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研究生： 徐昕煒
Hsu, Hsin-Wei
論文名稱： 確定與不確定閉迴圈物流之建模、求解與分析
The Modeling, Resolution and Analysis for Deterministic and Uncertain Closed-loop Logistics
指導教授： 王小璠
Wang, Hsiao-Fan
口試委員: 方述誠
Fang, Shu-Cherng
吳政鴻
Wu, Cheng-Hung
姚銘忠
Yao, Ming-Jong
陳茂生
Chern, Maw-Sheng
學位類別： 博士
Doctor
系所名稱： 工學院 - 工業工程與工程管理學系
Department of Industrial Engineering and Engineering Management
論文出版年： 2012
畢業學年度： 100
語文別： 英文
論文頁數： 159
中文關鍵詞： 封閉迴圈供應鏈 、選址 、遺傳演算法 、模糊理論 、數學規劃 、風險分析
外文關鍵詞： Closed-loop supply chain, Facility location, Genetic algorithm, Fuzzy set theory, Mathematical programming, Risk Analysis
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    • Due to the limited energy and resources, sustainability has become an important topic when it comes to environmental protection in recent years. Among all the strategies and policies, Green Supply Chain (GSC) management is suggested an efficient tactic to achieve this goal. Although there were many logistics models in the literatures, most of them were case based and not in a closed-loop. Therefore, they lacked generality and couldn’t serve the purposes of recycling, reuse and recovery required in a green supply chain. Furthermore, for those papers which discussed the closed-loop supply chain, most of them did not consider the uncertain environment in general terms. In this study, it aims to integrate the forward and reverse logistics and construct the closed-loop logistic models with deterministic and uncertain scenarios. Moreover, to facilitate logistic operations, determination of the locations of manufactories, distribution centers, and dismantlers is taken into account in order to reduce the overall operation cost. As regard to uncertainty, two issues are tackled: one is based on decision maker’s preference with risk information, and another is the scenario with shortage and surplus. Due to the uncertainty features caused by the overlapping phenomenon, the fuzzy numbers are used to clarify the situation. Since the network problems like this has been recognized as an NP-hard problem, an efficient and accurate algorithm should be developed for effective applications. In this study, the revised spanning-tree based genetic algorithm is proposed for both deterministic and uncertain problems.

    今時今日，由於能源與資源的有限，環境保育意識的抬頭，「永續發展」成為是一個相當重要的課題。為顧全經濟的發展與環境的維護，「綠色供應鏈管理」已成為達到永續發展的一個必要的步驟與方法。過去，傳統供應鏈管理對於物流的問題有很深入的研究；近來，綠色物流多針對逆向物流的部分做探討。由於多數的研究或缺乏整合正逆向物流的「封閉迴圈物流」，或採取個案研究的方式作分析，因此，缺乏一般性而無法解決綠色供應鏈中「回收、再利用與再製造」的目的。因此，本研究的目的為整合正逆向物流系統，且針對綠色供應鏈中相當重要的「不確定性」建構一般性的封閉迴圈物流模型，俾能處理確定與隨機環境的實際情境。在考慮營運成本下，除使總成本最小化，並針對「製造廠、配銷中心和拆解廠」的廠址做出適當的選擇。此外，在隨機情境中我們特別針對引起不確定的主要因素討論兩個議題: 其一為考慮決策者之決策行為，提供其最佳規劃之建議與風險評估；其二則為考慮不確定環境下「過剩」與「不足」的情形，建構符合原始隨機環境議題之模型架構。此外，由於模糊數擁有足以處理高度相依的不確定環境的特性，因而被採用於描述綠色物流中的隨機問題。由於此類型的物流問題，多為非多項式的困難(NP-hard)問題。發展一個快速且精確的演算法是必須的。而遺傳演算法無論在確定與不確定的問題上都成功的被廣泛應用，因此，藉由建構一個改良式伸展樹的遺傳演算法，同時能夠解決確定性與非確定性的封閉迴圈物流模型是本研究的另一項重要議題。

