Resource Constrained Pathfinding with Enhanced Bidirectional A* Search
Saman Ahmadi, Andrea Raith, Guido Tack, Mahdi Jalili
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.
BibTeX
@article{Ahmadi_Raith_Tack_Jalili_2025, title={Resource Constrained Pathfinding with Enhanced Bidirectional A* Search}, volume={39}, url={https://ojs.aaai.org/index.php/AAAI/article/view/34892}, DOI={10.1609/aaai.v39i25.34892}, abstractNote={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.}, number={25}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Ahmadi, Saman and Raith, Andrea and Tack, Guido and Jalili, Mahdi}, year={2025}, month={Apr.}, pages={26878-26885} }