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Entwicklung einer Methode zum Einsatz von Reinforcement Learning für die dynamische Fertigungsdurchlaufsteuerung

Entwicklung einer Methode zum Einsatz von Reinforcement Learning für die dynamische Fertigungsdurchlaufsteuerung

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This work aims to develop a method that can reschedule the matrix production in the case of a disruption. For this purpose, different artificial intelligence methods are combined in a novel way. The developed method is validated on a theoretical and a real scheduling case.

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Keywords

  • artificial intelligence
  • Künstliche Intelligenz
  • Mechanical engineering & materials
  • Production control
  • Produktionssteuerung
  • reinforcement learning
  • Scheduling
  • Technology, engineering, agriculture
  • Terminierung

Links

DOI: 10.5445/KSP/1000156002

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