As a fuzzy set extension, the hesitant set is effectively used to model situations where it is allowable to determine several possible membership degrees of an element to a set due to the ambiguity between different values. Some new hesitant fuzzy operational rules are introduced based on the Hamacher t-conorm and t-norm, whereby we present a multi attribute decision making method under hesitan...

Many real-world decision making problems require multiple criteria which can be from different nature. This implies to define a heterogeneous context with different types of information that experts will use to provide their assessments. But sometimes, experts do not have enough knowledge or information to assess the criteria and they hesitate to express their assessments. Therefore, in this co...

With respect to decision making problems by using probabilities, immediate probabilities and information that can be represented with hesitant fuzzy information, some new decision analysis are proposed. Then, we have developed some new probability aggregation operators with hesitant fuzzy information: probability hesitant fuzzy weighted average (P-HFWA) operator, immediate probability hesitant ...

In this paper, a hesitant fuzzy multiple attribute group decision making problem where there exists prioritization relationships over the attributes and decision makers is studied. First, some Einstein operations on hesitant fuzzy elements and their properties are presented. Then, several generalized hesitant fuzzy prioritized Einstein aggregation operators, including the generalized hesitant f...

In this paper, we defined some aggregation operators to aggregate generalized hesitant fuzzy elements and the relationship between our proposed operators and the existing ones are discussed in detail. Furthermore, the procedure of multicriteria decision making based on the proposed operators is given under generalized hesitant fuzzy environment. Finally, a practical example is provided to illus...

The choice of membership functions plays an essential role in the success of fuzzy systems. This is a complex problem due to the possible lack of knowledge when assigning punctual values as membership degrees. To face this handicap, we propose a methodology called Ignorance functions based Interval-Valued Fuzzy Decision Tree with genetic tuning, IIVFDT for short, which allows to improve the per...