DQN Learning Approach to Scheduling in Multi-job Production Systems

Abstract

Multi-job production is a class of manufacturing systems that produce different products within the same production system. These systems are widely used in production assembly, and becoming a trend with smart factories. In this paper, we propose a Deep-Q reinforcement learning driven scheduling algorithm for multi-job production. In particular, we take into account machine breakdown and production plan change as the inputs of the learning process, which are typically considered as unexpected situations in previous studies. We validate the proposed scheme with real data collected for 6 months between May and October 2019 from a tier-1 vendor of a world top-4 motor company. Our case study shows that the proposed scheme improves the throughput of the production line by 37% compared to the conventional rigid method.