  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.
