本研究針對半導體製造業中晶圓測試排程的關鍵挑戰，提出了一種創新的混合多目標基因演算法結合派工法則的方法。隨著積體電路日益複雜，半導體業在晶圓測試方面面臨著愈發嚴峻的挑戰，因此亟需能夠處理多重目標和實際限制的高效排程解決方案。

我們的方法巧妙地將傳統派工法則與先進的基因演算法框架融合，打造出一個強健而靈活的排程系統。這種混合方法旨在同時優化多個關鍵績效指標，包括最小化延遲工作和減少載板與探針卡的更換次數。該演算法還考慮了實際限制條件，如機器可用性、載板和探針卡的數量限制以及設置時間，確保其在實際製造環境中的適用性。

為驗證我們提出方法的有效性，我們利用模擬數據集和半導體製造設施的實際情境進行了全面評估。結果顯示，無論是解決方案品質還是對動態生產環境的適應性，我們的方法都始終優於現有的排程方法。這種卓越表現源於該演算法能夠在處理複雜限制條件的同時平衡多重目標，這一能力在現代半導體製造中愈發重要。

本研究的意義不僅限於即時的運營改進。通過提升晶圓測試過程的效率，我們的方法有助於縮短半導體製造中的整體更換時間並降低成本。此外，本研究獲得的見解對於解決各行業類似的多目標排程問題具有廣泛影響，可能帶來製造效率和資源利用的全面提升。

總而言之，本論文提出了一種解決半導體業關鍵需求的晶圓測試排程新方法。結合派工法則的多目標基因演算法為優化複雜製造流程提供了強大工具，不僅推動了半導體生產的進步，還為解決其他工業部門類似挑戰提供了可靠框架。

This research addresses the critical challenge of wafer test scheduling in semiconductor manufacturing, proposing an innovative hybrid multi-objective genetic algorithm combined with dispatching rules. The semiconductor industry faces increasing complexity in wafer testing due to the growing sophistication of integrated circuits, necessitating efficient scheduling solutions that can handle multiple objectives and real-world constraints.

Our approach uniquely integrates traditional dispatching rules with an advanced genetic algorithm framework to create a robust and flexible scheduling system. This hybrid method aims to optimize several key performance indicators simultaneously, including minimizing tardy jobs and reducing load board and probe card changeovers. The algorithm also accounts for practical constraints such as machine availability, limitations on load boards and probe cards, and setup times, ensuring its applicability in real-world manufacturing environments.

To validate the effectiveness of our proposed method, we conducted extensive evaluations using both simulated datasets and actual scenarios from a semiconductor manufacturing facility. The results demonstrate that our approach consistently outperforms existing scheduling methods in terms of solution quality and adaptability to dynamic production environments. This superior performance is attributed to the algorithm's ability to balance multiple objectives while adhering to complex constraints, a capability that is increasingly crucial in modern semiconductor manufacturing.

The significance of this research extends beyond immediate operational improvements. By enhancing the efficiency of the wafer testing process, our method contributes to reducing overall changeover time and costs in semiconductor manufacturing. Furthermore, the insights gained from this study have broader implications for addressing similar multi-objective scheduling problems across various industries, potentially leading to widespread improvements in manufacturing efficiency and resource utilization.

In conclusion, this paper presents a novel approach to wafer test scheduling that addresses a critical need in the semiconductor industry. The proposed multi-objective genetic algorithm, combined with dispatching rules, offers a powerful tool for optimizing complex manufacturing processes, ultimately contributing to advancements in semiconductor production and providing a framework for solving similar challenges in other industrial sectors.

