In smart manufacturing, scheduling autonomous internal logistic vehicles is crucial for optimizing operational efficiency. This paper proposes a multi-agent deep Q-network (MADQN) with a layer-based communication channel (LBCC) to address this challenge. The main goals are to minimize total job tardiness, reduce the number of tardy jobs, and lower vehicle energy consumption. The method is evaluated against nine well-known scheduling heuristics, demonstrating its effectiveness in handling dynamic job shop behaviors like job arrivals and workstation unavailabilities. The approach also proves scalable, maintaining performance across different layouts and larger problem instances, highlighting the robustness and adaptability of MADQN with LBCC in smart manufacturing.

翻译：在智能制造中，调度自主内部物流车辆对于优化运营效率至关重要。本文提出了一种基于分层通信信道的多智能体深度Q网络来解决这一挑战。其主要目标是最小化总作业延迟、减少延迟作业数量并降低车辆能耗。该方法与九种知名调度启发式算法进行了对比评估，证明了其在处理动态作业车间行为（如作业到达和工作站不可用）方面的有效性。该方法还证明了其可扩展性，能够在不同布局和更大规模问题实例中保持性能，突显了基于分层通信信道的多智能体深度Q网络在智能制造中的鲁棒性和适应性。