APULSE: A Scalable Hybrid Algorithm for the RCSPP on Large-Scale Dense Graphs

TL;DR

APULSE is a hybrid algorithm that significantly improves scalability and efficiency in solving the resource-constrained shortest path problem on large, dense graphs, with applications in autonomous vehicle planning.

Contribution

The paper introduces APULSE, a novel hybrid label-setting algorithm combining A* heuristics and Pulse pruning for large-scale RCSPP instances.

Findings

APULSE finds near-optimal solutions faster than existing methods.

APULSE is more robust on large, dense graphs.

It enables real-time decision support in complex environments.

Abstract

The resource-constrained shortest path problem (RCSPP) is a fundamental NP-hard optimization challenge with broad applications, from network routing to autonomous navigation. This problem involves finding a path that minimizes a primary cost subject to a budget on a secondary resource. While various RCSPP solvers exist, they often face critical scalability limitations when applied to the large, dense graphs characteristic of complex, real-world scenarios, making them impractical for time-critical planning. This challenge is particularly acute in domains like mission planning for unmanned ground vehicles (UGVs), which demand solutions on large-scale terrain graphs. This paper introduces APULSE, a hybrid label-setting algorithm designed to efficiently solve the RCSPP on such challenging graphs. APULSE integrates a best-first search guided by an A* heuristic with aggressive, Pulse-style pruning mechanisms and a time-bucketing strategy for effective state-space reduction. A computational study, using a large-scale UGV planning scenario, benchmarks APULSE against state-of-the-art algorithms. The results demonstrate that APULSE consistently finds near-optimal solutions while being orders of magnitude faster and more robust, particularly on large problem instances where competing methods fail. This superior scalability establishes APULSE as an effective solution for RCSPP in complex, large-scale environments, enabling capabilities such as interactive decision support and dynamic replanning.