Distance-aware Attention Reshaping: Enhance Generalization of Neural Solver for Large-scale Vehicle Routing Problems

TL;DR

This paper introduces a distance-aware attention reshaping technique that improves neural solvers' ability to generalize from small to large-scale vehicle routing problems by leveraging Euclidean distance information without extra training.

Contribution

It proposes a novel attention reshaping method that enhances neural solver generalization for large-scale problems using distance information without additional training.

Findings

Significantly outperforms existing neural solvers on large-scale datasets.

Effectively utilizes Euclidean distance to reshape attention scores.

Improves solution quality for large-scale vehicle routing problems.

Abstract

Neural solvers based on attention mechanism have demonstrated remarkable effectiveness in solving vehicle routing problems. However, in the generalization process from small scale to large scale, we find a phenomenon of the dispersion of attention scores in existing neural solvers, which leads to poor performance. To address this issue, this paper proposes a distance-aware attention reshaping method, assisting neural solvers in solving large-scale vehicle routing problems. Specifically, without the need for additional training, we utilize the Euclidean distance information between current nodes to adjust attention scores. This enables a neural solver trained on small-scale instances to make rational choices when solving a large-scale problem. Experimental results show that the proposed method significantly outperforms existing state-of-the-art neural solvers on the large-scale CVRPLib dataset.