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Sustainable scheduling of TFT-LCD cell production: A hybrid dispatching rule and two-phase genetic algorithm

Author

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  • Wang, Hung-Kai
  • Chou, Che-Wei
  • Wang, Chien-Han
  • Ho, Li-An

Abstract

TFT-LCD manufacturing process involves Array, Color Filter (CF), Cell, and Module. The Cell process is pivotal in the TFT-LCD production supply chain; it bridges the front-end (Array and CF) and back-end (Module), maintaining a balance of production capacities. Supply from the front-end is transmitted to the back-end, ensuring prompt responsiveness to customer demands. As awareness of environmental conservation grows, implementing sustainable development in the TFT-LCD manufacturing industry has become crucial. Specifically, reducing the machine setup frequency in TFT-LCD scheduling decreases energy consumption during machine operation and wait times and prolongs equipment lifespan by minimizing wear from repeated shutdowns. To tackle the intricate scheduling challenges within TFT-LCD production, this study proposes an intelligent, sustainable scheduling approach that employs a rolling dispatching and line balancing mechanism and a two-phase genetic algorithm (HDTPGA) for Cell scheduling. The goal is to minimize makespan considering product diversity, production constraints, work-in-process balancing, and machine setup frequency. This holistic approach promotes energy-efficient and sustainable manufacturing practices. According to the empirical study, the proposed HDTPGA reduces the makespan by approximately 10% compared to the baseline. Additionally, it reduces the average daily setup frequency of bottleneck stages (ODF and CCT) in the Cell process by approximately 68.25% and 66.25%, respectively. The main contribution of this paper is to propose an intelligent scheduling algorithm to optimize Cell production scheduling in practice. The HDTPGA achieves equilibrium between front-end and back-end production, utilizing an efficient line change mechanism to reduce energy consumption. The enhancement in efficiency not only restricts material waste but also reduces the environmental impact, enabling manufacturers to adopt green production practices and promoting a transition of the entire panel industry toward sustainability.

Suggested Citation

  • Wang, Hung-Kai & Chou, Che-Wei & Wang, Chien-Han & Ho, Li-An, 2024. "Sustainable scheduling of TFT-LCD cell production: A hybrid dispatching rule and two-phase genetic algorithm," International Journal of Production Economics, Elsevier, vol. 278(C).
    • Handle: RePEc:eee:proeco:v:278:y:2024:i:c:s092552732400269x
    DOI: 10.1016/j.ijpe.2024.109412
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    References listed on IDEAS

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    1. Tzu-Li Chen & Chen-Yang Cheng & Yi-Han Chou, 2020. "Multi-objective genetic algorithm for energy-efficient hybrid flow shop scheduling with lot streaming," Annals of Operations Research, Springer, vol. 290(1), pages 813-836, July.
    2. Youngbum Hur & Jonathan F. Bard & Rodolfo Chacon, 2019. "Hierarchy machine set-up for multi-pass lot scheduling at semiconductor assembly and test facilities," International Journal of Production Research, Taylor & Francis Journals, vol. 57(14), pages 4351-4370, July.
    3. Feng, Yanling & Li, Guo & Sethi, Suresh P., 2018. "A three-layer chromosome genetic algorithm for multi-cell scheduling with flexible routes and machine sharing," International Journal of Production Economics, Elsevier, vol. 196(C), pages 269-283.
    4. Lei Shi & Gang Guo & Xiaohui Song, 2021. "Multi-agent based dynamic scheduling optimisation of the sustainable hybrid flow shop in a ubiquitous environment," International Journal of Production Research, Taylor & Francis Journals, vol. 59(2), pages 576-597, January.
    5. Kai Li & Liping Xu & Han Zhang & Jianfu Chen, 2024. "A genetic algorithm for fuzzy identical parallel machine scheduling of minimising total weighted tardiness under resource constraint," International Journal of Production Research, Taylor & Francis Journals, vol. 62(21), pages 7619-7643, November.
    6. Liu, Ying & Dong, Haibo & Lohse, Niels & Petrovic, Sanja, 2016. "A multi-objective genetic algorithm for optimisation of energy consumption and shop floor production performance," International Journal of Production Economics, Elsevier, vol. 179(C), pages 259-272.
    7. James C. Chen & Tzu-Li Chen & Bayu Rezki Pratama & Qian-Fang Tu, 2018. "Capacity planning with ant colony optimization for TFT-LCD array manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 29(8), pages 1695-1713, December.
    8. Tzu-Yen Hong & Chen-Fu Chien, 2020. "A simulation-based dynamic scheduling and dispatching system with multi-criteria performance evaluation for Industry 3.5 and an empirical study for sustainable TFT-LCD array manufacturing," International Journal of Production Research, Taylor & Francis Journals, vol. 58(24), pages 7531-7547, December.
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