A new approach to solving fuzzy weighted average model

Abstract

This paper suggests a new approach to solving fuzzy weighted average (FWA) model, where t-distribution is used for normalization of quantitative data, ratings of alternatives versus qualitative criteria and the importance weights among criteria are assessed in linguistic values represented by fuzzy numbers. Membership functions of the final fuzzy evaluation values can be developed. A ranking method based on relative areas of a fuzzy number is applied to defuzzify these fuzzy numbers to order alternatives. Finally, a numerical example demonstrates the computational procedure of the proposed model.