Solving Fuzzy MCDM by Subtracting Benefit Criteria from Cost Criteria

Abstract This paper suggests a fuzzy MCDM (multiple criteria decision-making) approach, where ratings of alternatives versus criteria and weights of criteria are assessed in fuzzy numbers or linguistic values represented by fuzzy numbers. Criteria are classified into cost and benefit ones. In the proposed method, the ratings assigned by decision makers to each alternative versus each criterion and the weights assigned by decision makers to each criterion are averaged. The averaged cost and benefit ratings are further normalized into comparable scales respectively. The membership function of subtracting the summation of weighted normalized benefit ratings from that of weighted normalized cost ratings for each alternative can be developed by interval arithmetic of fuzzy numbers. The fuzzy number ranking method of centroid is then applied to determine the ordering of the alternatives. A numerical example of robot selection demonstrates feasibility of the proposed method.