A flow control system is a critical concept for increasing the production capacity of manufacturing systems. To solve the scheduling optimization problem related to the flow control with the aim of improving productivity, existing methods depend on a heuristic design by domain human experts. Therefore, the methods require correction, monitoring, and verification by using real equipment. As system designs increase in complexity, the monitoring time increases, which decreases the probability of arriving at the optimal design. As an alternative approach to the heuristic design of flow control systems, the use of deep reinforcement learning to solve the scheduling optimization problem has been considered. Although the existing research on reinforcement learning has yielded excellent performance in some areas, the applicability of the results to actual FAB such as display and semiconductor manufacturing processes is not evident so far. To this end, we propose a method to implement a physical simulation environment and devise a feasible flow control system design using a transfer robot in display manufacturing through reinforcement learning. We present a model and parameter setting to build a virtual environment for different display transfer robots, and training methods of reinforcement learning on the environment to obtain an optimal scheduling of glass flow control systems. Its feasibility was verified by using different types of robots used in the actual process.

翻译：流量控制系统是提升制造系统产能的关键概念。针对以提高生产力为目标的流量控制调度优化问题，现有方法依赖领域人类专家的启发式设计。因此，这些方法需要借助实际设备进行校正、监控和验证。随着系统设计复杂度提升，监控时间增加，达到最优设计的概率随之降低。作为流量控制系统启发式设计的替代方案，利用深度强化学习解决调度优化问题已得到关注。尽管现有强化学习研究在特定领域展现出优异性能，但其在显示面板、半导体制造等实际FAB场景中的适用性至今尚不明确。为此，我们提出一种在显示制造领域通过强化学习实现物理仿真环境搭建及搬运机器人可行流量控制系统设计的方法。我们构建了面向不同显示搬运机器人的虚拟环境模型与参数设置方案，并提出了在该环境中进行强化学习训练的方法以实现玻璃流量控制系统的优化调度。通过使用实际产线中的多种类型机器人验证了该方法的可行性。