Designing a sustainable network design is a major issue due to its impact of efficiency and greenness of the supply chain. While most of researches are only focusing on the economic issues, recently, some researches have added the environmental dimensions. One dimension to make a green supply chain is extending the forward supply chain to collection and recovery of products in a closed-loop configuration. Remanufacturing is the basis of profit-oriented in reverse logistics, in which recovered products are collected and restored to be resold again in the market. However these two main issues are such a trade-off, it is difficult for companies to have low total costs and low CO2 emissions in the same time. A single-objective mixed integer programming formulation model is developed to minimize the total logistics costs by involving the carbon emissions. The model introduces the environment protection level applied in each facility to denote the environmental investment. Thus, in order to find the optimal solutions, a modified Genetic Algorithm (GA) is proposed to solve the closed-loop supply chain problems. Moreover, the proposed approach can provide an applicable configuration in real closed-loop supply chain operated by companies in order to construct a sustainable environment.

永續經營網絡之設計為一重要議題，由於其對供應鏈之效率及綠化程度的影響甚大，然而，過去大部分的研究只著重於經濟議題，近期研究也開始加入環境議題作為考量。在閉鎖式結構中藉由蒐集及回收產品的方式，擴展正向供應鏈，進而形成綠色供應鏈，而逆向物流中，將被回收的產品再度銷售至市場，以再製造作為利益導向的基底；對公司而言，同時掌握最低成本及最低二氧化碳排放量相當困難，兩者之間需要權衡，本研究設計一單目標混整數規劃模型涉及碳排放量以求最小化整體物流成本，該模型指出在環境投資時，各設施所需符合的環境保護層級，因此，應用改良式基因演算法 (modified Genetic Algorithm) 解決閉迴路供應鏈問題，以求得最佳解，此外，該方法提供公司可應用的閉迴路供應鏈結構，建構永續經營的環境。

摘要 I
Abstract II
List of Figures V
List of Tables IX
Chapter 1: Introduction 1
1.1 Background 1
1.2 Objectives 3
1.3 Methodology and Procedures 4
1.4 Organization of Thesis 5
Chapter 2: Literature Review 6
2.1 Review of Closed-Loop Supply Chain Logistics Network 6
2.2 Carbon Emissions Consideration 9
2.3 Genetic Algorithm (GA) in General/Closed-Loop Supply Chain Network Design 11
Chapter 3: Model Formulation 15
3.1 Problem Definition 15
3.2 Mathematical Model 18
Chapter 4: Solution Approach 29
4.1 Chromosome Representation 31
4.2 Decoding Algorithm 33
4.3 Fitness and Evaluation 54
4.4 Selection 55
4.5 Crossover 56
4.6 Mutation 59
4.7 Termination 62
Chapter 5: Computational Study 63
5.1 Experimental Case Study 63
5.2 Optimal Parameter Settings 66
5.3 Sensitivity Analysis 72
5.4 Experimental Results and Analysis 129
Chapter 6: Conclusion and Future Study 137
Bibliography 139

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