Abstract

It is commonly observed in the food industry, battery production, automotive paint shop, and semiconductor manufacturing that an intermediate product’s residence time in the buffer within a production line is controlled by a time window to guarantee product quality. There is typically a minimum time limit reflected by a part’s travel time or process requirement. Meanwhile, these intermediate parts are prevented from staying in the buffer for too long by an upper time limit, exceeding which a part will be scrapped or need additional treatment. To increase production throughput and reduce scrap, one needs to control machines’ working mode according to real-time system information in the stochastic production environment, which is a difficult problem to solve, due to the system’s complexity. In this article, we propose a novel decomposition-based control approach by decomposing a production system into small-scale subsystems based on domain knowledge and their structural relationship. An iterative aggregation procedure is then used to generate a production control policy with convergence guarantee. Numerical studies suggest that the decomposition-based control approach outperforms general-purpose reinforcement learning method by delivering significant system performance improvement and substantial reduction on computation overhead.

摘要

在食品行业、电池生产、汽车涂装车间和半导体制造中，通常观察到中间产品在生产线缓冲区中的停留时间由时间窗口控制，以保证产品质量。零件的行程时间或流程要求通常反映了最短时间限制。同时，通过时间上限防止这些中间件在缓冲区中停留时间过长，超过该时间将导致零件报废或需要额外处理。为了增加生产量和减少废品，需要在随机生产环境中根据实时系统信息控制机器的工作模式，由于系统的复杂性，这是一个难以解决的问题。在本文中，我们通过将生产系统分解为基于领域知识及其结构关系的小规模子系统，提出了一种新的基于分解的控制方法。然后使用迭代聚合程序生成具有收敛保证的生产控制策略。数值研究表明，基于分解的控制方法通过显着提高系统性能和显着减少计算开销来优于通用强化学习方法。