Determining the Parameter of Distance Measure Between Dual Hesitant Fuzzy Information in Multiple Attribute Decision Making

Abstract

To cope with complex decision situations, this paper develops a multiple attribute decision-making method with dual hesitant fuzzy elements (DHFEs) which are created by combining the advantages of hesitant fuzzy elements with those of intuitionistic fuzzy numbers. In the method, a generalized distance measure between DHFEs with a parameter is designed to overcome the drawbacks of the existing hesitant fuzzy distance measures and DHF distance measures. Due to limited knowledge, experience, and cognitive capability, it is difficult for a decision maker to provide attribute weights and the parameter of the distance measure. With the help of the closeness coefficient, the proposed distance measure is used to determine the parameter and further to determine attribute weights. To maximize the closeness coefficient of each alternative, an optimization model is first constructed for each alternative to identify its most favorable parameter. With the aim of determining the optimum parameter which is fair to each alternative, another optimization model is constructed to minimize the differences between the optimum parameter and the most favorable parameter of each alternative. By using the optimum parameter, the attribute weights and the closeness coefficient of each alternative can be precisely calculated and used to rank all alternatives and generate solutions. An efficiency evaluation of technology transfer problem is performed by using the proposed method to demonstrate its applicability and validity.