TY  - JOUR
A1  - Panzer, Marcel
A1  - Bender, Benedict
A1  - Gronau, Norbert
T1  - Neural agent-based production planning and control
T2  - Journal of Manufacturing Systems
N2  - Nowadays, production planning and control must cope with mass customization, increased fluctuations in demand, and high competition pressures. Despite prevailing market risks, planning accuracy and increased adaptability in the event of disruptions or failures must be ensured, while simultaneously optimizing key process indicators. To manage that complex task, neural networks that can process large quantities of high-dimensional data in real time have been widely adopted in recent years. Although these are already extensively deployed in production systems, a systematic review of applications and implemented agent embeddings and architectures has not yet been conducted. The main contribution of this paper is to provide researchers and practitioners with an overview of applications and applied embeddings and to motivate further research in neural agent-based production. Findings indicate that neural agents are not only deployed in diverse applications, but are also increasingly implemented in multi-agent environments or in combination with conventional methods — leveraging performances compared to benchmarks and reducing dependence on human experience. This not only implies a more sophisticated focus on distributed production resources, but also broadening the perspective from a local to a global scale. Nevertheless, future research must further increase scalability and reproducibility to guarantee a simplified transfer of results to reality.
KW  - production planning and control
KW  - machine learning
KW  - neural networks
KW  - systematic literature review
KW  - taxonomy
Y1  - 2022
UR  - https://publishup.uni-potsdam.de/frontdoor/index/index/docId/60927
SN  - 0278-6125
VL  - 65
SP  - 743
EP  - 766
PB  - Elsevier
CY  - Amsterdam
ER  -