Speeding up Local Optimization in Vehicle Routing with Tensor-based GPU Acceleration

2025-06-20

AI Summary

To address the high computational cost and poor scalability of neighborhood evaluation in vehicle routing problem (VRP) local search, this paper proposes a tensorized GPU-accelerated framework based on attribute representation. The method introduces, for the first time, a tensor-based modeling paradigm tailored to VRP local search operators, uniformly encoding neighborhood structures as parallelizable tensor operations and fully offloading computation to the GPU. It supports multiple VRP variants while preserving algorithmic transparency and low coupling with existing solvers. Leveraging CUDA optimization, memory-aware scheduling, and attribute-driven encoding, the framework achieves an average 15.3× speedup across three standard benchmarks without compromising solution quality. Experimental results demonstrate its strong scalability and practical deployability in real-world VRP optimization systems.

Technology Category

• Search and Optimization: Local Search
• Machine Learning: Matrix & Tensor Methods
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Systems and Infrastructure for Web, Mobile and WoT: Virtualization and resource management in Web systems and infrastructures

Abstract

Local search plays a central role in many effective heuristic algorithms for the vehicle routing problem (VRP) and its variants. However, neighborhood exploration is known to be computationally expensive and time consuming, especially for large instances or problems with complex constraints. In this study, we explore a promising direction to address this challenge by introducing an original tensor-based GPU acceleration method designed to speed up the commonly used local search operators in vehicle routing. By using an attribute-based representation, the method offers broad extensibility, making it applicable to different VRP variants. Its low-coupling architecture, with intensive computations completely offloaded to the GPU, ensures seamless integration in various local search-based algorithms and frameworks, leading to significant improvements in computational efficiency and potentially improved solution quality. Through comparative experiments on benchmark instances of three routing problems, we demonstrate the substantial computational advantages of the proposed approach over traditional CPU-based implementations. We also provide a detailed analysis of the strengths and limitations of the method, providing valuable insights into its performance characteristics and identifying potential bottlenecks in practical applications. These findings contribute to a better understanding and suggest directions for future improvements.

Problem

• Accelerate local search in VRP using GPU tensor methods
• Reduce computational cost of neighborhood exploration in routing
• Extend GPU acceleration to diverse VRP variants effectively

Innovation

• Tensor-based GPU acceleration for local search
• Attribute-based representation for broad extensibility
• Low-coupling architecture with GPU offloading