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研究生： 曾國杭
Tseng, Kuo-Hang
論文名稱： 應用機器學習於半導體製造業物料需求計畫之個案研究
A Case Study on the Application of Machine Learning in Material Requirements Planning for the Semiconductor Manufacturing Industry
指導教授： 呂執中
Lyu, JrJung
學位類別： 碩士
Master
系所名稱： 管理學院 - 工業與資訊管理學系
Department of Industrial and Information Management
論文出版年： 2025
畢業學年度： 113
語文別： 中文
論文頁數： 68
中文關鍵詞： 需求預測 、機器學習 、強化學習 、馬可夫決策過程
外文關鍵詞： Demand Forecasting, Machine Learning, Reinforcement Learning, Markov Decision Process
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    • 摘要 i 目錄 vi 表目錄 ix 圖目錄 x 第一章 緒論 1 1.1 研究背景與動機 2 1.1.1 半導體產業發展與重要性 2 1.1.2 半導體產業營運特性與問題 2 1.2 研究目的 4 1.3 研究範圍與限制 5 1.3.1 研究範圍 5 1.3.2 研究限制 7 1.4 研究架構與流程 8 第二章 文獻探討 10 2.1 傳統物料需求預測方法(MRP) 10 2.1.1 MRP 基本原理與流程 10 2.1.2 傳統MRP 的限制與挑戰 11 2.2 物料預測重要性 11 2.2.1 預測誤差對供應鏈之影響 11 2.2.2 傳統預測方法的應用與限制 12 2.2.3 引入機器學習以提升預測效能 12 2.3 風險管控方法於供應鏈與預測領域之應用 13 2.4 機器學習於物料需求預測之理論與現況. 14 2.4.1 機器學習方法分類與應用說明 14 2.4.2 人工神經網路（ANN）、遞迴神經網路（RNN）與支持向量回歸（SVR） 於需求預測之應用 16 2.4.3 強化學習於供應鏈與物料預測之應用 18 2.4.4 深度強化學習（Deep Reinforcement Learning, DRL）與深度Q 網路（Deep Q-Network, DQN）之應用 20 2.4.5 馬可夫決策過程Markov Decision Process, MDP 22 2.4.6 MDP 的四個核心元素 22 2.4.7 MDP 在供應鏈與預測應用的價值 22 2.4.8 MDP 與強化學習、DQN 的關係 23 第三章 研究方法 24 3.1 問題定義與研究架構 24 3.2 產品需求特徵分類 26 3.3 人工神經網路預測模型介紹與架構 27 3.3.1 模型架構 27 3.3.2 模型運算函數（分層說明） 27 3.3.3 訓練與損失函數 28 3.4 遞迴神經網路預測模型介紹與架構 29 3.5 支持向量回歸預測模型介紹與架構 31 3.6 深度強化學習預測模型介紹與架構 33 3.6.1 MDP 框架下的預測模型 33 3.6.2 DQN 架構應用於需求預測 34 第四章 研究結果與分析 37 4.1 個案公司介紹 37 4.2 實驗步驟 37 4.3 資料前置作業 39 4.3.1 共同資料處理程序 40 4.3.2 ANN 與SVR 模型之前置作業 41 4.3.3 RNN 模型之前置作業 41 4.3.4 DQN 模型之前置作業 42 4.4 評估指標 43 4.5 各預測模型結果分析 45 4.6 研究結果 48 第五章 結論與建議 50 5.1 結論 50 5.1.1 研究意涵 50 5.2 建議與未來研究方向 51 5.3 研究侷限性 51 參考文獻 53

    1. Carbonneau, R., Laframboise, K., & Vahidov, R. (2008). Application of machine learning techniques for supply chain demand forecasting. European Journal of Operational Research, 184(3), 1140–1154.
    2. Choi, T. M., Wallace, S. W., & Wang, Y. (2018). Big data analytics in operations management. Production and Operations Management, 27(10), 1868–1889.
    3. Chopra, S., & Meindl, P. (2016). Supply Chain Management: Strategy, Planning, and Operation (6th ed.). Pearson. ISBN-13: 978-0133800203
    4. Christopher, M., & Peck, H. (2004). Building the resilient supply chain. The International Journal of Logistics Management, 15(2), 1–14.
    5. Drucker, H., Burges, C. J. C., Kaufman, L., Smola, A., & Vapnik, V. (1997). Support Vector Regression Machines. Advances in Neural Information Processing Systems, 9, 155–161.
    6. Gao, Y., & Wang, C. (2024). Research on Supply Chain Optimization and Management Based on Deep Reinforcement Learning. ResearchGate.
