Resource Constrained Pathfinding with Enhanced Bidirectional A* Search

TL;DR

This paper presents an enhanced bidirectional A* search framework for the Resource Constrained Shortest Path problem, significantly improving search efficiency in large networks through novel pruning strategies.

Contribution

It introduces a new constrained search framework with efficient pruning strategies that accelerates RCSP solutions compared to existing methods.

Findings

Achieves over two orders of magnitude speed-up in search time.

Effectively handles large-scale network instances.

Outperforms state-of-the-art heuristic-guided search methods.

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 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.