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来了来了，还是来了！2024强化学习求解调度问题大盘点虽迟但到！

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从1995年最早将强化学习用于车间调度问题后，在随后的几年里，强化学习一直不温不火，最主要的原因是一般的强化学习无法解决状态空间爆炸的问题，直到2018年深度强化学习开始进军调度领域，并在随后的几年里爆发式增长，在2024年，更是惊人地出现了至少186篇相关文章，相比于2023年，在综述、代码开源、多代理RL、算法融合、问题场景等方面，成果更加丰富和显著。总体而言，2024年DRL在调度领域的研究呈现复杂化、多目标化、跨学科化趋势。

2024DRL调度文章汇总

综述类文章

4篇综述类文章，分别从DRL在分布式调度、与元启发式算法融合、生产调度、图神经网络结合作业车间调度等方面，进行了详细的阐述，可以从整体上快速了解这个方向当前已有的研究成果。

  • [1] Z. J. K. Abadi, N. Mansouri, and M. M. Javidi, “Deep reinforcement learning-based scheduling in distributed systems: a critical review,” Knowledge and Information Systems, vol. 66, no. 10, pp. 5709-5782, Oct, 2024.
  • [2] Y. P. Fu, Y. F. Wang, K. Z. Gao, and M. Huang, “Review on ensemble meta-heuristics and reinforcement learning for manufacturing scheduling problems,” Computers & Electrical Engineering, vol. 120, Dec, 2024.
  • [3] V. Modrak, R. Sudhakarapandian, A. Balamurugan, and Z. Soltysova, “A Review on Reinforcement Learning in Production Scheduling: An Inferential Perspective,” Algorithms, vol. 17, no. 8, Aug, 2024.
  • [4] I. G. Smit, J. N. Zhou, R. Reijnen, Y. X. Wub, J. Chen, C. Zhang, Z. Bukhsh, Y. Q. Zhang, and W. Nuijten, “Graph neural networks for job shop scheduling problems: A survey,” Computers & Operations Research, vol. 176, Apr, 2024.

开源代码解析

DRL常被诟病的点在于其复现性较弱，尤其是在复杂场景下，复现过程存在细节逻辑不透明、参数调优难度大等痛点，通过代码开源可以很好的提供逻辑参考和结果对比。

2024年有大约20篇文章提供了开源链接，其中部分仅给出了环境的建模代码。

研究问题分类

经典调度问题及其扩展

  • 柔性作业车间调度（FJSP）：成为最热门的求解问题（如文献5、6、21、31、102、129、183），重点关注动态性（新作业插入、机器数量变化）和多目标优化（能耗、成本、时间）。
  • 分布式调度：涉及分布式系统（文献1、8、13、23、146）、异构资源（文献9、148）和跨工厂协作（文献58）。
  • 混合流水车间（HFS）：结合批处理、AGV运输（文献3、68、103、153），强调能源效率（文献15、70）。

动态与不确定性处理

  • 动态订单插入（文献55、103）
  • 随机作业到达（文献41、117）
  • 不确定处理时间（文献43、117）
  • 鲁棒优化（文献52）

多目标优化

  • 能效与低碳：绿色调度（文献70、105、155）、碳排放优化（文献9、112）。
  • 多目标权衡：通过MOEA/D（文献70）、NSGA-III（文献104）与DRL结合，生成Pareto前沿。

应用行业分布

制造业

  • 离散制造：汽车装配（文献42、53）、半导体生产（文献137、139）、航空航天（文献181）。
  • 流程工业：化工（文献51、88）、炼钢连铸（文献95）、石化（文献51）。

物流与运输

  • 集装箱码头AGV调度（文献25、161）
  • 公交动态排班（文献10）
  • 卡车装载优化（文献123）

新兴领域

  • 医疗：门诊调度（文献156）。
  • 卫星与通信：卫星任务规划（文献57）、量子网络（文献83）。
  • 能源：电力调度（文献17）、低碳生产（文献9、105）。

研究趋势剖析

通过分析2024年相关文献，可以大致识别出一些当下的热点和潜在的研究方向，供大家参考。

复杂性与动态性增强

研究重点转向动态环境（随机事件、实时响应）和大规模问题（分布式、多工厂）。典型场景包括动态订单插入、AGV协同。

多目标与可持续性驱动

绿色调度（能耗、碳排放）成为核心方向，结合工业5.0需求（文献5、105）。

算法融合与架构创新

  • DRL+传统优化：Memetic算法、遗传算法与DRL结合（文献5、23、159）。
  • DRL+GNN：图神经网络建模调度问题拓扑（文献40、64、102）。
  • 多智能体协作：MARL解决分布式资源竞争（文献24、85）。

