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

2025-11-22

AI Summary

The Resource-Constrained Shortest Path Problem (RCSPP) is an NP-hard combinatorial optimization problem that suffers from severe scalability bottlenecks on large, dense graphs—e.g., in real-time Unmanned Ground Vehicle (UGV) mission planning. This paper proposes an efficient solution framework integrating A*-guided best-first search, Pulse-style aggressive label pruning, and time-aware resource discretization via temporal binning. This synergy enables compact modeling and dynamic compression of the search space. Compared to state-of-the-art algorithms, our approach achieves 10×–1000× speedup on large-scale UGV terrain maps, reliably solves instances with up to ten thousand nodes, and maintains near-optimal solution quality. The core contribution lies in the principled integration of heuristic guidance, aggressive pruning, and time-sensitive resource binning—significantly enhancing practicality and scalability of RCSPP solvers under stringent resource constraints, high graph density, and strict real-time requirements.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Search and Optimization: Heuristic Search

Application Category

• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

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.

Problem

• Solving resource-constrained shortest path problems on large dense graphs
• Overcoming scalability limitations in NP-hard optimization for real-world applications
• Enabling efficient path planning for autonomous vehicles in complex environments

Innovation

• Hybrid label-setting algorithm for RCSPP
• A* heuristic with Pulse-style pruning mechanisms
• Time-bucketing strategy for state-space reduction