Resource Constrained Pathfinding with Enhanced Bidirectional A* Search
This work addresses faster pathfinding in large-scale networks for applications like logistics or routing, but it is incremental as it builds upon existing bidirectional A* search paradigms.
The paper tackled the Resource Constrained Shortest Path problem by developing an enhanced bidirectional A* search framework with efficient pruning strategies, resulting in speed-ups of over two orders of magnitude compared to state-of-the-art methods.
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 research 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.