M. S. Akturk and T. Ilhan. Single cnc machine scheduling with controllable processing times to minimize total weighted tardiness. Computers & Operations Research, 38(4):771–781, 2011.
M. Aruldoss, T. M. Lakshmi, and V. P. Venkatesan. A survey on multi criteria decision making methods and its applications. American Journal of Information Systems, 1(1):31–43, 2013.
A. Bagheri, M. Zandieh, I. Mahdavi, and M. Yazdani. An artificial immune algorithm for the flexible job-shop scheduling problem. Future Generation Computer Systems, 26(4):533–541, 2010.
J.-Y. Bang and Y.-D. Kim. Scheduling algorithms for a semiconductor probing facility. Computers & Operations Research, 38(3):666–673, 2011.
T. C. Bora, V. C. Mariani, and L. dos Santos Coelho. Multi-objective optimization of the environmentaleconomic dispatch with reinforcement learning based on non-dominated sorting genetic algorithm. Applied Thermal Engineering, 146:688–700, 2019.
Z. Cao, C. Lin, M. Zhou, and R. Huang. Scheduling semiconductor testing facility by using cuckoo search algorithm with reinforcement learning and surrogate modeling. IEEE Transactions on Automation Science and Engineering, 16(2):825–837, 2018.
A. Che, X. Wu, J. Peng, and P. Yan. Energy-efficient bi-objective single-machine scheduling with power-down mechanism. Computers & Operations Research, 85:172–183, 2017.
C.-A. Chen, H.-K. Wang, and C.-L. Wu. A hybrid multi-objective genetic algorithm combined with dispatching rule for wafer test scheduling. In Proceedings of the Industrial Engineering and Management International Conference on Smart Manufacturing, Industrial and Logistics Engineering and Asian Conference of Management Science and Applications, pages 81–88. Springer, 2024.
R. Chen, B. Yang, S. Li, and S. Wang. A self-learning genetic algorithm based on reinforcement learning for flexible job-shop scheduling problem. Computers & industrial engineering, 149:106778, 2020.
T.-C. Chiang, Y. Shen, and L.-C. Fu. A new paradigm for rule-based scheduling in the wafer probe centre. International Journal of Production Research, 46(15):4111–4133, 2008.
C.-F. Chien and Y.-B. Lan. Agent-based approach integrating deep reinforcement learning and hybrid genetic algorithm for dynamic scheduling for industry 3.5 smart production. Computers & Industrial Engineering, 162:107782, 2021.
K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan. A fast and elitist multiobjective genetic algorithm: Nsga-ii. IEEE transactions on evolutionary computation, 6(2):182–197, 2002.
D. Doleschal, J. Lange, G. Weigert, and A. Klemmt. Improving flow line scheduling by upstream mixed integer resource allocation in a wafer test facility. In Proceedings of the 2012 Winter Simulation Conference (WSC), pages 1–12. IEEE, 2012.
K. Ellis, Y. Lu, and E. Bish. Scheduling of wafer test processes in semiconductor manufacturing. International Journal of Production Research, 42(2):215–242, 2004.
J. Fan, C. Zhang, Q. Liu, W. Shen, and L. Gao. An improved genetic algorithm for flexible job shop scheduling problem considering reconfigurable machine tools with limited auxiliary modules. Journal of Manufacturing Systems, 62:650–667, 2022.
P. Fattahi, M. Saidi Mehrabad, and F. Jolai. Mathematical modeling and heuristic approaches to flexible job shop scheduling problems. Journal of intelligent manufacturing, 18:331–342, 2007.
J. Gao, L. Sun, and M. Gen. A hybrid genetic and variable neighborhood descent algorithm for flexible job shop scheduling problems. Computers & Operations Research, 35(9):2892–2907, 2008.
K. Z. Gao, P. N. Suganthan, T. J. Chua, C. S. Chong, T. X. Cai, and Q. K. Pan. A two-stage artificial bee colony algorithm scheduling flexible job-shop scheduling problem with new job insertion. Expert systems with applications, 42(21):7652–7663, 2015.
A. M. Ham and E. Cakici. Flexible job shop scheduling problem with parallel batch processing machines: Mip and cp approaches. Computers & Industrial Engineering, 102:160–165, 2016.
R. Haupt. A survey of priority rule-based scheduling. Operations-Research-Spektrum, 11(1):3–16, 1989.
J. H. Holland. Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence. MIT press, 1992.
E. Köksal Ahmed, Z. Li, B. Veeravalli, and S. Ren. Reinforcement learning-enabled genetic algorithm for school bus scheduling. Journal of Intelligent Transportation Systems, 26(3):269–283, 2022.
D. Lei, Y. Zheng, and X. Guo. A shuffled frog-leaping algorithm for flexible job shop scheduling with the consideration of energy consumption. International Journal of Production Research, 55(11):3126–3140, 2017.
K. Lei, P. Guo, W. Zhao, Y. Wang, L. Qian, X. Meng, and L. Tang. A multi-action deep reinforcement learning framework for flexible job-shop scheduling problem. Expert Systems with Applications, 205:117796, 2022.
J. Lin, Y.-Y. Li, and H.-B. Song. Semiconductor final testing scheduling using q-learning based hyper-heuristic. Expert Systems with Applications, 187:115978, 2022.
