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 | 
|---|---|
| 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  | 
- Ingenieurwissenschaften
 