    7. Giannoccaro, I., & Pontrandolfo, P. (2002). Inventory management in supply chains: A reinforcement learning approach. International Journal of Production Economics, 78(2), 153–161.
    8. Hewamalage, H., Bergmeir, C., & Bandara, K. (2021). Recurrent neural networks for time series forecasting: Current status and future directions. International Journal of Forecasting, 37(1), 388–427.
    9. Hyndman, R. J., & Athanasopoulos, G. (2018). Forecasting: Principles and Practice. OTexts. ISBN-13: 978-0987507136
    10. Ivanov, D., Dolgui, A., & Sokolov, B. (2019). The impact of digital technology and Industry 4.0 on the ripple effect and supply chain risk analytics. International Journal of Production Research, 57(3), 829–846.
    11. LeCun, Y., Bengio, Y., & Hinton, G. (2015).Deep learning. Nature, 521(7553), 436–444.
    12. Lee, H. L., & Billington, C. (1995). The evolution of supply-chain-management models and practice at Hewlett-Packard. Interfaces, 25(5), 42–63.
    13. Lee, Y.-H., & Lee, S. (2022). Deep reinforcement learning based scheduling within production plan in semiconductor fabrication. Expert Systems with Applications, 191, 116222.
    14. Makridakis, S., Spiliotis, E., & Assimakopoulos, V. (2018). Statistical and machine learning forecasting methods: Concerns and ways forward. PLOS ONE, 13(3), e0194889.
    15. Mentzer, J. T., & Moon, M. A. (2004). Sales Forecasting Management: A Demand Management Approach. SAGE Publications. ISBN-13: 978-1412905718
    16. Mnih, V., Kavukcuoglu, K., Silver, D., et al. (2015). Human-level control through deep reinforcement learning. Nature, 518(7540), 529–533. https://doi.org/10.1038/nature14236
    17. Orlicky, J. (1975). Material requirements planning: The new way of life in production and inventory management. McGraw-Hill. ISBN-10: 0070477086
    18. Oroojlooyjadid, A., Nazari, M., Snyder, L., & Takáč, M. (2017). A Deep Q-Network for the Beer Game: A Deep Reinforcement Learning algorithm to Solve Inventory Optimization Problems. arXiv preprint arXiv:1708.05924.
    19. Pai, P.-F., & Lin, C.-S. (2005). A hybrid ARIMA and support vector machines model in stock price forecasting. Omega, 33(6), 497–505.
    20. Puterman, M. L. (1994). Markov decision processes: Discrete stochastic dynamic programming. John Wiley & Sons. ISBN-10: 0471619779
    21. SEMI. (2023). Materials Market Data Report 2023. SEMI Industry Research & Statistics.
    22. Sheffi, Y., & Rice Jr., J. B. (2005). A supply chain view of the resilient enterprise. MIT Sloan Management Review, 47(1), 41–48.
    23. Simchi-Levi, D., Kaminsky, P., & Simchi-Levi, E. (2008). Designing and managing the supply chain: Concepts, strategies, and case studies (3rd ed.). McGraw-Hill Education. ISBN-13: 978-0073403366
    24. Stranieri, F., & Stella, F. (2022). Comparing deep reinforcement learning algorithms in two-echelon supply chains. arXiv preprint arXiv:2204.09603
    25. Sutton, R. S., & Barto, A. G. (2018).Reinforcement learning: An introduction (2nd ed.). MIT Press. ISBN-13: 978-0262039246
    26. Tang, C. S. (2006). Robust strategies for mitigating supply chain disruptions. International Journal of Logistics, 9(1), 33–45.
    27. Tian, R., & Wang, H. (2023). IACPPO: A deep reinforcement learning-based model for warehouse inventory replenishment. SSRN Electronic Journal.
    28. Trkman, P., & McCormack, K. (2009). Supply chain risk in turbulent environments—A conceptual model for managing supply chain network risk. International Journal of Production Economics, 119(2), 247–258.
    29. Vollmann, T. E., Berry, W. L., Whybark, D. C., & Jacobs, F. R. (2005). Manufacturing Planning and Control for Supply Chain Management. McGraw-Hill. ISBN-10: 0071750312
    30. Waller, M. A., & Fawcett, S. E. (2013). Data science, predictive analytics, and big data: a revolution that will transform supply chain design and management. Journal of Business Logistics, 34(2), 77–84.
    31. Zhang, G. (2003). Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing, 50, 159–175.
    32. Zhao, X., Xie, J., & Zhang, W. J. (2002). The impact of information sharing and forecasting on supply chain performance. International Journal of Production Economics, 71(2), 315–328.

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