发表机构
The University of Hong Kong（香港大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文针对配备RFID的智能工厂案例，通过挖掘RFID数据量化生产不确定性，提出基于深度强化学习的动态调度框架，仿真显示其在最小化总完工时间上优于FIFO、LIFO和DQN方法。
AI 中文摘要
射频识别（RFID）技术已被广泛应用于制造车间的实时数据采集，进而可用于支持动态车间生产规划与调度。在该环境中，作业与生产过程的不确定性共同导致制造的动态性，阻碍调度系统实现最大效用。为凸显处理此类不确定性的重要性，本文针对一个配备RFID技术的实际智能工厂案例，研究动态车间调度问题。基于RFID采集的生产数据，开展可行生产序列挖掘与实时加工速率估计，以量化作业与生产不确定性。随后，提出一种基于RFID数据分析的深度强化学习方法用于车间生产调度。基于实际案例数据的仿真研究已验证所提出的动态生产调度框架的可行性与实用性。具体而言，观察到该框架在最小化作业总完工时间方面优于现有调度方法，包括先进先出（FIFO）、后进先出（LIFO）和深度Q网络（DQN）。
英文摘要
Radio frequency identification (RFID) technology has been widely implemented for real-time data collection in manufacturing shop floors, which, in turn, can be used to support dynamic shop floor production planning and scheduling. Within such an environment, uncertainty in operation and production processes collectively contribute to the dynamicity in manufacturing, thereby hampering the scheduling system from achieving maximal utility. To highlight the importance of handling such uncertainty, this paper addresses the problem of dynamic shop floor scheduling for a real-life case smart factory equipped with RFID technology. Feasible production sequence mining and real-time processing rate estimation are conducted on RFID-collected production data to quantify the operation and production uncertainties. A deep reinforcement learning approach based on the RFID data analysis is then presented for shop floor production scheduling. Simulation studies based on real-life case data have demonstrated the feasibility and practicality of the proposed dynamic production scheduling framework. Specifically, it is observed that the proposed framework outperforms existing dispatch methods in terms of minimizing operation makespan, including first in first out (FIFO), last in first out (LIFO) and deep Q network (DQN).
