The Research Of Determining Weights Method For Multiple Attribute Group Decision Making Based On Multi-granularity Two-tuple Linguistic Information

Practical decision information is hard to be quantified, because many facts such as complexity,uncertainty of things and the ambiguity of human thought always place restrictions on it. Using a specified language to express it is a better choice. In the process of group decision making, different decision-makers are likely to use linguistic terms of different granularity to describe the same object of decision-making based on his or her preference. So it is valuable to study multiple attribute group decision making problems with multi-granularity linguistic assessment information and application.In this dissertation, some analysis and research on the preference information in the form of multi-granularity two-tuple linguistic information are carried out, then some multi-objective programming models are constructed in order to determine the attribute weights, and the responding decision making methods are given, main jobs are as follows:In chapter 1, first of all, the author introduce the background and the current research situation of multiple attribute group decision making problems with multi-granularity two-tuple linguistic information, and point out the main research tasks of the article.In chapter 2, two new methods are proposed for multiple attribute group decision making problems with multi-granularity linguistic assessment information, based on two-tuple linguistic information. Firstly, a transformation function is given to uniform the multi-granularity linguistic preference information into the form of two-tuple linguistic information in basic linguistic term set. Then, multi-objective programming models based on relative entropy and project method, by which the attribute weight information is incomplete, are established. By solving these models, the attribute weights can be determined. The two-tuple aggregation operator is utilized to aggregate the linguistic assessment information corresponding to each alternative in order to rank the alternatives and select the most desirable one(s).In chapter 3, two new methods are proposed for multiple attribute group decision making problems with multi-granularity interval linguistic information, based on interval two-tuple linguistic information. Firstly, a transformation function is given to uniform the multi-granularity interval linguistic preference information into the form of interval two-tuple linguistic information in basic linguistic term set. Then, multi-objective programming models based on maximizing deviation and TOPS IS method, by which the attribute weight information is completely unknown or partly unknown, are established. By solving these models, the attribute weights can be determined. The interval two-tuple aggregation operator is utilized to aggregate the linguistic assessment information corresponding to each alternative. The possibility degree of interval two-tuple linguistic information and the relative closeness degrees between each alternative and idea alternative are utilized to the result of aggregation in order to rank the alternatives and select the most desirable one(s).In chapter 4, a new combination weighting method is proposed for multiple attribute group decision making problems with multi-granularity linguistic assessment information. Based on sum of square deviates, an optimal combination weights model is proposed in order to make full use of information, which is contained in the subjective weighting methods and the objective weighting methods. By solving this model, the attribute weights can be determined.In chapter 5, the example analysis of objective weighting methods and combination weighting methods, which are put forward in the last three chapters, is given to demonstrate the feasibility and practicability of the proposed methods.In chapter 6, full text job is summed up, and the prospect of the research on multiple attribute group decision making based on multi-granularity two-tuple linguistic information is looking into the distance.