An intelligent decision-making methodology based on a new Fermatean fuzzy similarity measure: a case study of building site preference

Abstract

Intelligent-driven decision-making through soft computing tools like Fermatean fuzzy sets has proven to be effective even in the presence of imprecision. The Fermatean fuzzy similarity measure is one of the avenues through which intelligent-based decision-making is made. Various methods of Fermatean fuzzy similarity measure have been fashioned; however, many of them are deficient in terms of formulation, precision, and compliance with the similarity conditions. These deficiencies can result in faulty decision-making with a concomitant effect during implementation. To this end, this paper presents a new approach of Fermatean fuzzy similarity measure, which outperformed the extant measures by comparison. Equally, the new approach evidently satisfies the similarity's conditions. To test the application potential of the new similarity approach, it is used to discuss the complexity of locating a prefer location for an estate building based on TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution). In addition, a comparative analysis of the new approach in conjunction with the extant ones based on TOPSIS is presented, and the new approach unmistakably shows precision and reliability over the extant approaches. The flexibility of the new approach of intelligent-based decision-making qualifies its application in other complex decision-making scenarios.