  Recent deep models for solving routing problems always assume a single
distribution of nodes for training, which severely impairs their
cross-distribution generalization ability. In this paper, we exploit group
distributionally robust optimization (group DRO) to tackle this issue, where we
jointly optimize the weights for different groups of distributions and the
parameters for the deep model in an interleaved manner during training. We also
design a module based on convolutional neural network, which allows the deep
model to learn more informative latent pattern among the nodes. We evaluate the
proposed approach on two types of well-known deep models including GCN and
POMO. The experimental results on the randomly synthesized instances and the
ones from two benchmark dataset (i.e., TSPLib and CVRPLib) demonstrate that our
approach could significantly improve the cross-distribution generalization
performance over the original models.
