根據一般模糊數排序之新方法及區間值模糊數相似度測量之新方法以作模糊風險分析

In this thesis, we present two new methods for dealing with fuzzy risk analysis problems. We propose a fuzzy risk analysis algorithm based on a new method for ranking generalized fuzzy numbers and propose a fuzzy risk analysis algorithm based on a new similarity measure between interval-valued fuzzy numbers. First, we present a new method for ranking generalized fuzzy numbers. The proposed fuzzy ranking method considers the areas on the positive side, the areas on the negative side and the heights of the generalized fuzzy numbers to evaluate ranking scores of the generalized fuzzy numbers. We also prove the properties of the proposed fuzzy ranking method and show that it can overcome the drawbacks of the existing fuzzy ranking methods. Then, we apply the proposed method for ranking generalized fuzzy numbers to develop a new method for dealing with fuzzy risk analysis problems. Moreover, we also present a new similarity measure between interval-valued fuzzy numbers. The proposed method considers the degrees of closeness between interval-valued fuzzy numbers on the X-axis and the degrees of differences between the shapes of the interval-valued fuzzy numbers on the X-axis and the Y-axis, respectively. We also prove three properties of the proposed similarity measure and make an experiment to compare the experimental results of the proposed method with the existing similarity measures between interval-valued fuzzy numbers. Based on the proposed similarity measure between interval-valued fuzzy numbers, we present a new fuzzy risk analysis algorithm for dealing with fuzzy risk analysis problems. The proposed algorithm is more flexible than Chen and Chen’s method due to the fact that Chen and Chen’s method lacks the capability to let the evaluating values of the risk of each sub-component for fuzzy risk analysis to be represented by interval-valued fuzzy numbers.

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