隨著需求的快速變化和智慧製造的彈性決策，TFT-LCD廠面臨巨大的客戶群及多樣的產品別。因此，在維持產品品質情況下如何提升生產力成為一個重要的議題。由於黃光區為瓶頸站點，本研究以黃光區的排程為目標，同時考慮產品的來到時間。為了解決來到時間的不確定性，本研究發展了兩階段解碼之遺傳演算法(TDGA)並結合滾動式的排程，以解決在黃光區的動態排程問題。透過染色體解碼設計，TDGA也可避免重工與產能不均的問題。為了驗證其效度，本研究透過台灣某實際的TFT-LCD 廠資料作為實證研究，分析TDGA與有左移機制之遺傳演算法的表現。實驗結果顯示，TDGA可以縮短在工單之間的閒置時間以提升機台利用率到99%以上，進而獲得較高品質的解。因此在所有情境之下，TDGA的表現皆比左移機制之遺傳演算法來得好。

Due to the rapid change of the market and decision flexibilities of intelligent manufacturing, TFT-LCD industries are facing the challenges of a huge number of customers and different kinds of products. Therefore, it is important to enhance productivity as well as remain product quality. Because photolithography stage is the bottleneck, this study focuses on photolithography scheduling which considers job arrivals. To deal with the uncertainty of arrival time, this study develops Two-phase Decoding Genetic Algorithm (TDGA) combined with rolling strategy for dynamic scheduling in photolithography stage under complex restrictions. TDGA can also avoid the reworked problem and load unbalancing through the design of chromosome. For validation, TDGA is also compared with GA which has the left-shift mechanism through empirical data from a leading TFT-LCD industry in Taiwan. The experimental result shows that TDGA can shorten the idle time between jobs. It can obtain a high quality solution with 99% machine utilization. Thus, TDGA performances better than GA in all scenarios.

Table Contents ii
Figure Contents iii
Chapter 1 Introduction 1
1.1 Background 1
1.2 Objective 2
1.3 Structure of the study 2
Chapter 2 Literature Review 4
2.1 The photolithography process 4
2.2 Scheduling problems in the TFT-LCD 6
2.3 Genetic algorithm 10
2.4 Dynamic scheduling 13
Chapter 3 Model Construction 16
3.1 Structure of the photolithography scheduling problem 17
3.2 Mathematical model 19
3.3 Numerical example 22
Chapter 4 Genetic Algorithm of TFT-LCD 23
4.1 Structure of the TDGA 23
4.2 Rolling mechanism for dynamic scheduling 32
Chapter 5 Empirical Study 35
5.1 Background and problem description 35
5.2 Parameter and scenario setting 35
5.3 Performance analysis 38
Chapter 6 Conclusion 49
6.1 Contribution 49
6.2 Future research 49
References 50

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