Study On Multi-attribute Group-decision Making Models And Methods Using Intuitionistic Fuzzy Sets

According to the characteristics of fuzziness and complexity for many group-decision making problems in real world, and to the theories and methods on group-decision making, the intuitionistic fuzzy sets theory, which is seldom used in the papers on group-decision making problems, is applied to study multi-attribute group-decision making problems. The model of multi-attribute group-decision making based on intuitionistic fuzzy sets is put forward after analyzing the development process and application of multi-attribute group-decision making, and describing briefly the theory of intuitionistic fuzzy sets and membership. It investigates systematically methodologies and implementation process for group-decision making with complete or incomplete preference information. Feasibility and effectiveness of the proposed methods are illustrated using a numerical example and the resulting outcomes are expounded. Detailed ways is organized as follows: the data are described using intuitionistic fuzzy sets. Classical compromise TOPSIS algorithm, the relative membership degree method and a new method based on the consistency degree under different forms of preference information are transplanted to fuzzy multi-attribute group-decision making. And lengthways thought-mode is changed by landscape thought-mode. The minimum average weighted deviation method and the fuzzy iterative method are revised and extended to group-decision making under partial weight information. All theories and methods which present effective ways to solve multi-attribute group-decision making problems for practical complex system presented in this thesis are easy to operate apply, and are of great value to scientific researches and real applications.This dissertation is organized as follows: chapter 1 is the preface, the backgrand, motivation, path, content and truss are presented in this chapter. Literature review and academic discussion are given to intuitionistic fuzzy sets theory and membership function, and the conception of the relative membership function is put forward in chapter 2. After this, the corresponding decision-making model has been proposed. The weights of attributes which are estimated using the linear programming model constructed by Deng-Feng Li are transplanted to group-decision makingproblems. Compromise TOPSIS algorithm and the relative membership degree method have been proposed. Comparison analysis is made between the compromise TOPSIS method and the relative membership degree method. A new method based on the consistency degree under different forms of preference information is proposed, which considers the influence of group-evaluation result from the weight of the decision-makers and the consistency degree of expert-evaluation, making the methods and the outcomes more close to the impersonal fact. In chapter 4, the unknown weights of attributes are estimated using revised minimum average weighted deviation method and the fuzzy iterative method for group-decision making under partial weight information and are justified by using Lagrange multiplier function. And the demonstration and expectation are found in chapter 5.