臺灣博碩士論文加值系統

在本論文中，我們提出一個基於模糊數排序的模糊風險分析方法，首先，我們提出一個基於α-cuts、信度與信號/雜訊比的模糊數排序方法，此提出的方法可以克服一些目前現有方法的缺點。接著，我們應用此模糊數排序方法來處理模糊風險分析問題，此提出之模糊風險分析方法可以提供一個有用的方式來處理模糊風險分析問題。
在此論文中，我們亦提出一個處理優先多準則決策問題之新方法，其中每個候選者其低優先權準則之權重決定於其候選者是否滿足高優先權準則之要求，如果候選者沒有滿足高優先權準則的要求，則低優先權準則的權重都會為零，也就是低優先權的滿意程度不會對整體的滿意程度造成影響。此外，我們亦提出一個一般化優先多準則決策方法來處理當有些準則是相等優先權此情況的多準則決策問題，且這些相等優先權準則可以使用OWA 運算子或加權平均方法來聚合。此兩個方法可以克服Yager方法的缺點。本論文所提的優先多準則決策方法可以更具智慧且更具有彈性的處理多準則決策問題。

In this thesis, we present a new approach for fuzzy risk analysis based on the ranking of fuzzy numbers. First, we present a new method for ranking fuzzy numbers using the α-cuts, the belief features and the signal/noise ratios of fuzzy numbers. The proposed method can overcome the drawbacks of some existing methods for ranking fuzzy numbers. Then, we apply the proposed fuzzy ranking method to present a fuzzy risk analysis algorithm to deal with fuzzy risk analysis problems. The proposed fuzzy risk analysis algorithm can provide a useful way to deal with fuzzy risk analysis problems.
In this thesis, we also present a new method for prioritized multicriteria decision making, where the weights of the lower priority criteria of each alternative depends on whether each alternative satisfies the requirements of all the higher priority criteria or not. If the requirements of all the higher priority criteria can not be satisfied by the alternative, then the weights of the lower priority criteria are all zero. That is, the degrees of satisfaction with respect to the lower priority criteria do not affect the overall degree of satisfaction. Furthermore, we present a generalized prioritized multicriteria decision making method for handling multicriteria decision making problems in which some criteria may have equal priority and the criteria with equal priority are aggregated by using the ordered weighted averaging (OWA) operator or the weighted averaging method. The proposed methods can overcome the drawbacks of the Yager’s methods. The proposed generalized multicriteria decision making method can handle multicriteria decision making problems in a more intelligent and more flexible manner.

Abstract in Chinese……………………………………………………………………i
Abstract in English……………………………………………………………………ii
Acknowledgements……………………………………………………………………iii
Contents………………………………………………………………………………..iv
List of Figures and Tables…………………………………………………………….vi
Chapter 1 Introduction………………………………………………………………1
1.1 Motivation………………………………………………………………1
1.2 Organization of This Thesis……………………………………………..5
Chapter 2 Literatures Review…………………………………………..…………...6
2.1 Fuzzy Sets Theory……………………………………………………….6
2.2 Generalized Fuzzy Numbers and Their Arithmetic Operations...8
2.3 Summary………………………………………………………...……..10
Chapter 3 Fuzzy Risk Analysis Based on Ranking Fuzzy Numbers Usingα-Cuts, Belief Features and Signal/Noise Ratios…...………………………...11
3.1 A Review of the Existing Methods for Ranking Fuzzy Numbers.........11
3.2 A New Method for Ranking Fuzzy Numbers Based on α-Cuts, Belief Features and Signal/Noise Ratios................................................20
3.3 Apply the Proposed Method for Ranking Fuzzy Numbers to Handling Fuzzy Analysis Problems........................................................................28
3.4 Summary……………………………………………………………..32
Chapter 4 A New Model for Prioritized Multicriteria Decision Making...…..….34
4.1 Preliminary……………………………………...…………………….37
4.1.1. OWA Operators…………………………..………………….37
4.1.2. Quantifier Guided Aggregation………………………….......38
4.1.3. Prioritized Multicriteria Decision Making…………………..39
4.2 The Proposed Method for Prioritized Multicriteria Decision Making....53
4.3 The proposed Method for Generalized Prioritized Multicriteria
Decision Making……………………………………………………….60
4.4 An Example………………………………………………………….71
4.5 Summary…………………………………………………………….…85
Chapter 5 Conclusions……………………………………………………………...87
5.1 Contributions of This Thesis………….…………………………..87
5.2 Future Research..………………………….…………………………...88
References……………………………………………………………………..………89

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