多智能体强化学习优化智能工厂物流车调度，降延迟、省能耗。
Multi-Agent Deep Q-Network with Layer-based Communication Channel for Autonomous Internal Logistics Vehicle Scheduling in Smart Manufacturing
- 基于分层通信的多智能体DQN，协同决策车辆调度。
- 相比九种启发式方法，任务延迟减少23.7%，车辆能耗降低18.5%。
- 适用于动态生产环境，适合智能制造中的实时调度场景。
在智能制造中，调度自主内部物流车辆对提升运营效率至关重要。本文提出一种带分层通信通道（LBCC）的多智能体深度Q网络（MADQN），以最小化总任务延迟、减少迟交任务数并降低车辆能耗。该方法在应对动态作业车间行为（如工单到达与工作站不可用）方面表现优异，相较于九种知名调度启发式算法，展现出显著优势。同时，该方法在不同布局和更大规模问题实例下仍保持稳定性能，验证了MADQN与LBCC在智能制造中的鲁棒性与可扩展性。
原文摘要 · Abstract (English)
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.
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