Neural construction models have shown promising performance for Vehicle Routing Problems (VRPs) by adopting either the Autoregressive (AR) or Non-Autoregressive (NAR) learning approach. While AR models produce high-quality solutions, they generally have a high inference latency due to their sequential generation nature. Conversely, NAR models generate solutions in parallel with a low inference latency but generally exhibit inferior performance. In this paper, we propose a generic Guided Non-Autoregressive Knowledge Distillation (GNARKD) method to obtain high-performance NAR models having a low inference latency. GNARKD removes the constraint of sequential generation in AR models while preserving the learned pivotal components in the network architecture to obtain the corresponding NAR models through knowledge distillation. We evaluate GNARKD by applying it to three widely adopted AR models to obtain NAR VRP solvers for both synthesized and real-world instances. The experimental results demonstrate that GNARKD significantly reduces the inference time (4-5 times faster) with acceptable performance drop (2-3\%). To the best of our knowledge, this study is first-of-its-kind to obtain NAR VRP solvers from AR ones through knowledge distillation.

翻译：神经构造模型通过采用自回归（AR）或非自回归（NAR）学习方式，在车辆路径问题（VRP）中展现出良好的性能。AR模型虽能生成高质量解，但因序列生成特性通常具有高推理延迟；而NAR模型虽能并行生成解且推理延迟低，但性能通常较差。本文提出一种通用的引导式非自回归知识蒸馏（GNARKD）方法，以获取高性能且低推理延迟的NAR模型。GNARKD通过移除AR模型中序列生成的约束，同时保留网络架构中习得的关键组件，借助知识蒸馏获得对应的NAR模型。我们将GNARKD应用于三种广泛采用的AR模型，分别获取合成实例与真实实例的NAR型VRP求解器，并对其性能进行评估。实验结果表明，GNARKD能在性能可接受下降（2-3%）的前提下显著减少推理时间（加速4-5倍）。据我们所知，本研究首次通过知识蒸馏从AR求解器获取NAR型VRP求解器。