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研究生(外文):Huang, Yen-Shan
論文名稱:Demand-Driven Disassembly Plan for a Robust Closed-Loop Supply Chain System
指導教授(外文):Wang, Hsiao-Fan
外文關鍵詞:Demand-Driven Disassembly PlanningClosed-Loop Supply ChainEnd-of-Life Recovery OptionsRobust Programming
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In the wake of imminent government regulations and consumer awareness of environment-friendly manufacturing, the manufacturers must take the responsibility of the used products. Closed-loop supply chain system, which integrates the forward and reverse logistics, is a desirable policy for retaining recoverable resources and extending life cycle of products. In this thesis, we propose a mixed-integer programming model to contend a disassembly planning problem under a closed-loop supply chain system with multi-period, multiple products, and hierarchical product’s structure. The objective of the model is to determine the optimal volume and timing of each type of end-of-life (EOL) products to be recycled from end-users. The recycled products are then disassembled to be reused, remanufactured, repaired, or disposed. The optimal disassembly and recovery strategies are also determined under the constraints of capacities and satisfying demand for products. Furthermore, the proposed model accounts for the market mechanism under a closed-loop environment, including timing, quality and quantity issues of recycled EOL products. The numerical results of the illustrative example show the validity of the model being able to provide the required information for policy making.
However, due to the uncertainty exists in the proposed closed-loop supply chain system, a series of scenario analysis is conducted to investigate the sensitivities of periodic demands, quality of recycled product, and timing of product return, and also the resulted impacts on optimal strategy. The results suggest that all of these factors are critical and substantial to influence the decision. Therefore, to mitigate the difficulty of decision-making resulted from the uncertainty in these factors, we propose a two-stage robust programming approach to determine a robust solution that provides the most adequate strategy by considering future scenarios at the beginning with a decision-maker’s attitude towards risk. The first stage decision is to determine a compromised solution that is close to optimal solution for every scenario while remaining a certain level of infeasibility of constraints, such as unsatisfied demand. Afterward, when the outcome of scenario realizes, the second stage decision, for example, inventory volume, is conducted to become a buffer for absorbing or mitigating uncertainty impacts.
Furthermore, the computational results confirmed the trade-off relationship between solution robustness and model robustness, which are also kernel results of the robust model apart from expected profit. Finally, a contingency plan of a robust decision is developed, providing higher profit when shortage and excessive of procurement are both allowed.


中文摘要 vii
1.1 Research Background 1
1.2 Motivation for Current Research 2
1.3 Problem Statement and Research Objectives 5
1.4 Organization of Thesis 6
2.1 Introduction 8
2.2 Closed-Loop Supply Chain (CLSC) 8
2.2.1 The Activities in a Closed-Loop Supply Chain 10
2.2.2 Life Cycle Impacts on Quantity and Quality of the Returned Products 11
2.3 End-of-Life & End-of-Use Recovery Selection 13
2.4 Disassembly 15
2.4.1 Design for Disassembly Representation 18
2.4.2 Demand-driven Disassembly Planning 22
2.5 Robust Optimization 23
2.5.1 Measure of Robustness 25
2.5.2 Two-Stage Robust Programming 25
2.6 Concluding Remarks 29
3.1 System Boundary and Framework 30
3.2 Mathematical Representation of Products Structure 33
3.2.1 Mathematical Representation of Products Structure 33
3.2.2 Disassembly Configurations for Modules 35
3.3 The Recovery Options 36
3.4 Kernels of Closed-Loop Mechanism 36
3.5 Mathematical Model 40
3.5.1 Notations 40
3.5.2 The Mixed-Integer Programming Model 45
3.6 An Illustrative Example in Certain Environment 49
3.6.1 Description of the Input Data 49
3.6.2 Numerical Results 55
3.6.3 Scenario Analysis 58
3.6.4 Discussion 65
3.7 Concluding Remarks 65
4.1 The Framework of Robust Optimization for the Proposed Model 66
4.2 Two-Stage Robust Programming 68
4.2.1 Uncertain Parameters Description 71
4.2.2 Classification of Decision Variables at Each stage 71
4.2.3 Measures of Robustness 72
4.2.4 The Proposed Robust Model 74
4.3 An Illustrative Example in Uncertain Environment 76
4.3.1 Description of the Uncertain Parameters 76
4.3.2 Numerical Results 77
4.3.3 Trade-Off between Solution Robustness and Model Robustness 82
4.4 Extension of the Proposed Robust Model 84
4.4.1 Observation of the Robust Outcome 84
4.4.2 Extended Robust Model 86
4.4.3 Numerical Results 88
4.5 Concluding Remarks 90

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