A Ranking Method of Hexagonal Fuzzy Numbers Based on Their Possibilistic Mean Values

Abstract

A hexagonal fuzzy number (HFN) with its membership function as a nonlinear function, which is a generalization of triangular fuzzy numbers, trapezoidal fuzzy numbers, linear pentagonal fuzzy numbers and linear hexagonal fuzzy numbers, is defined in this paper. Cardinality of HFN is applied to achieve an algorithm for classifying types of HFNs. In addition, we present a ranking method for those fuzzy numbers based on their possibilistic mean values. Therefore, an explicit formula of the possibilistic mean value of HFN is proposed.