# 臺灣博碩士論文加值系統

(44.200.30.73) 您好！臺灣時間：2022/08/09 19:37

:::

### 詳目顯示

:

• 被引用:0
• 點閱:101
• 評分:
• 下載:1
• 書目收藏:0
 Ranking fuzzy numbers, a significant component in decision making process, supports a decision maker in selecting the optimal solution. Althoung there are many existing ranking methods for fuzzy numbers, most of them suffer from some shortcomings. To overcome these shortcomings, this study proposes a new ranking approach for both normal and generalized fuzzy numbers that ensures full consideration of all information of fuzzy numbers. The proposed approach integrates the concept of centroid point, the left and the right (LR) areas between fuzzy numbers, height of a fuzzy number and the degree of decision maker’s optimism. Several numerical examples are presented to illustrate the efficiency and superiority of the proposed.To reduce uncertainty in decision making and avoid loss of information, this study also proposed a new fuzzy multi-criteria decision making (MCDM) approach based on the proposed ranking method for generalized fuzzy numbers. The applicability of the proposed fuzzy MCMD model is illustrated through a case study.
 ABSTRACT IACKNOWLEDGEMENT IIITABLE OF CONTENTS IVLIST OF TABLES VILIST OF FIGURES VIICHAPTER 1. INTRODUCTION 11.1. Research background and motivation 11.2. Research objectives and contributions 51.3. Research framework 5CHAPTER 2. FUNDAMENTALS 72.1. Classical sets (crisp sets) 72.2. Fuzzy sets 72.2.1. Terminology and Notation 72.2.2. Basic definitions 82.2.2.1 Linguistic variables: 92.2.2.2 Fuzzy arithmetic 92.2.2.3 Basic definitions of triangular and trapezoidal fuzzy numbers 102.2.2.4 Arithmetic operations for generalized fuzzy numbers 132.2.2.5 Basic properties of fuzzy quantities 14CHAPTER 3. REVIEW OF EXISTING APPROACHES FOR RANKING FUZZY NUMBERS 153.1. Centroid approaches 153.2. Deviation degree approaches 173.2.1. Wang et al.’s approach 173.2.2. Nejad and Mashinchi’s approach 193.2.3. Asady’s approach 203.3. Magnitude approaches for ranking fuzzy numbers 213.4. Chen and Chen’s approaches 243.5. Kumar et al.’s approaches 26CHAPTER 4. THE PROPOSED APPROACHES FOR RANKING FUZZY NUMBERS 304.1. The proposed epsilon-deviation degree approach 304.1.1. Shortcomings of deviation degree approaches 304.1.2. The proposed epsilon-deviation degree approach 344.1.3. Comparison the proposed epsilon degree approach with other existing approaches 384.2. The proposed approach for ranking generalized fuzzy numbers based on centroid and rank index 494.2.1. Shortcomings of Kumar et al.’s approach 494.2.2. The proposed approach 534.2.3. Comparison of the proposed approach with existing approaches 55CHAPTER 5. THE PROPOSED FUZZY MCDM APPROACH 615.1. The proposed fuzzy MCDM approach 615.2. Ilustrated example 64CHAPTER 6. CONCLUSIONS AND FUTURE RESEARCH DIRECTIONS 686.1. Conclusions 686.2. Recommendations for further research 69REFERENCE 71