Reconfigurable manufacturing systems (RMS) are critical for future market adjustment given their rapid adaptation to fluctuations in consumer demands, the introduction of new technological advances, and disruptions in linked supply chain sections. The adjustable hard settings of such systems require a flexible soft planning mechanism that enables realtime production planning and scheduling amid the existing complexity and variability in their configuration settings. This study explores the application of multi agent reinforcement learning (MARL) for dynamic scheduling in soft planning of the RMS settings. In the proposed framework, deep Qnetwork (DQN) agents trained in centralized training learn optimal job machine assignments in real time while adapting to stochastic events such as machine breakdowns and reconfiguration delays. The model also incorporates a negotiation with an attention mechanism to enhance state representation and improve decision focus on critical system features. Key DQN enhancements including prioritized experience replay, nstep returns, double DQN and soft target update are used to stabilize and accelerate learning. Experiments conducted in a simulated RMS environment demonstrate that the proposed approach outperforms baseline heuristics in reducing makespan and tardiness while improving machine utilization. The reconfigurable manufacturing environment was extended to simulate realistic challenges, including machine failures and reconfiguration times. Experimental results show that while the enhanced DQN agent is effective in adapting to dynamic conditions, machine breakdowns increase variability in key performance metrics such as makespan, throughput, and total tardiness. The results confirm the advantages of applying the MARL mechanism for intelligent and adaptive scheduling in dynamic reconfigurable manufacturing environments.

翻译：可重构制造系统（RMS）因其能够快速适应消费者需求波动、新技术进步引入以及关联供应链环节中断，对未来市场调整至关重要。此类系统的可调硬件设置需要一种灵活的软规划机制，以在现有配置设置的复杂性和可变性中实现实时生产规划与调度。本研究探讨了多智能体强化学习（MARL）在RMS设置软规划中动态调度的应用。在所提出的框架中，通过集中式训练训练的深度Q网络（DQN）智能体实时学习最优的作业-机器分配，同时适应机器故障和重构延迟等随机事件。该模型还结合了带有注意力机制的协商机制，以增强状态表示并提高对关键系统特征的决策聚焦。关键的DQN增强技术包括优先经验回放、n步回报、双DQN和软目标更新，用于稳定和加速学习。在模拟RMS环境中进行的实验表明，所提出的方法在减少制造周期和延迟时间、提高机器利用率方面优于基准启发式方法。可重构制造环境被扩展以模拟现实挑战，包括机器故障和重构时间。实验结果表明，虽然增强的DQN智能体能有效适应动态条件，但机器故障增加了制造周期、吞吐量和总延迟时间等关键性能指标的变异性。结果证实了应用MARL机制在动态可重构制造环境中实现智能自适应调度的优势。