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

TL;DR

This paper introduces a tensor-based GPU acceleration method for local search in vehicle routing problems, significantly improving computational efficiency and broadening applicability across VRP variants.

Contribution

The study presents a novel tensor-based GPU acceleration technique for local search in VRP, enabling faster computations and greater flexibility compared to traditional CPU methods.

Findings

Substantial speedups over CPU implementations.

Effective across multiple VRP variants.

Insight into method's strengths and limitations.

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.