TY  - JOUR
A1  - Panzer, Marcel
A1  - Gronau, Norbert
T1  - Enhancing economic efficiency in modular production systems through deep reinforcement learning
T2  - Procedia CIRP
N2  - In times of increasingly complex production processes and volatile customer demands, the production adaptability is crucial for a company's profitability and competitiveness. The ability to cope with rapidly changing customer requirements and unexpected internal and external events guarantees robust and efficient production processes, requiring a dedicated control concept at the shop floor level. Yet in today's practice, conventional control approaches remain in use, which may not keep up with the dynamic behaviour due to their scenario-specific and rigid properties. To address this challenge, deep learning methods were increasingly deployed due to their optimization and scalability properties. However, these approaches were often tested in specific operational applications and focused on technical performance indicators such as order tardiness or total throughput. In this paper, we propose a deep reinforcement learning based production control to optimize combined techno-financial performance measures. Based on pre-defined manufacturing modules that are supplied and operated by multiple agents, positive effects were observed in terms of increased revenue and reduced penalties due to lower throughput times and fewer delayed products. The combined modular and multi-staged approach as well as the distributed decision-making further leverage scalability and transferability to other scenarios.
KW  - modular production
KW  - production control
KW  - multi-agent system
KW  - deep reinforcement learning
KW  - discrete event simulation
Y1  - 2024
UR  - https://publishup.uni-potsdam.de/frontdoor/index/index/docId/63256
SN  - 2212-8271
VL  - 121
SP  - 55
EP  - 60
PB  - Elsevier
CY  - Amsterdam
ER  -