提出兼顾稀疏与层次结构的多任务多分布路径规划模型，提升真实场景泛化能力。
SHIELD: Multi-task Multi-distribution Vehicle Routing Solver with Sparsity and Hierarchy
- 采用动态稀疏选择机制和分层聚类，自适应分配计算资源
- 在9个真实地图16种路径问题上均超越现有方法
- 适合需要跨场景、多类型路径优化的工业应用
针对路径规划领域基础模型在真实客户分布建模上的不足，本文将多任务路径规划（MTVRP）拓展为更贴近现实的多任务多分布路径规划（MTMDVRP）问题。提出SHIELD模型，融合稀疏性与层次结构设计：通过深度解码器架构引入混合深度（MoD）技术实现动态节点选择，提升效率与泛化能力；构建基于上下文的聚类层，挖掘问题中的层级结构以生成更优局部表示。两项设计共同引导网络识别跨任务与分布的关键特征，在9个真实地图上测试的16种路径规划变体中均表现更优。
原文摘要 · Abstract (English)
Recent advances toward foundation models for routing problems have shown great potential of a unified deep model for various VRP variants. However, they overlook the complex real-world customer distributions. In this work, we advance the Multi-Task VRP (MTVRP) setting to the more realistic yet challenging Multi-Task Multi-Distribution VRP (MTMDVRP) setting, and introduce SHIELD, a novel model that leverages both sparsity and hierarchy principles. Building on a deeper decoder architecture, we first incorporate the Mixture-of-Depths (MoD) technique to enforce sparsity. This improves both efficiency and generalization by allowing the model to dynamically select nodes to use or skip each decoder layer, providing the needed capacity to adaptively allocate computation for learning the task/distribution specific and shared representations. We also develop a context-based clustering layer that exploits the presence of hierarchical structures in the problems to produce better local representations. These two designs inductively bias the network to identify key features that are common across tasks and distributions, leading to significantly improved generalization on unseen ones. Our empirical results demonstrate the superiority of our approach over existing methods on 9 real-world maps with 16 VRP variants each.
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