Real-Time Decision Making for a Car Manufacturing Process Using Deep Reinforcement Learning

Abstract

Computer simulations of manufacturing processes are in widespread use for optimizing production planning and order processing. If unforeseeable events are common, real-time decisions are necessary to maximize the performance of the manufacturing process. Pre-trained AI-based decision support offers promising opportunities for such time-critical production processes. Here, we explore the effectiveness of deep reinforcement learning for real-time decision making in a car manufacturing process. We combine a simulation model of a central production part, the line buffer, with deep reinforcement learning algorithms, in particular with deep Q-Learning and Monte Carlo tree search. We simulate two different versions of the buffer, a single-agent and a multi-agent one, to generate large amounts of data and train neural networks to represent near-optimal strategies. Our results show that deep reinforcement learning performs extremely well and the resulting strategies provide near-optimal decisions in real-time, while alternative approaches are either slow or give strategies of poor quality.