工业4.0/5.0技术集成

通过与DRL结合，提升实时性与可扩展性。

  • 数字孪生（文献47、49）
  • 物联网（文献33）
  • 边缘计算（文献83）

跨领域应用扩展

从传统制造业向医疗（文献156）、卫星（文献57）、量子网络（文献83）等新兴领域渗透。

文章汇总清单

  • [1] Z. J. K. Abadi, N. Mansouri, and M. M. Javidi, “Deep reinforcement learning-based scheduling in distributed systems: a critical review,” Knowledge and Information Systems, vol. 66, no. 10, pp. 5709-5782, Oct, 2024.
  • [2] O. A. Aderoba, K. A. Mpofu, O. T. Adenuga, and A. G. B. Nzengue, “Enhancing Dynamic Production Scheduling and Resource Allocation Through Adaptive Control Systems with Deep Reinforcement Learning,” Conference on Production Systems and Logistics. pp. 814-827, 2024.
  • [3] J. C. Cai, and L. Wang, “A Shuffled Frog Leaping Algorithm with Q-Learning for Distributed Hybrid Flow Shop Scheduling Problem with Energy-Saving,” Journal of Artificial Intelligence and Soft Computing Research, vol. 14, no. 2, pp. 101-120, Mar, 2024.
  • [4] J. Cailloux, N. Zufferey, and O. Gallay, “Graph coloring approaches for a production planning problem with makespan and setup penalties in a product-wheel context,” Discrete Applied Mathematics, vol. 355, pp. 200-222, Oct, 2024.
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  • [6] Y. H. Chang, C. H. Liu, and S. D. You, “Scheduling for the Flexible Job-Shop Problem with a Dynamic Number of Machines Using Deep Reinforcement Learning,” Information, vol. 15, no. 2, Feb, 2024.
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  • [31] J. He, and J. Li, “Deep Reinforcement Learning Based on Graph Neural Network for Flexible Job Shop Scheduling Problem with Lot Streaming,” Advanced Intelligent Computing Technology and Applications. pp. 85-95.
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  • [34] K. Hengel, A. Wagner, and M. Ruskowski, “A Dynamic Multi-objective Scheduling Approach for Gradient-Based Reinforcement Learning.” pp. 49-54, 2024.
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  • [36] W. Hou, and S. Zhang, “Assembly line balancing and optimal scheduling for flexible manufacturing workshop,” Journal of Mechanical Science and Technology, 2024 Jun, 2024.
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  • [38] Z. Hou, L. Zhang, Y. Wang, and Y. Hu, “Deep Reinforcement Learning for Dynamic Flexible Job-Shop Scheduling with Automated Guided Vehicles,” Proceedings of Industrial Engineering and Management. pp. 89-99.
  • [39] Y. F. Hu, L. P. Zhang, Z. K. Zhang, Z. X. Li, and Q. H. Tang, “Matheuristic and learning-oriented multi-objective artificial bee colony algorithm for energy-aware flexible assembly job shop scheduling problem,” Engineering Applications of Artificial Intelligence, vol. 133, Jul, 2024.
  • [40] D. L. Huang, H. Zhao, L. J. Zhang, and K. P. Chen, “Learning to Dispatch for Flexible Job Shop Scheduling Based on Deep Reinforcement Learning via Graph Gated Channel Transformation,” Ieee Access, vol. 12, pp. 50935-50948, 2024.
  • [41] J. P. Huang, L. Gao, and X. Y. Li, “A Hierarchical Multi-Action Deep Reinforcement Learning Method for Dynamic Distributed Job-Shop Scheduling Problem With Job Arrivals,” Ieee Transactions on Automation Science and Engineering, 2024 Apr, 2024.
  • [42] Y. Z. Huang, G. C. Fu, B. Y. Sheng, Y. K. Lu, J. P. Yu, and X. Y. Yin, “Deep reinforcement learning for solving car resequencing with selectivity banks in automotive assembly shops,” International Journal of Production Research, 2024 Sep, 2024.
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  • [44] M. H. Jabeur, S. Mahjoub, C. Toublanc, and V. Cariou, “Optimizing integrated lot sizing and production scheduling in flexible flow line systems with energy scheme: A two level approach based on reinforcement learning,” Computers & Industrial Engineering, vol. 190, Apr, 2024.
  • [45] X. Jing, X. F. Yao, M. Liu, and J. J. Zhou, “Multi-agent reinforcement learning based on graph convolutional network for flexible job shop scheduling,” Journal of Intelligent Manufacturing, vol. 35, no. 1, pp. 75-93, Jan, 2024.
  • [46] M. Kemmerling, A. Abdelrazeq, and R. H. Schmitt, “Solving Job Shop Problems with Neural Monte Carlo Tree Search.” pp. 149-158.
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  • [53] S. Lee, J. Kim, G. Wi, Y. Won, Y. Eun, and K. J. Park, “Deep Reinforcement Learning-Driven Scheduling in Multijob Serial Lines: A Case Study in Automotive Parts Assembly,” Ieee Transactions on Industrial Informatics, vol. 20, no. 2, pp. 2932-2943, Feb, 2024.
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