Neural models produce promising results when solving Vehicle Routing Problems
(VRPs), but often fall short in generalization. Recent attempts to enhance
model generalization often incur unnecessarily large training cost or cannot be
directly applied to other models solving different VRP variants. To address
these issues, we take a novel perspective on model architecture in this study.
Specifically, we propose a plug-and-play Entropy-based Scaling Factor (ESF) and
a Distribution-Specific (DS) decoder to enhance the size and distribution
generalization, respectively. ESF adjusts the attention weight pattern of the
model towards familiar ones discovered during training when solving VRPs of
varying sizes. The DS decoder explicitly models VRPs of multiple training
distribution patterns through multiple auxiliary light decoders, expanding the
model representation space to encompass a broader range of distributional
scenarios. We conduct extensive experiments on both synthetic and widely
recognized real-world benchmarking datasets and compare the performance with
seven baseline models. The results demonstrate the effectiveness of using ESF
and DS decoder to obtain a more generalizable model and showcase their
applicability to solve different VRP variants, i.e., travelling salesman
problem and capacitated VRP. Notably, our proposed generic components require
minimal computational resources, and can be effortlessly integrated into
conventional generalization strategies to further elevate model generalization.

提出了一种新的模型架构，包括熵增缩放因子（ESF）和分布特定解码器（DS），用于增强神经模型在解决不同规模和分布的车辆路径问题方面的泛化能力，并证明了它们的有效性和适用性。