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Multi-attribute Group Decision Making and Its Applications Based on Intuitionistic Fuzzy Sets

Author: DingXiaoYang
Tutor: FuHaiYan
School: Hainan Normal University,
Course: Applied Mathematics
Keywords: intuitionistic fuzzy sets TOPSIS method dominance degree of intuitionistic fuzzyvalue dominance degree of intuitionistic fuzzy value group decision making
CLC: C934
Type: Master's thesis
Year: 2013
Downloads: 23
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In this paper, we study multi-attribute group decision making problems based on intuitionistic fuzzy sets. First of all, introduces the basic knowledge of fuzzy sets and intuitionistic fuzzy set,using the basic principles of TOPSIS method and improving the traditional TOPSIS method, Put forward the corresponding multiple attribute group decision-making method. In order to illustrate this method is superior to the traditional TOPSIS method, two corresponding theorems are binged forward,and the details of the proof is given.Secondly, in many of the existing literature, compare the size of intuitionistic fuzzy values,generally calculated the score function and the precision function of intuitionistic fuzzy values,assigned the hes-itation degrees according to certain proportion to the degree of membership and nonmembership,then converted the Two-dimensional intuitionistic fuzzy values to One-dimension to compare the size of in-tuitionistic fuzzy values.But this transformation is easy to lose some information of intuitionistic fuzzy values.That is why we define the dominance degree of intuitionistic fuzzy value and intuitionistic fuzzy set,study their properties and proof some related theorems,then new method to compare the intuitionistic fuzzy values is proposed to keep the more information of intuitionistic fuzzy value.Finally, based on the dominance degree of the intuitionistic fuzzy values, in the attribute weights and policy makers weights are known, second case is attribute weights is incomplete, decision makers weights unknown three cases, Put forward the corresponding multiple attribute group decision-making method. The advantage of this method is every two decision schemes are compared, finally chooses the best scheme; But the existing methods are only gathered their information of the program, which don’t compared with other schemes. Feasibility and effectiveness of the proposed method is verified by an example.

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