Study On Methods Of Fuzzy Multi-Attribute Decision Making

As an important part of the modern decision theory, the multi-attribute decision theory has been widely used in construction work, military management, emergency decision-making and other areas. With the complexity and uncertainty of decision-making problems, the vagueness of the human mind results in attribute values are often given in the form of fuzzy linguistic variables. Therefore, the fuzzy multi-attribute decision-making problems have the important theoretical significance and the application value. In this paper, with the deep analysis of fuzzy numbers, intuitionistic fuzzy values and interval-valued intuitionistic fuzzy values, the problem of the fuzzy multi-attribute decision making under the fuzzy environment is studied.The methods of ranking fuzzy variables are discussed in this paper. With the deficiencies of the existing ranking methods of fuzzy numbers and interval values, the method of fuzzy relative entropy is proposed.To solve the disaster emergency repository location problem, the KM fuzzy weight weighted average algorithm has been used for fuzzy linguistic variables expert evaluation model which gives the order of the pros and cons of disaster emergency repository alternatives. It provides the basis for the decision of disaster emergency repository location problem.With the research on the nature of crisp values, fuzzy numbers, intuitionistic fuzzy values and interval-valued intuitionistic fuzzy values, the hybrid fuzzy weighed average algorithm is proposed for crisp values, fuzzy numbers, intuitionistic fuzzy values and interval-valued intuitionistic fuzzy values. The extended method of possible degrees is using for sorting hybrid fuzzy weighted averages.The fuzzy inference system with fuzzy linguistic variables is designed which includes the base of fuzzy rules, the fuzzy inference and the defuzzification module. With the determination of the fuzzy linguistic variables domain and the membership function, the formal description of the reasoning objectives of the fuzzy rules, the calculation of the nearness of fuzzy rules and incentive intensity, the final objective is reached.