Research On Multi-attribute Decision Making Method Based On Intuitionistic Fuzzy Entropy

The problem of multiple attribute decision making is a branch of the operations research discipline and a part of the decision-making problem.Due to the complexity and uncertainty of the objective world and the degree of cognition of the decision-maker on the objective matters,the obtained decision information cannot be described by accurate numerical values,it is more realistic to describe the uncertainty and unknown of decision information by intuitionistic fuzzy sets.Intuitionistic fuzzy entropy can reflect the uncertainty and unknown of intuitionistic fuzzy sets by numerical values.Uncertainty and unknown are common phenomena in multiple attribute decision making problems.Therefore,it is better to use intuitionistic fuzzy sets to describe multiple attribute decision making problems and to apply intuitionistic fuzzy entropy to measure uncertainty and unknown in multiple attribute decision making problems.Based on the intuitionistic fuzzy entropy,the problem of multiple attribute decision making will be studied in this thesis.After analyzing the shortcomings of intuitionistic fuzzy entropy proposed by other scholars,a new formula for intuitionistic fuzzy entropy is proposed,and it is verified that the formula satisfies the axiomatic definition of intuitionistic fuzzy entropy.The advantages of the formula are illustrated by several examples.Then,the calculation formula of intuitionistic fuzzy entropy is constructed by exploiting the property advantages of the cosine function,and comparing this formula with other formulas for an example illustrates the advantages of this formula.In order to reduce the subjectivity of attribute weights,intuitionistic fuzzy entropy is used to solve attribute weights.Finally,the multiple attribute decision-making steps based on intuitionistic fuzzy entropy are given and applied to the example.The decision result of the example is given to illustrate the effectiveness of the decision-making steps.