Research On The Multicritiera Decision Making Based On Intuitionistic Fuzzy Set Theory

Since Zadeh brought forward the fuzzy set theory in 1965, it has been widely usedin various fields in modern world. Intuitionistic fuzzy set theory is an extension fortraditional fuzzy set theory. As it considered the element's degree of the membership andnon-membership to the set at the same time, it is more flexible and practical than thetraditional fuzzy set when dealing with these questionable and uncertain problems. Fuzzymultiple attribute decision making theory is an important branch of modern decisionscience and its theories and methods have been widely used in economics, management,engineering, military, and many other areas. Decision makers often face with complexproblems in uncertain environment. Sometimes because of the ambiguity of humanthinking, decision makers are often unable or difficult to give the definite decisioninformation; it is easier for them to give some uncertain decision making informationwhich could be easily denoted by fuzzy numbers. In reality, many decision makingproblems are uncertain, which made the fuzzy multiple attribute decision makingproblems caused great concern in academic community. To solve these problems, thisthesis carried out a series of research as following.First, provide a brief introduction to the intuitionistic fuzzy number; introduce thetraditional score function and intuitionistic fuzzy information integration operators. Thenimprove the traditional score function, provide a new score function considering allingredients including membership, non-membership and intuition, then use it to solve theintuitionistic fuzzy multiple attribute decision making problems. Improve the traditionalfuzzy information integration operator. Define the similarity degree to measure thedistance of two intuitionistic fuzzy vectors. Based on these two ideas, two method havebeen brought forward to solve the intuitionistic fuzzy multiple attribute decision makingproblems witch we know the complete weight information, and also provide twoexamples to prove its availability. For problems that we only know partial weightsinformation, an approach based on the proposed score function is given to address suchintuitionistic fuzzy multiple attribute decision making problems.For those problems that attribute weights information is completely unknown, thisthesis presents a method based on entropy to compute the weighing coefficient, and thendetermine the best option.