Energieeffizientes eKanban-System mit autonomen Sensormodulen zur Füllstandsmessung und Reinforcement Learning zur Messintervallanpassung
DOI:
https://doi.org/10.2195/lj_Proc_kreutz_de_202112_01Keywords:
Bestandserfassung, Reinforcement Learning, Volumenmessung, e-Kanban, inventory control, volume measurementAbstract
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.Downloads
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
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