Interval Type-2 Fuzzy Multiple Attribute Group Decision Making Method Based On Similarity Measure And Its Application

The essence of the multiple attribute group decision making is a process of ranking and selecting the best alternatives for a group of decision makers with multiple attributes by using a proper aggregation technique.However,due to the fuzziness and uncertainty of human thinking and objective reality,decision preference may be provided with fuzzy or uncertain information,and then it is necessary to express the decision preference with fuzzy sets.Therefore,fuzzy multiple attribute group decision making plays a very important role in our lives,which are applied to many fields,such as economics,science,military and engineering,etc.The purpose of this thesis is to develop some approaches to multiple attribute group decision making with interval type-2 fuzzy information.The paper involves the following six chapters.In chapter 1,we mainly introduce the background and the research status of this paper,analyze the main research contents and innovation of this paper.In chapter 2,the concepts,operation rules and raking method of interval type-2 trapezoid fuzzy numbers and intuitionistic trapezoid fuzzy numbers are introduced.In chapter 3,develops a multiple attribute group decision making based on interval type-2 fuzzy similarity measure.First of all,we analyze the deficiency of the existing interval type-2 fuzzy similarity measures,and a new interval type-2 fuzzy similarity measure is proposed on the basis of the perimeters,areas and negative exponential distances of the interval type-2 fuzzy numbers,and then some desirable properties of the new similarity are investigated.Moreover,two models to determine interval type-2 fuzzy expert weights and attribute weights are constructed by using the new similarity measure.Furthermore,a new fuzzy multiple attribute group decision making approach is presented based on the new interval type-2 fuzzy similarity measure.In the end,an example is given to show the rationality and effectiveness of the proposed method.In chapter 4,we propose an extended TOPSIS method to determine the expert weights in which the attribute values and the attribute weights take the form of the interval type-2 fuzzy numbers,and a method on the incentre of centroids and Euclidean distance to rank interval type-2 fuzzy numbers is put forward.This chapter also proposes an extended TOPSIS method to deal with group decision making problems,and an example about cars evaluation is given to show the feasibility and effectiveness of the proposed approach.In chapter 5,a continuous intuitionistic trapezoidal fuzzy averaging(CITFA)operator is presented to deal with multiple attribute group decision-making problems with intuitionistic trapezoidal fuzzy numbers based on ?-cut set,?-cut set and continuous interval ordered weighted averaging(COWA)operator.Moreover,a new continuous intuitionistic trapezoidal fuzzy similarity measure is defined on the basis of the CITFA operator.Meanwhile,in order to determine experts weights and attributes weights,two models are constructed by using the new similarity measure.Furthermore,a new fuzzy multiple attribute group decision making approach is developed based on the continuous intuitionistic trapezoidal fuzzy similarity measure.In the end,the sensitivity analysis is used to analyze the variation of experts weights,attributes weights and ranking values on different attitude parameters.The effectiveness and rationality of the proposed method are illustrated through an investment selection group decision making problem.In chapter 6,some conclusions in this thesis are summarized and the future research proposals are suggested.