Dynamic Lot Size Optimization with Reinforcement Learning
Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review
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.
Original language | English |
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Title of host publication | Dynamics in Logistics : Proceedings of the 8th International Conference LDIC 2022, Bremen, Germany |
Editors | Michael Freitag, Aseem Kinra, Hebert Kotzab, Nicole Megow |
Number of pages | 10 |
Place of Publication | Cham |
Publisher | Springer Science and Business Media B.V. |
Publication date | 01.01.2022 |
Pages | 376-385 |
ISBN (print) | 978-3-031-05358-0 |
ISBN (electronic) | 978-3-031-05359-7 |
DOIs | |
Publication status | Published - 01.01.2022 |
Event | International Conference on Dynamics in Logistics - LDIC 2022 - Universität Bremen, Bremen, Germany Duration: 23.02.2022 → 25.02.2022 Conference number: 8 https://www.ldic-conference.org/about-ldic |
- Lot sizing, Reinforcement learning, Simulation
- Engineering