University of Twente Student Theses
Deep reinforcement learning to support dynamic decision-making in a transport network amid travel- and handling time uncertainty
Kessels, J.C. (2024) Deep reinforcement learning to support dynamic decision-making in a transport network amid travel- and handling time uncertainty.
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| Abstract: | This thesis explores efficient route planning techniques within the transport network of Gam Bakker. Specifically, the effectiveness of a deep reinforcement learning-based route planning approach is evaluated using a simulation study. Conceptually and experimentally deep reinforcement learning has demonstrated its efficacy in training agents that contribute to efficient route planning within the model of Gam Bakker’s proposed transport network. |
| Item Type: | Essay (Master) |
| Clients: | Bolk Business Improvement, Hengelo, The Netherlands |
| Faculty: | BMS: Behavioural, Management and Social Sciences |
| Subject: | 31 mathematics, 54 computer science, 55 traffic technology, transport technology |
| Programme: | Industrial Engineering and Management MSc (60029) |
| Link to this item: | https://purl.utwente.nl/essays/98669 |
| Export this item as: | BibTeX EndNote HTML Citation Reference Manager |
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