File Download
There are no files associated with this item.
Supplementary
-
Citations:
- Appears in Collections:
Conference Paper: Dynamic routing for trucks and drones for multiple tasks after disasters
| Title | Dynamic routing for trucks and drones for multiple tasks after disasters |
|---|---|
| Authors | |
| Issue Date | 28-Sep-2024 |
| Abstract | This study addresses the dynamic routing problem for a truck-and-drone collaboration system with multiple tasks after disasters. The post-disaster scenarios are highly unstable due to uncertainties, for example, the rescue tasks may come stochastically during the rescue response. Therefore, it is critical to update the route of trucks and drones when the new demand comes. Considering different priorities for the rescue tasks, the objective of the proposed problem is to minimize the priority cost, which can be regarded as maximizing the number of rescued people. In this paper, we model the problem as a Markov decision process (MDP) and solve it using the multi-agent reinforcement learning (MARL) method. |
| Persistent Identifier | http://hdl.handle.net/10722/353607 |
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Sun, Wenbo | - |
| dc.contributor.author | Zhang, Fangni | - |
| dc.date.accessioned | 2025-01-21T00:35:58Z | - |
| dc.date.available | 2025-01-21T00:35:58Z | - |
| dc.date.issued | 2024-09-28 | - |
| dc.identifier.uri | http://hdl.handle.net/10722/353607 | - |
| dc.description.abstract | <p>This study addresses the dynamic routing problem for a truck-and-drone collaboration system with multiple tasks after disasters. The post-disaster scenarios are highly unstable due to uncertainties, for example, the rescue tasks may come stochastically during the rescue response. Therefore, it is critical to update the route of trucks and drones when the new demand comes. Considering different priorities for the rescue tasks, the objective of the proposed problem is to minimize the priority cost, which can be regarded as maximizing the number of rescued people. In this paper, we model the problem as a Markov decision process (MDP) and solve it using the multi-agent reinforcement learning (MARL) method. <br></p> | - |
| dc.language | eng | - |
| dc.relation.ispartof | The 6th International Symposium on Multimodal Transportation (27/09/2024-29/09/2024, Nanjing) | - |
| dc.title | Dynamic routing for trucks and drones for multiple tasks after disasters | - |
| dc.type | Conference_Paper | - |