    ACKNOWLEDGEMENT v ABSTRACT vi 摘 要 vii NOTATIONS viii TABLE CAPTIONS xiii FIGURE CAPTIONS xv Chapter 1 INTRODUCTION 1 1.1 Motivation and Purposes of this Study 3 1.2 Framework of the Thesis 7 Chapter 2 LITERATURE REVIEW 9 2.1 Reverse and Closed-loop Logistics 9 2.1.1 Deterministic Closed-loop Logistics 11 2.1.2 Uncertain Closed-loop Logistics 13 2.1.3 Summary 16 2.2 Modeling and Solution Procedures for Logistic Problems 16 2.2.1 The Conventional Logistic Models 17 2.2.2 Genetic Algorithm 18 2.2.3 Summary 20 2.3 Summary and Conclusions 20 Chapter 3 METHODOLOGIES FOR RESOLVING UNCERTAINTY 22 3.1 The Defuzzification Procedures 23 3.1.1 The Possibilistic Mean 25 3.1.2 The Mean Square Imprecision Index 28 3.1.3 Summary and Discussion 29 3.2 The Methodology of Interval Programming 29 3.2.1 The General Interval Programming Model 32 3.2.2 The Solution of an Interval Programming Model 34 3.3 Algorithms for Resolving Uncertainty 34 3.4 Summary and Conclusions 35 Chapter 4 THE PROPOSED CLOSED-LOOP LOGISTIC MODELS 37 4.1 The Deterministic Closed-loop Logistic Model 37 4.1.1 The GCLL Model 39 4.1.2 The Transformed Integer Linear Programming Model 42 4.1.3 An Illustrative Example 45 4.1.4 Sensitivity Analysis for Recovery and Landfilling Rates 49 4.1.5 Summary and Discussion 55 4.2 The Uncertain Closed-loop Logistic Model 56 4.2.1 Fuzzy-GCLL Model 57 4.2.2 The Preference Model of Interval Programming 59 4.3 The Cases of Shortages and Surpluses for Uncertainty 60 4.3.1 The Possibilistic Mean of Shortage and Surplus 61 4.3.2 The Mean Square Imprecision Index of Shortage and Surplus 67 4.3.3 The Possibilistic Mean-based Closed-loop Logistic Model 74 4.4 Summary 78 Chapter 5 ANALYSIS FOR PREFERENCE MODEL 80 5.1 The Risk Analysis 81 5.1.1 Risk Level of Interval Programming 82 5.1.2 Trade-off Analysis between Risk and Cost 87 5.1.3 Numerical Example 88 5.1.4 Summary and Discussion 91 5.2 Numerical Illustration of Preference Model 91 5.2.1 Numerical Example 91 5.2.2 Parameter Analysis 97 5.2.3 Post-analysis and Discussion 103 5.3 Summary and Conclusions 105 Chapter 6 THE REVISED SPANNING-TREE BASED GA 107 6.1 Revised Determinant Encoding 109 6.1.1 The Initial Chromosome and Determinant Encoding 110 6.1.2 The Flows and Costs of Determinant Encoding 115 6.2 Genetic Operations 117 6.2.1 The Operators of Integer Numbers 117 6.2.2 The Operators of Real Numbers 119 6.2.3 Selection and Termination Conditions 120 6.2.4 Dynamic Adjustable Crossover and Mutation rate 121 6.3 Summary of the Proposed Algorithm 122 Chapter 7 NUMERICAL EXAMPLE AND EVALUATION 123 7.1 The Results of Deterministic Model 123 7.1.1 Preliminary Evaluation 124 7.1.2 Advanced Evaluation 126 7.1.3 Summary and Discussion 128 7.2 The Results of Uncertain Model 130 7.2.1 Test of the Accuracy 130 7.2.2 Different Types of Shortage and Surplus Costs 132 7.2.3 Test of the Efficiency 134 7.2.4 Summary and Discussion 137 7.3 Summary and Conclusions 137 Chapter 8 SUMMARY AND CONCLUSION 139 8.1 Summary of Thesis 140 8.2 Possible Future Work 142 8.3 Conclusions 143 REFERENCES 146

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