A PROBABILISTIC APPROACH TO RANK COMPLEX FUZZY NUMBERS

Techniques for ranking simple fuzzy numbers are abundant in the litera ture. However, we lack efficient methods for comparing complex fuzzy n umbers that are induced by arithmetic operations. In this paper a prob abilistic approach is taken. Membership functions of fuzzy numbers are first converted into probability density functions. The Mellin transf orm is then introduced to compute the mean and the variance of a compl ex fuzzy number. The fuzzy number with the higher mean is ranked highe r. If the means are equal, the one with the smaller variance is judged higher rank. Two numerical examples and a fuzzy multiple attribute de cision analysis are illustrated.