1. Hesitant Fuzzy Multiple Criteria Decision Analysis Based on TOPSIS

HFE which allows the membership degree of an element to a set represented by several possible values is a powerful tool to describe and deal with uncertain data. This chapter develops the decision making approach based on TOPSIS and the maximizing deviation model for solving MCDM problems in which the evaluation information provided by the decision maker is expressed in HFEs and the information about criteria weights is incomplete. There are two key issues being addressed in this approach. The first one is to establish an optimization model based on the maximizing deviation method, which can be used to determine the weights of criteria. The second one is to calculate the revised closeness index of each alternative to the hesitant fuzzy PIS. The considered alternatives are ranked according to the revised closeness indices of alternatives and the most desirable one is selected. An important advantage of this proposed method is its ability to relieve the influence of subjectivity of the decision maker concerning the weights of criteria and at the same time to remain the original decision information sufficiently. Additionally, the extended results in the interval-valued hesitant fuzzy situations are also pointed out.