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 …

@inproceedings{grosRealTimeDecisionMakingFor2020,
 author = {Timo P. Gros and Joschka Groß and Verena Wolf},
 booktitle = {2020 Winter Simulation Conference (WSC). Winter Simulation Conference (WSC-2020), December 14-18, Orlando,, FL, USA},
 doi = {10.1109/WSC48552.2020.9383884},
 pages = {3032-3044},
 publisher = {IEEE},
 title = {Real-Time Decision Making for a Car Manufacturing Process Using Deep Reinforcement Learning},
 year = {2020}
}