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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 李家岩 | zh_TW |
| dc.contributor.advisor | Chia-Yen Lee | en |
| dc.contributor.author | 盧冠均 | zh_TW |
| dc.contributor.author | Kuan-Chun Lu | en |
| dc.date.accessioned | 2024-11-28T16:20:14Z | - |
| dc.date.available | 2024-11-29 | - |
| dc.date.copyright | 2024-11-28 | - |
| dc.date.issued | 2024 | - |
| dc.date.submitted | 2024-09-05 | - |
| dc.identifier.citation | Chen, J. C., Chen, T.-L., Pratama, B. R., & Tu, Q.-F. (2016). Capacity planning in thin film transistor – Liquid crystal display cell process. Journal of Manufacturing Systems, 39, 63-78. https://doi.org/https://doi.org/10.1016/j.jmsy.2016.03.003
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/96236 | - |
| dc.description.abstract | 隨著薄膜電晶體液晶顯示器（TFT-LCD）需求量的增加，對於生產排程的效率要求也隨之提升。本研究聚焦於TFT-LCD 組立製程，並將此製程歸納成為動態彈性零工式排程問題（DFJSS）。此排程問題主要解決薄膜電晶體（TFT）陣列基板與彩色濾光片（CF）基板之間的生產平衡，稱為T/C平衡。為了貼近真實生產環境，我們考慮了工作插單與生產時間之不確定性。本研究提出一個深度強化學習（DRL）框架以同時處理多個生產排程目標，包含總加權延遲時間、總完工時間、過度等候時間和T/C平衡。我們基於實證資料進行數值實驗，以評估本研究所提出的深度強化學習框架，並將其排程表現與傳統的最佳化模型和基因演算法進行比較。實驗結果顯示，相較於基準模型，本研究的方法在目標值上提升了約30%，並且每次處理工作插單只需約莫5秒鐘。這證實了在動態的生產情境下，此框架具有優良的效力與適用性，同時在面對生產環境的不確定性時，也能夠保持一定程度的穩健性。 | zh_TW |
| dc.description.abstract | The rising demand for Thin-Film Transistor Liquid Crystal Display (TFT-LCD) has amplified the need for efficient manufacturing process. In this study, we focus on TFT-LCD cell process, generalized as a dynamic flexible job shop scheduling problem (DFJSS). The scheduling problem tackles the production balance between Thin-Film Transistor (TFT) array substrate and Color Filter (CF) substrate, known as T/C balance. To align with real-world manufacturing environment, new job arrival and uncertain processing time are considered. A deep reinforcement learning (DRL) framework is proposed to address multiple objectives simultaneously, including total weighted tardiness, makespan, over-queued time and T/C balance. To validate the proposed DRL framework, numerical experiments based on empirical data are conducted to compare its performance with traditional optimization-based models and genetic algorithm. The result shows proposed framework achieves about 30% improvement in objective value compared to benchmark models, while handling each new job arrival around 5 seconds. This demonstrates the effectiveness and applicability in dynamic manufacturing scenario, while maintaining a certain degree of robustness against uncertainty. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2024-11-28T16:20:13Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2024-11-28T16:20:14Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | 誌謝 i
摘要 iii Abstract iv Table of Contents v List of Figures viii List of Tables x Chapter 1 Introduction 1 1.1 Background and Motivation 1 1.2 Research Objective 6 1.3 Thesis Architecture 8 Chapter 2 Literature Review 9 2.1 Dynamic Flexible Job Shop Scheduling Problem 9 2.2 Scheduling with Uncertainty 13 2.3 TFT-LCD Scheduling 16 Chapter 3 Methodology 20 3.1 Research Framework 20 3.2 Problem Description 21 3.3 Mathematical Model 27 3.4 Deep Reinforcement Learning 34 3.4.1 Algorithm 35 3.4.2 Network Structure 38 3.4.3 Definition of State Features 39 3.4.4 Definition of Action 41 3.4.5 Definition of Reward 45 3.4.6 Training Workflow 50 3.4.7 Scheduling Procedure 52 Chapter 4 Numerical Study 57 4.1 Data Simulation 58 4.2 Benchmark Model 61 4.2.1 Optimization-Based Model 61 4.2.2 Genetic Algorithm 64 4.3 Training Process of DRL 66 4.4 Sensitivity Analysis 70 4.5 Solution Value Analysis 72 4.5.1 Overall Performance 72 4.5.2 Objective Performance 75 4.6 Execution Time Analysis 77 4.6.1 Solving and Training Time Efficiency 77 4.6.2 Reschedule Efficiency 80 Chapter 5 Conclusion and Future Work 82 5.1 Conclusion 82 5.2 Future Work 84 Bibliography 86 | - |
| dc.language.iso | en | - |
| dc.subject | T/C 平衡 | zh_TW |
| dc.subject | 深度強化學習 | zh_TW |
| dc.subject | 生產時間之不確定性 | zh_TW |
| dc.subject | 工作插單 | zh_TW |
| dc.subject | 動態彈性零工式排程問題 | zh_TW |
| dc.subject | TFT-LCD 組立製程 | zh_TW |
| dc.subject | New job arrival | en |
| dc.subject | Dynamic flexible job shop scheduling problem | en |
| dc.subject | T/C balance | en |
| dc.subject | TFT-LCD cell process | en |
| dc.subject | Deep reinforcement learning | en |
| dc.subject | Uncertain processing time | en |
| dc.title | 深度強化學習處理 T/C 平衡生產排程於面板製造商組立製程 | zh_TW |
| dc.title | Deep Reinforcement Learning for T/C Balance of TFT-LCD Cell Process Scheduling in TFT-LCD Manufacturer | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 113-1 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.oralexamcommittee | 洪子晏;孔令傑;黃奎隆 | zh_TW |
| dc.contributor.oralexamcommittee | Tzu-Yen Hong;Ling-Chieh Kung;Kwei-Long Huang | en |
| dc.subject.keyword | TFT-LCD 組立製程,T/C 平衡,動態彈性零工式排程問題,工作插單,生產時間之不確定性,深度強化學習, | zh_TW |
| dc.subject.keyword | TFT-LCD cell process,T/C balance,Dynamic flexible job shop scheduling problem,New job arrival,Uncertain processing time,Deep reinforcement learning, | en |
| dc.relation.page | 90 | - |
| dc.identifier.doi | 10.6342/NTU202404333 | - |
| dc.rights.note | 未授權 | - |
| dc.date.accepted | 2024-09-05 | - |
| dc.contributor.author-college | 管理學院 | - |
| dc.contributor.author-dept | 資訊管理學系 | - |
| 顯示於系所單位： | 資訊管理學系 | |
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