Dynamic Lot Size Optimization with Reinforcement Learning
Publikation: Beiträge in Sammelwerken › Aufsätze in Konferenzbänden › Forschung › begutachtet
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.
Originalsprache | Englisch |
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Titel | Dynamics in Logistics : Proceedings of the 8th International Conference LDIC 2022, Bremen, Germany |
Herausgeber | Michael Freitag, Aseem Kinra, Hebert Kotzab, Nicole Megow |
Anzahl der Seiten | 10 |
Erscheinungsort | Cham |
Verlag | Springer Science and Business Media B.V. |
Erscheinungsdatum | 01.01.2022 |
Seiten | 376-385 |
ISBN (Print) | 978-3-031-05358-0 |
ISBN (elektronisch) | 978-3-031-05359-7 |
DOIs | |
Publikationsstatus | Erschienen - 01.01.2022 |
Veranstaltung | International Conference on Dynamics in Logistics - LDIC 2022 - Universität Bremen, Bremen, Deutschland Dauer: 23.02.2022 → 25.02.2022 Konferenznummer: 8 https://www.ldic-conference.org/about-ldic |
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