Research On Methods And Application Of Multi-attribute Decision Making With Incomplete Information Based On Fuzzy Set Theory

The multi-attribute decision making with incomplete information is such decision problem in which the evaluation of parameters is often impossible to completely obtain their values. For this kind of decision making does not require exact information about decision parameters such as attribute weights and preference values, it has been testified to be considerably reasonable to simulate decision making processes and activities in the real world. Therefore, this dissertation focuses on several problems of multi-attribute decision making with incomplete information by using the information technology and theory of optimization. The main contents discussed are as follows.(1) Based on the definition of the complete consistency of fuzzy judgment matrix, an index of identifying the degree of consistency is presented, and a new method for regulating the consistency of the fuzzy judgment matrix is proposed. An optimization model is set up to obtain an aggregating method in group decision making, where theoretic analysis shows that the aggregating method is practical. After that, some methods are put forward to solve ranking problem for fuzzy judgment matrix, then several optimization methods for ranking are analyzed comparatively.(2) An experimental analysis to observe the triangular fuzzy TOPSIS results yielded by different distance measures is made. A comparative analysis of triangular fuzzy TOPSIS ranking form each distance measure is illustrated with discussion on consistency rates and standard deviation. Otherwise, by establishing a linear programming model about the maximal deviation of weighted attribute values, an approach to deal with attribute weights which are completely unknown is developed by using expected value operator of fuzzy variables.(3) Considering much information about hesitancy and vagueness inherent to intuitionistic fuzzy sets, a new class of distance for describing the deviation degrees between intuitionistic fuzzy sets is introduced. Furthermore, the measure of similarity degree for each alternative to ideal point is calculated through using the new proposed fuzzy distance. A model of TOPSIS is designed with the introduction of the particular closeness coefficient composed of similarity degrees for alternative ranking.(4) A new method for ranking interval-valued intuitionistic fuzzy numbers is proposed by the distance measure, based on which an approach for decision making with interval-valued intuitionistic fuzzy information is developed. In addition, an experimental analysis to observe the decision making results yielded by different distance measures of interval-valued intuitionistic fuzzy numbers is made. And the result of comparative analysis of alternative ranking form each distance measure shows that the normalized Hamming distance is illustrated to be more reasonable than other distances in multi- attribute decision making under interval-valued intuitionistic fuzzy environment.(5) The theory of multi-attribute decision making is applied to the evaluation of electronic resource service in academic libraries. According to the performance measurement indicator system of electronic resources, service quality of some electronic resources is measured by using the developed method of fuzzy multi-attribute decision making. Finally, some conclusions are gained to help improving the electronic resource service and construction.