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.

通过距离感知的注意力重整方法，本文提出了一种助力神经求解器解决大规模车辆路径问题的方法，并且实验结果显示该方法在大规模数据集上明显优于现有的神经求解器。