#7304. Modular production control using deep reinforcement learning: proximal policy optimization

October 2026publication date
Proposal available till 27-05-2025
4 total number of authors per manuscript0 $

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Journal’s subject area:
Industrial and Manufacturing Engineering;
Artificial Intelligence;
Software;
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Abstract:
EU regulations on CO2 limits and the trend of individualization are pushing the automotive industry towards greater flexibility and robustness in production. One approach to address these challenges is modular production, where workstations are decoupled by automated guided vehicles, requiring new control concepts. Experiments in several modular production control settings demonstrate stable, reliable, optimal, and generalizable learning behavior.
Keywords:
Automotive industry; Deep reinforcement learning; Modular production; Production control; Production scheduling; Proximal policy optimization

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