Resource Constrained Pathfinding with Enhanced Bidirectional A* Search
Abstract
The classic Resource Constrained Shortest Path (RCSP) problem aims to find a cost optimal path between a pair of nodes in a network such that the resources used in the path are within a given limit. Having been studied for over a decade, RCSP has seen recent solutions that utilize heuristic-guided search to solve the constrained problem faster. Building upon the bidirectional A* search paradigm, this paper introduces a novel constrained search framework that uses efficient pruning strategies to allow for accelerated and effective RCSP search in large-scale networks. Results show that, compared to the state of the art, our enhanced framework can significantly reduce the constrained search time, achieving speed-ups of over to two orders of magnitude.
Cite
Text
Ahmadi et al. "Resource Constrained Pathfinding with Enhanced Bidirectional A* Search." AAAI Conference on Artificial Intelligence, 2025. doi:10.1609/AAAI.V39I25.34892Markdown
[Ahmadi et al. "Resource Constrained Pathfinding with Enhanced Bidirectional A* Search." AAAI Conference on Artificial Intelligence, 2025.](https://mlanthology.org/aaai/2025/ahmadi2025aaai-resource/) doi:10.1609/AAAI.V39I25.34892BibTeX
@inproceedings{ahmadi2025aaai-resource,
title = {{Resource Constrained Pathfinding with Enhanced Bidirectional A* Search}},
author = {Ahmadi, Saman and Raith, Andrea and Tack, Guido and Jalili, Mahdi},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2025},
pages = {26878-26885},
doi = {10.1609/AAAI.V39I25.34892},
url = {https://mlanthology.org/aaai/2025/ahmadi2025aaai-resource/}
}