Dynamic Lot Size Optimization with Reinforcement Learning

Publikation: Beiträge in SammelwerkenAufsätze in KonferenzbändenForschungbegutachtet

Authors

Production planning and control has a great influence on the economic efficiency and logistical performance of a company. In this context, this article gives an insight into the use of simulation as a virtual model of a filling machine in the process industry. Furthermore, it shows the possibilities of a reinforcement learning (RL) approach for dynamic lot sizing. The contribution indicates a possible implementation in an ERP system and shows how a decision support tool can support the planner to save up to 5% of costs compared to a human planner and a heuristic approach proposed by Groff.

OriginalspracheEnglisch
TitelDynamics in Logistics : Proceedings of the 8th International Conference LDIC 2022, Bremen, Germany
HerausgeberMichael Freitag, Aseem Kinra, Hebert Kotzab, Nicole Megow
Anzahl der Seiten10
ErscheinungsortCham
VerlagSpringer Science and Business Media B.V.
Erscheinungsdatum2022
Seiten376-385
ISBN (Print)978-3-031-05358-0
ISBN (elektronisch)978-3-031-05359-7
DOIs
PublikationsstatusErschienen - 2022
VeranstaltungInternational Conference on Dynamics in Logistics - LDIC 2022 - Universität Bremen, Bremen, Deutschland
Dauer: 23.02.202225.02.2022
Konferenznummer: 8
https://www.ldic-conference.org/about-ldic

DOI