  Neural combinatorial optimization (NCO) is a promising learning-based
approach to solving various vehicle routing problems without much manual
algorithm design. However, the current NCO methods mainly focus on the
in-distribution performance, while the real-world problem instances usually
come from different distributions. A costly fine-tuning approach or generalized
model retraining from scratch could be needed to tackle the out-of-distribution
instances. Unlike the existing methods, this work investigates an efficient
prompt learning approach in NCO for cross-distribution adaptation. To be
concrete, we propose a novel prompt learning method to facilitate fast
zero-shot adaptation of a pre-trained model to solve routing problem instances
from different distributions. The proposed model learns a set of prompts among
various distributions and then selects the best-matched one to prompt a
pre-trained attention model for each problem instance. Extensive experiments
show that the proposed prompt learning approach facilitates the fast adaptation
of pre-trained routing models. It also outperforms existing generalized models
on both in-distribution prediction and zero-shot generalization to a diverse
set of new tasks. Our code implementation is available online
https://github.com/FeiLiu36/PromptVRP.
