On Fuzzy Multi-Attribute Decision Making Theory And Its Applications In Technical Economic Analysis

A review on the theory and applications of fuzzy Multi-attribute decision making iscarried out in this paper. This paper studies multi-attribute decision making theories andmethodologies and their applications, based on the uncertainty preference of decisionmakers, in technical economics and management.Main improvements and contributions of the paper are as follows.(1) A new normalization method for fuzzy numbers based on the decision maker'sideal solution is put forward. This method could retain the proportion between theoriginal numbers and does not vary with else alternatives. Further, this method takes intoaccount the decision maker's preferences for uncertainty, thus can achieve more accurateresults.(2)A new formula is put forward to calculate the fuzzy distances after taking intoaccount the decision maker's preferences for uncertainty. This formula can be used tocalculate not only distances between fuzzy numbers, but also distances between fuzzynumbers and crisp numbers. At the same time, it takes into consideration the effect ofthe decision maker's preferences for uncertainty on the fuzzy distances. And a newcomparison and ranking method for fuzzy numbers is put forward based on the formulamentioned above. This method is also applicable to the comparison of fuzzy numbersand crisp numbers, as is not feasible with tradition methods.(3) Three fuzzy weight determination methods, namely, fuzzy subjective weightsdetermination methods, objective weights determination methods and combined weightsdetermination methods, are put forward. The relationships between the three methodsand their transformation are studies. Methods to calculate the differences between fuzzynumbers, e.g., fuzzy entropy method, are put forward. Further, fuzzy objective weightdetermination methods based on fuzzy entropy and fuzzy combined weightdetermination method based on fuzzy subjective and objective weight, are put forward.(4)New fuzzy TOPSIS methods, new fuzzy analytical hierarchy process methodand the Angle Measure Evaluation Method are put forward. And numerical illustrationsapprove the effectiveness of those methods.