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Injection Mold Production Sustainable Scheduling Using Deep Reinforcement Learning

Author

Listed:
  • Seunghoon Lee

    (Department of Industrial Engineering, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul 03722, Korea)

  • Yongju Cho

    (Korea Institute of Industrial Technology, 89 Yangdaegiro-gil, Seobuk-gu, Cheonan-si 31056, Korea)

  • Young Hoon Lee

    (Department of Industrial Engineering, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul 03722, Korea)

Abstract

In the injection mold industry, it is important for manufacturers to satisfy the delivery date for the products that customers order. The mold products are diverse, and each product has a different manufacturing process. Owing to the nature of mold, mold manufacturing is a complex and dynamic environment. To meet the delivery date of the customers, the scheduling of mold production is important and is required to be sustainable and intelligent even in the complicated system and dynamic situation. To address this, in this paper, deep reinforcement learning (RL) is proposed for injection mold production scheduling. Before presenting the RL algorithm, a mathematical model for the mold scheduling problem is presented, and a Markov decision process framework is proposed for RL. The deep Q -network, which is an algorithm for RL, is employed to find the scheduling policy to minimize the total weighted tardiness. The results of experiments demonstrate that the proposed deep RL method outperforms the dispatching rules that are presented for minimizing the total weighted tardiness.

Suggested Citation

  • Seunghoon Lee & Yongju Cho & Young Hoon Lee, 2020. "Injection Mold Production Sustainable Scheduling Using Deep Reinforcement Learning," Sustainability, MDPI, vol. 12(20), pages 1-17, October.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:20:p:8718-:d:432170
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    References listed on IDEAS

    as
    1. Nagarur, Nagen & Vrat, Prem & Duongsuwan, Wanchai, 1997. "Production planning and scheduling for injection moulding of pipe fittings A case study," International Journal of Production Economics, Elsevier, vol. 53(2), pages 157-170, November.
    2. Olumide Emmanuel Oluyisola & Fabio Sgarbossa & Jan Ola Strandhagen, 2020. "Smart Production Planning and Control: Concept, Use-Cases and Sustainability Implications," Sustainability, MDPI, vol. 12(9), pages 1-29, May.
    3. Seunghoon Lee & Young Hoon Lee & Yongho Choi, 2019. "Project Portfolio Selection Considering Total Cost of Ownership in the Automobile Industry," Sustainability, MDPI, vol. 11(17), pages 1-17, August.
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    Citations

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    Cited by:

    1. Behice Meltem Kayhan & Gokalp Yildiz, 2023. "Reinforcement learning applications to machine scheduling problems: a comprehensive literature review," Journal of Intelligent Manufacturing, Springer, vol. 34(3), pages 905-929, March.
    2. Hail Jung & Jinsu Jeon & Dahui Choi & Jung-Ywn Park, 2021. "Application of Machine Learning Techniques in Injection Molding Quality Prediction: Implications on Sustainable Manufacturing Industry," Sustainability, MDPI, vol. 13(8), pages 1-16, April.
    3. Seunghoon Lee & Yongju Cho & Minjae Ko, 2020. "Robust Optimization Model for R&D Project Selection under Uncertainty in the Automobile Industry," Sustainability, MDPI, vol. 12(23), pages 1-15, December.

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