University of Twente Student Theses
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Optimising early design decisions for SBS/RS warehouses through a Deep Q-Learning model integrated with simulation software at Vanderlande
Eldik, J.E. van (2025) Optimising early design decisions for SBS/RS warehouses through a Deep Q-Learning model integrated with simulation software at Vanderlande.
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| Abstract: | This thesis explores developing a Deep Q-Learning model to optimize early design decisions in Shuttle-Based Storage and Retrieval systems at Vanderlande warehouses. The model automates configuration through simulation feedback on throughput and costs, aiming to determine optimal designs for ADAPTO warehouses. Trained to interact with simulation software, the DQL agent proposes configurations and adjusts policies based on feedback to approach an optimal strategy. However, it struggled to consistently yield optimal configurations or converge reliably. Insights into reward structures, exploration strategies, and DQL variants highlight the benefit of simple reward structures penalizing goal-reaching failures and rewarding cost-effective solutions. Despite efforts like Prioritized Experience Replay and Double DQN, performance did not significantly improve. Tuning hyperparameters, especially for Double DQN, may enhance future performance. In conclusion, while the model didn't generalize well across complex reward environments with shifting throughput goals, it offers foundational insights. Future work could focus on refining hyperparameters and extending architectures for better convergence and scalability. |
| Item Type: | Essay (Master) |
| Faculty: | BMS: Behavioural, Management and Social Sciences |
| Subject: | 50 technical science in general |
| Programme: | Industrial Engineering and Management MSc (60029) |
| Link to this item: | https://purl.utwente.nl/essays/106354 |
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