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<p>Due to the growing market demand for product differentia, the household paper production has to adapt to be more flexible in recent years, while it is challenged by uncertainty and complexity in dynamic scheduling. The objectives of minimizing maximum completion time and energy consumption are equivalent to the household paper firms, while traditional tools relied on expert knowledge and human intervention with multiple objectives in this issue. This paper mathematically identified and summarized the household paper workshop scheduling based on its process characteristics of high requirement on flexibility and energy efficiency, and innovatively proposed a deep reinforcement learning-based optimization system via formulating the problem into a Markov game. The optimal solutions are attained through interactions between multiple agents, representing different objectives, in the production scheduling environment. To validate the proposed approach, case studies were conducted using industrial data derived from a household paper company. The results demonstrate the superiority of the proposed method in multiple aspects. Future study could explore deeper to strengthen the model’s generalizability, scalability, and handling uncertainty to validate its applicability in real-world environments.</p>

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Multi-agent deep reinforcement learning-based flexible flow shop scheduling of household paper production

  • 期刊论文

  • 发表日期：

    2026年8月3日

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  • Zhenglei He,
  • Zejun Liu,
  • Xiuli He,
  • Jigeng Li,
  • Mengna Hong,
  • Kim Phuc Tran,
  • Yi Man,
  • Xianyi Zeng

摘要

Due to the growing market demand for product differentia, the household paper production has to adapt to be more flexible in recent years, while it is challenged by uncertainty and complexity in dynamic scheduling. The objectives of minimizing maximum completion time and energy consumption are equivalent to the household paper firms, while traditional tools relied on expert knowledge and human intervention with multiple objectives in this issue. This paper mathematically identified and summarized the household paper workshop scheduling based on its process characteristics of high requirement on flexibility and energy efficiency, and innovatively proposed a deep reinforcement learning-based optimization system via formulating the problem into a Markov game. The optimal solutions are attained through interactions between multiple agents, representing different objectives, in the production scheduling environment. To validate the proposed approach, case studies were conducted using industrial data derived from a household paper company. The results demonstrate the superiority of the proposed method in multiple aspects. Future study could explore deeper to strengthen the model’s generalizability, scalability, and handling uncertainty to validate its applicability in real-world environments.