  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.
