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

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 ([Formula: see text] S) 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 [Formula: see text] S and elaborate the [Formula: see text] S associated with threshold. The algebraic and Yager operations on [Formula: see text] numbers are also defined. Several algorithms are proposed to demonstrate the applicability of [Formula: see text] S 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 ([Formula: see text] G) and the Fermatean fuzzy Yager weighted average ([Formula: see text] A) operators. Further, we developed a remarkable [Formula: see text] -TOPSIS approach by applying innovative [Formula: see text] 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 [Formula: see text] S and [Formula: see text] -TOPSIS approach by highlighting its advantages over the existent models and TOPSIS approaches. Finally, we holistically describe the conclusion of the whole work.