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<p>In recent years, learning based methods (LBMs) show great potential for solving vehicle routing problems (VRPs). However, most existing LBMs exhibit poor ability in distribution generalization, which hampers their applications in the real world. One reason behind this weakness is their underlying reinforcement learning training objective, which learns the best policy through interactions with the environment while ignoring the structural properties of training instances. To address this weakness and improve the distributional robustness of learned solvers, we propose a novel framework that enhances the knowledge distillation scheme with self-supervised contrastive learning. Compared with reinforcement learning, self-supervised learning can extract rich information from inherent feature representations and relationships of data. Specifically, we introduce a lightweight auxiliary network to the teacher network, so that the corresponding self-supervised signals can be extracted from this auxiliary module for knowledge distillation. By measuring the similarity between self-supervised signals and exploiting it to guide the training process, useful hidden information can be effectively transferred from the teacher network to the student network. Numerical experiments demonstrate the effectiveness of our proposed method, especially in improving the models’ generalization ability.</p>

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Contrastive Learning Enhanced Knowledge Distillation for Learning Distributionally Robust Vehicle Routing Problems

  • 期刊论文

  • 发表日期：

    2025年9月19日

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  • Rui-Yang Shi,
  • Ling-Feng Niu,
  • Xin Shen,
  • Yu-Hong Dai

摘要

In recent years, learning based methods (LBMs) show great potential for solving vehicle routing problems (VRPs). However, most existing LBMs exhibit poor ability in distribution generalization, which hampers their applications in the real world. One reason behind this weakness is their underlying reinforcement learning training objective, which learns the best policy through interactions with the environment while ignoring the structural properties of training instances. To address this weakness and improve the distributional robustness of learned solvers, we propose a novel framework that enhances the knowledge distillation scheme with self-supervised contrastive learning. Compared with reinforcement learning, self-supervised learning can extract rich information from inherent feature representations and relationships of data. Specifically, we introduce a lightweight auxiliary network to the teacher network, so that the corresponding self-supervised signals can be extracted from this auxiliary module for knowledge distillation. By measuring the similarity between self-supervised signals and exploiting it to guide the training process, useful hidden information can be effectively transferred from the teacher network to the student network. Numerical experiments demonstrate the effectiveness of our proposed method, especially in improving the models’ generalization ability.