Fuzzy Preference Relations For Hesitant Fuzzy Sets And Its Application In Multi-attribute Decision Making

Multiple attribute decision making involves all aspects of people's life.Whether it is strategic decisions based on the national level,such as food security?"One Belt One Road"?deleveraging?environmental protection and targeted poverty alleviation and so on,or business decisions based on the individual level,such as choosing a career?shopping?travel plans and so on,the key is sort the scheme.In recent years,the study of the multiple attribute decision making problems of close to the life structures concern of more and more scholars home and abroad in hesitant fuzzy environment.In this thesis,hesitant fuzzy multiple attribute decision making problem have been studied based on the fuzzy preference relation between hesitant fuzzy elements,the main contents are as follows:(1)Aiming at the flaws of existing aggregation operator?distance measure and so on based on decision information is not underutilized and lack the flexibility in the process of information integration of hesitant fuzzy sets,construct new fuzzy preference relation and research the additive consistency and then expand it to the generalized form by analyzing the average deviation between hesitant fuzzy elements.The new fuzzy preference relationship could sufficiently makes use of the decision information and effectively avoid the loss of decision information.(2)In the hesitant fuzzy environment,a new method of hesitant fuzzy multiple attribute decision making is constructed based on the proposed hesitant fuzzy preference relation and it's additive consistency and then the validity and rationality of the proposed method are verified by the example.(3)A new objective attribute weights optimization model is constructed utilize the maximum deviation method based on the proposed hesitant fuzzy preference relation and it's additive consistency.Based on this,a new objective attribute weights acquisition approach in the hesitant fuzzy environment is proposed and apply it to the multiple attribute decision making and then through the example is given to illustrate the feasibility of the method.