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研究生： Nguyen Hong Phuc
Nguyen Hong Phuc
論文名稱： 技術與需求不確定之產能隨機動態規劃模式
Stochastic Dynamic Programming for Capacity Planning with Technology Selection under Demand and Technology Uncertainties
指導教授： 王孔政
Kung-Jeng Wang
口試委員: 王孔政
Kung-Jeng Wang
郭人介
Ren-Jieh Kuo
喻奉天
Vincent F. Yu
陳銘芷
Ming- Chih Chen
蔣明晃
Ming-Huang Chiang
陸行
Hsing Luh
學位類別： 博士
Doctor
系所名稱： 管理學院 - 工業管理系
Department of Industrial Management
論文出版年： 2018
畢業學年度： 106
語文別： 英文
論文頁數： 97
中文關鍵詞： 產能規劃 、技術選擇 、隨機動態規劃 、平行計算
外文關鍵詞： Investment analysis, Technology selection, Stochastic dynamic programming, Soft computing
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    • 產能規劃對高科技製造業至關重要，製造技術的快速發展和不確定性需求所帶來的風險讓此一問題面臨高度挑戰，技術的快速變革加快了投資技術的過時，而波動的需求則導致了缺貨成本或超額需求大幅提升。此外，高科技產業的設備是非常耗費資金的。因此，一個能考慮上述問題和應對方案的縝密優化模型對此產業而言，十分迫切，些微決策的改良質量將帶來顯著的節省。本研究同時關注生產計劃的兩個層面，在戰略方面涉及最佳技術的採用計劃，根據規劃期間的市場需求來確定最佳的容量水平。考慮需求和技術的不確定性，本研究以隨機動態規劃(Stochastic Dynamic Programming, SDP)建模，SDP模型的利潤則以隨機混整數規劃 (Stochastic Mixed Integer Programming) 建模，目標是將規劃期間之預期利潤最大化。對於模型之求解方法，本研究採用後向歸納法來解SDP模型，而相應的利潤則由本研究提出的啟發式算法求解。此外，本研究更進一步利用平行計算技術來緩解SDP模型在計算上的負擔。經過實驗，本研究證明所提模型的有效性，並提供若干管理意涵。

    Capacity planning optimization is crucial to high-tech manufacturing industries. Such a problem is challenged by the risks involved in the fast-paced developments in the manufacturing technology and uncertain demands. The rapid technological changes cause the invested technology to become obsolete faster while fluctuating demands cause an increase in costs of shortage or excess demands. Furthermore, manufacturing equipment in hi-tech industries is very capital intensive. Therefore, a sophisticated model considering aforementioned issues and corresponding solution approaches are urgent in such industries. That is because a slight improvement in the decision quality will result in significant savings. This study simultaneously focuses on both levels of production planning. The strategic level involves in finding the optimal technology adoption plan while the tactical level figures out the optimal capacity level to meet the market demands over the planning horizon. The problem is modeled by stochastic dynamic programming (SDP) considering demand and technology uncertainties. The reward of the SDP model is modeled by deterministic/stochastic mixed integer programming (MIP). The objective is to maximize expected profit over the planning horizon. For solution approach, a backward induction algorithm is employed to solve the SDP model while the corresponding rewards are solved by proposed metaheuristic algorithms. Further, a parallel computing technique is utilized to relax the computational burden of the SDP model. Through experiments, we demonstrate the effectiveness of the proposed model, highlight the importance of considering uncertain factors, and provide useful managerial insights.

    Table of Contents 摘要 i Abstract ii Acknowledgement iii Table of Contents iv List of Tables xiii List of Figures ix CHAPTER 1 INTRODUCTION 1 1.1. Research motivations 1 1.1.1. Rapid technology development 1 1.1.2. Simultaneity of new technology adoption and capacity allocation 2 1.2. Research objective 3 1.3. Research contribution 3 1.4. Research limitation 4 1.5. Research framework 4 CHAPTER 2 LITERATURE REVIEW 6 2.1 Approaches to technology replacement 6 2.2 Approaches to technology portfolio selection 8 2.3 Approaches to capacity planning with new technology adoption 8 2.4 Solution approaches for MIP 9 2.4.1 Metaheuristic algorithms for MIP problems 9 2.4.2 Hybridizations of DE-PSO and PS-GA 10 2.5 Summary 12 CHAPTER 3 METHODOLOGY 13 3.1 Research issues 13 3.2 Modelling approaches 13 3.2.1 Uncertainties 13 3.2.2 Production strategy 14 3.3 Solution approaches 14 3.4 Dealing with the computational burden of the SDP model 15 3.5 Resolving computational burden of the SDP model 16 3.6 Model assessment and sensitivity analyses 16 CHAPTER 4 CAPACITY PLANNING WITH TECHNOLOGY REPLACEMENT 18 4.1 Problem formulation 18 4.1.1 Modeling of technology economic life 19 4.1.2 Modeling of the resource investment and allocation 21 4.2 Solution method 24 4.2.1 PS-GA 25 4.2.2 Fitness function and chromosome design for PS-GA 27 4.3 Model assessment and sensitivity analysis 28 4.3.1 Model complexity analysis 28 4.3.2 Sensitivity analysis to PS-GA operation parameters 29 4.3.3 Sensitivity analysis regarding technology replacement related parameters 33 4.3.4 Method evaluation 36 4.4 Summary 37 CHAPTER 5 CAPACITY PLANNING WITH TECHNOLOGY PORTFOLIO SELECTION 38 5.1 Model development 38 5.1.1 Modeling on technology portfolio 38 5.1.2 Modeling of the reward function 41 5.2 Solution approach 44 5.3 DE-PSO algorithm 45 5.3.1 DE-PSO algorithm development 45 5.3.2 Solution vector presentation and infeasible solution repairing mechanism 46 5.4 Experiments 49 5.4.1 Sensitivity analysis for DE-PSO parameters 49 5.4.2 Performance evaluation for DE-PSO 56 5.4.3 Impact analysis owning to uncertain demands 56 5.4.4 Model evaluation 56 5.4.5 A numerical example 58 5.5 Summary 59 CHAPTER 6 CONCLUSIONS AND FUTURE RESEARCH 61 6.1 Conclusions and contributions 61 6.1.1 Conclusions 61 6.1.2 Contributions 61 6.2 Future research 62 REFERENCES 64 Appendix A: Input data for experiments in Chapter 4 73 Appendix B: Input data for experiments in Chapter 5 77 Appendix C: Author’s Resume 84

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