S.-W. Lin, Z.-J. Lee, K.-C. Ying, and R.-H. Lin. Meta-heuristic algorithms for wafer sorting scheduling problems. Journal of the Operational Research Society, 62(1):165–174, 2011.
H. Luo, G. Q. Huang, Y. Zhang, Q. Dai, and X. Chen. Two-stage hybrid batching flowshop scheduling with blocking and machine availability constraints using genetic algorithm. Robotics and Computer-Integrated Manufacturing, 25(6):962–971, 2009.
S. Luo. Dynamic scheduling for flexible job shop with new job insertions by deep reinforcement learning. Applied Soft Computing, 91:106208, 2020.
L. Meng, C. Zhang, X. Shao, and Y. Ren. Milp models for energy-aware flexible job shop scheduling problem. Journal of cleaner production, 210:710–723, 2019a.
L. Meng, C. Zhang, B. Zhang, and Y. Ren. Mathematical modeling and optimization of energy-conscious flexible job shop scheduling problem with worker flexibility. IEEE Access, 7:68043–68059, 2019b.
L. Meng, C. Zhang, Y. Ren, B. Zhang, and C. Lv. Mixed-integer linear programming and constraint programming formulations for solving distributed flexible job shop scheduling problem. Computers & industrial engineering, 142:106347, 2020.
P. Michael. Scheduling. theory, algorithms and systems. ISBN0-13-706757-7, 1995.
M. Nouiri, A. Bekrar, A. Jemai, S. Niar, and A. C. Ammari. An effective and distributed particle swarm optimization algorithm for flexible job-shop scheduling problem. Journal of Intelligent Manufacturing, 29: 603–615, 2018.
C. Özgüven, L. Özbakır, and Y. Yavuz. Mathematical models for job-shop scheduling problems with routing and process plan flexibility. Applied Mathematical Modelling, 34(6):1539–1548, 2010.
F. Pezzella, G. Morganti, and G. Ciaschetti. A genetic algorithm for the flexible job-shop scheduling problem. Computers & operations research, 35(10):3202–3212, 2008.
S. H. A. Rahmati and M. Zandieh. A new biogeography-based optimization (bbo) algorithm for the flexible job shop scheduling problem. The International Journal of Advanced Manufacturing Technology, 58:1115–1129, 2012.
C. Rajendran and O. Holthaus. A comparative study of dispatching rules in dynamic flowshops and jobshops. European journal of operational research, 116(1):156–170, 1999.
B. Roy. The outranking approach and the foundations of electre methods. Theory and decision, 31:49–73, 1991.
R. Ruiz and C. Maroto. A genetic algorithm for hybrid flowshops with sequence dependent setup times and machine eligibility. European journal of operational research, 169(3):781–800, 2006.
T. L. Saaty. Decision making with the analytic hierarchy process. International journal of services sciences, 1 (1):83–98, 2008.
Y. Song, L. Wei, Q. Yang, J. Wu, L. Xing, and Y. Chen. Rl-ga: A reinforcement learning-based genetic algorithm for electromagnetic detection satellite scheduling problem. Swarm and Evolutionary Computation, 77: 101236, 2023.
G.-H. Tzeng and J.-J. Huang. Multiple attribute decision making: methods and applications. CRC press, 2011.
R. Uzsoy, C.-Y. Lee, and L. A. Martin-Vega. A review of production planning and scheduling models in the semiconductor industry part i: system characteristics, performance evaluation and production planning. IIE transactions, 24(4):47–60, 1992.
H.-K. Wang, C.-F. Chien, and M. Gen. An algorithm of multi-subpopulation parameters with hybrid estimation of distribution for semiconductor scheduling with constrained waiting time. IEEE Transactions on Semiconductor Manufacturing, 28(3):353–366, 2015.
W. Xia and Z. Wu. An effective hybrid optimization approach for multi-objective flexible job-shop scheduling problems. Computers & industrial engineering, 48(2):409–425, 2005.
M. Yazdani, M. Amiri, and M. Zandieh. Flexible job-shop scheduling with parallel variable neighborhood search algorithm. Expert Systems with Applications, 37(1):678–687, 2010.
K.-C. Ying. Scheduling identical wafer sorting parallel machines with sequence-dependent setup times using an iterated greedy heuristic. International Journal of Production Research, 50(10):2710–2719, 2012.
K.-C. Ying and S.-W. Lin. Efficient wafer sorting scheduling using a hybrid artificial immune system. Journal of the Operational Research Society, 65(2):169–179, 2014.
H. J. Yoon and J. Chae. Simulation study for semiconductor manufacturing system: dispatching policies for a wafer test facility. Sustainability, 11(4):1119, 2019.
G. Zhang, X. Shao, P. Li, and L. Gao. An effective hybrid particle swarm optimization algorithm for multiobjective flexible job-shop scheduling problem. Computers & Industrial Engineering, 56(4):1309–1318, 2009.
G. Zhang, L. Gao, and Y. Shi. An effective genetic algorithm for the flexible job-shop scheduling problem. Expert Systems with Applications, 38(4):3563–3573, 2011.
L. Zhao, C.-F. Chien, and M. Gen. A bi-objective genetic algorithm for intelligent rehabilitation scheduling considering therapy precedence constraints. Journal of Intelligent Manufacturing, 29:973–988, 2018.