Rapid advances in sensing and communication technologies connect isolated manufacturing units, which generates large amounts of data. The new trend of mass customization brings a higher level of disturbances and uncertainties to production planning. Traditional manufacturing systems analyze data and schedule orders in a centralized architecture, which is inefficient and unreliable for the overdependence on central controllers and limited communication channels. Internet of things (IoT) and cloud technologies make it possible to build a distributed manufacturing architecture such as the multi-agent system (MAS). Recently, artificial intelligence (AI) methods are used to solve scheduling problems in the manufacturing setting. However, it is difficult for scheduling algorithms to process high-dimensional data in a distributed system with heterogeneous manufacturing units. Therefore, this paper presents new cyber-physical integration in smart factories for online scheduling of low-volume-high-mix orders. First, manufacturing units are interconnected with each other through the cyber-physical system (CPS) by IoT technologies. Attributes of machining operations are stored and transmitted by radio frequency identification (RFID) tags. Second, we propose an AI scheduler with novel neural networks for each unit (e.g., warehouse, machine) to schedule dynamic operations with real-time sensor data. Each AI scheduler can collaborate with other schedulers by learning from their scheduling experiences. Third, we design new reward functions to improve the decision-making abilities of multiple AI schedulers based on reinforcement learning (RL). The proposed methodology is evaluated and validated in a smart factory by real-world case studies. Experimental results show that the new architecture for smart factories not only improves the learning and scheduling efficiency of multiple AI schedulers but also effectively deals with unexpected events such as rush orders and machine failures.

传感和通信技术的快速发展将产生大量数据的孤立制造单元连接起来。大规模定制的新趋势给生产计划带来了更高水平的干扰和不确定性。传统的制造系统在集中式架构中分析数据和调度订单，由于过度依赖中央控制器和有限的通信渠道，这种架构效率低下且不可靠。物联网（IoT）和云技术使构建多代理系统（MAS）等分布式制造架构成为可能。最近，人工智能 (AI) 方法用于解决制造环境中的调度问题。然而，在具有异构制造单元的分布式系统中，调度算法难以处理高维数据。因此，本文提出了智能工厂中新的网络物理集成，用于在线调度小批量高混合订单。首先，制造单位通过物联网技术通过信息物理系统（CPS）相互连接。加工操作的属性由射频识别 (RFID) 标签存储和传输。其次，我们为每个单元（例如，仓库、机器）提出了一种具有新颖神经网络的 AI 调度程序，以使用实时传感器数据调度动态操作。每个 AI 调度器都可以通过学习其他调度器的调度经验来与其他调度器协作。第三，我们设计了新的奖励函数，以提高基于强化学习 (RL) 的多个 AI 调度程序的决策能力。通过实际案例研究，在智能工厂中对所提出的方法进行了评估和验证。实验结果表明，智能工厂的新架构不仅提高了多个AI调度器的学习和调度效率，还有效处理了紧急订单和机器故障等突发事件。