Abstract Driven by the recent advances in industry 4.0 and industrial artificial intelligence, Automated Guided Vehicles (AGVs) has been widely used in flexible shop floor for material handling. However, great challenges aroused by the high dynamics, complexity, and uncertainty of the shop floor environment still exists on AGVs real-time scheduling. To address these challenges, an adaptive deep reinforcement learning (DRL) based AGVs real-time scheduling approach with mixed rule is proposed to the flexible shop floor to minimize the makespan and delay ratio. Firstly, the problem of AGVs real-time scheduling is formulated as a Markov Decision Process (MDP) in which state representation, action representation, reward function, and optimal mixed rule policy, are described in detail. Then a novel deep q-network (DQN) method is further developed to achieve the optimal mixed rule policy with which the suitable dispatching rules and AGVs can be selected to execute the scheduling towards various states. Finally, the case study based on a real-world flexible shop floor is illustrated and the results validate the feasibility and effectiveness of the proposed approach.

中文翻译：

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基于深度强化学习的 AGV 实时调度混合规则，适用于工业 4.0 中的灵活车间

摘要 在工业 4.0 和工业人工智能的最新进展的推动下，自动导引车 (AGV) 已广泛应用于柔性车间的物料搬运。然而，AGV实时调度仍然存在车间环境的高动态性、复杂性和不确定性带来的巨大挑战。为了应对这些挑战，灵活的车间提出了一种基于自适应深度强化学习 (DRL) 的 AGV 实时调度方法，该方法具有混合规则，以最大限度地减少完工时间和延迟率。首先，AGV 实时调度问题被表述为马尔可夫决策过程 (MDP)，其中详细描述了状态表示、动作表示、奖励函数和最优混合规则策略。然后进一步开发了一种新的深度 q 网络（DQN）方法来实现最优混合规则策略，通过该策略可以选择合适的调度规则和 AGV 来执行针对各种状态的调度。最后，说明了基于现实世界灵活车间的案例研究，结果验证了所提出方法的可行性和有效性。