Hypersoft Expert Set With Application in Decision Making for Recruitment Process

Abstract

Many researchers have created some models based on soft set, to solve problems in decision making
and medical diagnosis, but most of these models deal only with one expert. This causes a problem with the
users, especially with those who use questionnaires in their work and studies. Therefore we present a new
model i.e. Hypersoft Expert Set which not only addresses this limitation of soft-like models by emphasizing the
opinion of all experts but also resolves the inadequacy of soft set for disjoint attribute-valued sets corresponding
to distinct attributes. In this study, the existing concept of soft expert set is generalized to 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 applied to recruitment process for hiring ”right person for the right job”.