Energieeffizientes eKanban-System mit autonomen Sensormodulen zur Füllstandsmessung und Reinforcement Learning zur Messintervallanpassung

Authors

  • Markus Kreutz Universität Bremen, Fachbereich Produktionstechnik, Bremen, Deutschland
  • Abderrahim Ait Alla Universität Bremen, Fachbereich Produktionstechnik, Bremen, Deutschland
  • Michael Lütjen Universität Bremen, Fachbereich Produktionstechnik, Bremen, Deutschland
  • Michael Freitag Universität Bremen, Fachbereich Produktionstechnik, Bremen, Deutschland

DOI:

https://doi.org/10.2195/lj_Proc_kreutz_de_202112_01

Keywords:

Bestandserfassung, Reinforcement Learning, Volumenmessung, e-Kanban, inventory control, volume measurement

Abstract

Despite the progress of digitalization in industry, manual triggers are still used for inventory measurement, as existing solutions for automating the process are associated with high costs and integration efforts. This paper presents an approach for solving this problem, which is based on cost-effective, autonomous sensor modules for fill level measurement. The measurement is not performed at fixed intervals, but is triggered dynamically and intelligently by a reinforcement learning approach based on the intervals in which contents are taken from the relevant load carriers and the current order situation. The first hardware prototypes for measuring the access to load carriers for content removal and for the sensor modules are also presented in the article.

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Published

2021-12-13

How to Cite

Kreutz, M., Ait Alla, A., Lütjen, M., & Freitag, M. (2021). Energieeffizientes eKanban-System mit autonomen Sensormodulen zur Füllstandsmessung und Reinforcement Learning zur Messintervallanpassung. Logistics Journal: Proceedings, (17). https://doi.org/10.2195/lj_Proc_kreutz_de_202112_01