Dynamic Lot Size Optimization with Reinforcement Learning
Research output: Contributions to collected editions/works › Chapter › 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 |
|---|---|
| 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 | 2022 |
| Pages | 376-385 |
| ISBN (Print) | 978-3-031-05358-0 |
| ISBN (Electronic) | 978-3-031-05359-7 |
| DOIs | |
| Publication status | Published - 2022 |
- Lot sizing, Reinforcement learning, Simulation
- Engineering