| dc.creator |
Bogyrbayeva, Aigerim |
|
| dc.creator |
Yoon, Taehyun |
|
| dc.creator |
Ko, Hanbum |
|
| dc.creator |
Kwon, Changhyun |
|
| dc.creator |
Yun, Hyokun |
|
| dc.creator |
Lim, Sungbin |
|
| dc.date |
2023-03-01T00:00:00Z |
|
| dc.date.accessioned |
2025-02-25T10:17:59Z |
|
| dc.date.available |
2025-02-25T10:17:59Z |
|
| dc.identifier |
1bc7ee7f-2068-4974-8a84-15278186e66e |
|
| dc.identifier |
10.1016/j.trc.2022.103981 |
|
| dc.identifier |
https://avesis.sdu.edu.tr/publication/details/1bc7ee7f-2068-4974-8a84-15278186e66e/oai |
|
| dc.identifier.uri |
http://acikerisim.sdu.edu.tr/xmlui/handle/123456789/98954 |
|
| dc.description |
Reinforcement learning has recently shown promise in learning quality solutions in many combinatorial optimization problems. In particular, the attention-based encoder-decoder models show high effectiveness on various routing problems, including the Traveling Salesman Problem (TSP). Unfortunately, they perform poorly for the TSP with Drone (TSP-D), requiring routing a heterogeneous fleet of vehicles in coordination-a truck and a drone. In TSP-D, the two vehicles are moving in tandem and may need to wait at a node for the other vehicle to join. State-less attention-based decoder fails to make such coordination between vehicles. We propose a hybrid model that uses an attention encoder and a Long Short-Term Memory (LSTM) network decoder, in which the decoder's hidden state can represent the sequence of actions made. We empirically demonstrate that such a hybrid model improves upon a purely attention-based model for both solution quality and computational efficiency. Our experiments on the min-max Capacitated Vehicle Routing Problem (mmCVRP) also confirm that the hybrid model is more suitable for the coordinated routing of multiple vehicles than the attention-based model. The proposed model demonstrates comparable results as the operations research baseline methods. |
|
| dc.language |
eng |
|
| dc.rights |
info:eu-repo/semantics/openAccess |
|
| dc.title |
A deep reinforcement learning approach for solving the Traveling Salesman Problem with Drone |
|
| dc.type |
info:eu-repo/semantics/article |
|