Production scheduling is an essential task in manufacturing, with
Reinforcement Learning (RL) emerging as a key solution. In a previous work, RL
was utilized to solve an extended permutation flow shop scheduling problem
(PFSSP) for a real-world production line with two stages, linked by a central
buffer. The RL agent was trained to sequence equallysized product batches to
minimize setup efforts and idle times. However, the substantial impact caused
by varying the size of these product batches has not yet been explored. In this
follow-up study, we investigate the effects of varying batch sizes, exploring
both the quality of solutions and the training dynamics of the RL agent. The
results demonstrate that it is possible to methodically identify reasonable
boundaries for the batch size. These boundaries are determined on one side by
the increasing sample complexity associated with smaller batch sizes, and on
the other side by the decreasing flexibility of the agent when dealing with
larger batch sizes. This provides the practitioner the ability to make an
informed decision regarding the selection of an appropriate batch size.
Moreover, we introduce and investigate two new curriculum learning strategies
to enable the training with small batch sizes. The findings of this work offer
the potential for application in several industrial use cases with comparable
scheduling problems.

通过使用强化学习算法解决实际生产线上的排产问题，本研究探讨了产品批次大小对解决方案质量和强化学习算法的训练动态的影响，并提出了两种新的课程学习策略，为小批量训练提供支持。这些研究结果可应用于与排产问题类似的多个工业场景。