Connectives and quantifiers in fuzzy sets
Fuzzy Sets and Systems - Special memorial volume on foundations of fuzzy reasoning
A fuzzy approach to select the location of the distribution center
Fuzzy Sets and Systems
On experimental equilibria strategies for selecting sellers and satisfying buyers
Decision Support Systems
A fuzzy closeness approach to fuzzy multi-attribute decision making
Fuzzy Optimization and Decision Making
An extension of TOPSIS for group decision making
Mathematical and Computer Modelling: An International Journal
Integration of fuzzy AHP and FPP with TOPSIS methodology for aeroengine health assessment
Expert Systems with Applications: An International Journal
A new multi-perspective framework for multi-attribute decision making
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
TOPSIS with fuzzy belief structure for group belief multiple criteria decision making
Expert Systems with Applications: An International Journal
A user centric service-oriented modeling approach
World Wide Web
Using hybrid MCDM to evaluate the service quality expectation in linguistic preference
Applied Soft Computing
A new decision making structure for managing arriving orders in MTO environments
Expert Systems with Applications: An International Journal
Review: A state-of the-art survey of TOPSIS applications
Expert Systems with Applications: An International Journal
Selecting Adequate Security Mechanisms in E-Business Processes Using Fuzzy TOPSIS
International Journal of Fuzzy System Applications
Expert Systems with Applications: An International Journal
Hi-index | 12.06 |
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.