Fuzzy Hypersoft Expert Set with Application in Decision Making for the Best Selection of Product

Abstract

Numerous researchers have made a few models dependent on soft set, to tackle issues in decision making and clinical analysis, yet a large portion of these models manage one expert. This causes an issue with the clients, particularly with the individuals who use polls in their work and studies. Accordingly we present another model i.e. fuzzy hypersoft expert set which not just addresses this constraint of fuzzy soft-like models by accentuating the assessment, all things considered, yet additionally settle the deficiency of soft set for disjoint attribute-valued sets comparing to distinct attributes. In this study, the existing concept of fuzzy soft expert set is generalized to fuzzy hypersoft expert set which is more flexible and useful. Some fundamental properties (i.e. subset, not set and equal set), results (i.e. commutative, associative, distributive and D Morgan Laws) and set-theoretic operations (i.e. complement, union, intersection AND, and OR) are discussed. An algorithm is proposed to solve decision-making problems and is applied to select the best product.