demonstrate the amenability of the initiated approach by analyzing its tremendous potential to select the best city in the USA for farming. An integrated comparative analysis with existing Fermatean fuzzy TOPSIS technique is rendered to certify the terrific capability of the established approach. Further, we decisively investigate the rationality and reliability of the presented hbox {CFFNS}_fS and hbox {CFFNS}_f-TOPSIS approach by highlighting its advantages over the existent models and TOPSIS approaches. Finally, we holistically describe the conclusion of the whole work.