Distance-aware Attention Reshaping: Enhance Generalization of Neural Solver for Large-scale Vehicle Routing Problems
This addresses a scalability issue for researchers and practitioners using neural solvers in logistics and optimization, but it is incremental as it builds on existing attention-based methods.
The paper tackles the poor generalization of neural solvers for vehicle routing problems when scaling from small to large instances by proposing a distance-aware attention reshaping method, which significantly outperforms existing state-of-the-art neural solvers on the large-scale CVRPLib dataset.
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.