Approaches to interval intuitionistic trapezoidal fuzzy multiple attribute decision making and their application to evaluating the cluster network competitiveness of SMEs

Abstract

With respect to multiple attribute decision making problems with interval intuitionistic trapezoidal fuzzy information, some operational laws of interval intuitionistic trapezoidal fuzzy numbers, score function and accuracy function of interval intuitionistic trapezoidal fuzzy numbers are introduced. An optimization model based on the maximizing deviation method, by which the attribute weights can be determined, is established. For the special situations where the information about attribute weights is completely unknown, we establish another optimization model. By solving this model, we get a simple and exact formula, which can be used to determine the attribute weights. We utilize the interval intuitionistic trapezoidal fuzzy weighted averaging (IITFWA) operator to aggregate the interval intuitionistic trapezoidal fuzzy information corresponding to each alternative, and then rank the alternatives and select the most desirable one(s) according to the score function and accuracy function. Finally, an illustrative example for evaluating the cluster network competitiveness of small and medium-sized enterprises is given to verify the developed approach and to demonstrate its practicality and effectiveness.