用联邦学习提升跨问题车辆路径优化的通用性与效果
Enhancing Cross-Problem Vehicle Routing via Federated Learning
- 构建多问题预训练+单问题微调的联邦学习框架
- 在多种复杂约束下仍保持高精度与强泛化能力
- 适合需要跨场景部署的物流优化系统
车辆路径问题（VRP）是现代物流与供应链管理中的核心优化挑战。近年来，神经组合优化（NCO）在效率上优于部分传统算法。然而，现有跨问题学习范式在从简单VRP变体迁移到包含不同且复杂约束的问题时，性能下降、泛化能力衰退。为此，本文提出一种创新的“多问题预训练，单问题微调”联邦学习框架（MPSF-FL）。该框架利用联邦全局模型共享共性知识，促进本地模型间高效的知识迁移与适配，使本地模型在保留最新全局知识的同时，能高效适应具有异构复杂约束的下游任务。实验表明，该框架不仅提升了多种VRP下的表现，还在未见问题中增强了泛化能力。
原文摘要 · Abstract (English)
Vehicle routing problems (VRPs) constitute a core optimization challenge in modern logistics and supply chain management. The recent neural combinatorial optimization (NCO) has demonstrated superior efficiency over some traditional algorithms. While serving as a primary NCO approach for solving general VRPs, current cross-problem learning paradigms are still subject to performance degradation and generalizability decay, when transferring from simple VRP variants to those involving different and complex constraints. To strengthen the paradigms, this paper offers an innovative "Multi-problem Pre-train, then Single-problem Fine-tune" framework with Federated Learning (MPSF-FL). This framework exploits the common knowledge of a federated global model to foster efficient cross-problem knowledge sharing and transfer among local models for single-problem fine-tuning. In this way, local models effectively retain common VRP knowledge from up-to-date global model, while being efficiently adapted to downstream VRPs with heterogeneous complex constraints. Experimental results demonstrate that our framework not only enhances the performance in diverse VRPs, but also improves the generalizability in unseen problems.
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