Resource Constrained Pathfinding with A* and Negative Weights
This addresses pathfinding with multiple resource limits, a challenging problem in network optimization with broad real-world applications, representing a strong specific gain.
The paper tackles the Resource Constrained Shortest Path Problem (RCSP) in large networks, including cases with negative costs and resources, by introducing a new A*-based search framework, achieving up to two orders of magnitude faster performance compared to state-of-the-art algorithms.
Constrained pathfinding is a well-studied, yet challenging network optimisation problem that can be seen in a broad range of real-world applications. Pathfinding with multiple resource limits, which is known as the Resource Constrained Shortest Path Problem (RCSP), aims to plan a cost-optimum path subject to limited usage of resources. Given the recent advances in constrained and multi-criteria search with A*, this paper introduces a new resource constrained search framework on the basis of A* to tackle RCSP in large networks, even in the presence of negative cost and negative resources. We empirically evaluate our new algorithm on a set of large instances and show up to two orders of magnitude faster performance compared to state-of-the-art RCSP algorithms in the literature.