Customer evaluation for order acceptance using a novel class of fuzzy methods based on TOPSIS

Customer evaluation plays an important role as a part of the order acceptance process of suppliers in optimally allocating resources and prioritizing orders accordingly. In this paper, a new class of fuzzy methods for evaluating customers is applied. Firstly, our approach tackles the issue of uncertainty that is inherent in the problem of customer evaluation that involves qualitative criteria by employing the method proposed by Yong [Yong, D. (2006). Plant location selection based on fuzzy TOPSIS. International Journal of Advanced Manufacturing Technology, 28(7-8), 839-844] in order to efficiently transform linguistic assessments of the weights of criteria and of the ratings of customers into crisp numbers. Secondly, the TOPSIS method is modified in order to integrate the behavioral pattern of the decision maker into its "principle of compromise". In this context, a new model for the aggregating function of TOPSIS that is based on a fuzzy set representation of the closeness to the ideal and the negative ideal solution is applied. In particular, we use the class of intersection connectives proposed by Yager [Yager, R. R. (1980). On a general class of fuzzy connectives. Fuzzy Sets and Systems, 4(3), 235-242] that enables a formal definition of the relation between the closeness to the ideal solution and the closeness to the negative ideal solution. Thus, a class of methods is formulated whose different instances correspond to different behavioral patterns of the decision makers, e.g. with preference to customers that make as much profit as possible but also avoid as much risk as possible or to customers that are performing well in at least one of the profit and risk criteria. A numerical example, illustrating the application of this class of methods to customer evaluation is given.