重新思考约束意识以实现高效的神经路由求解器状态嵌入
Rethinking Constraint Awareness for Efficient State Embedding of Neural Routing Solver
AI总结：
本文提出CARM模块，通过自适应调节上下文嵌入以增强约束意识，提升神经路由求解器在复杂约束下的性能和扩展性。
摘要：
本文提出CARM模块，通过自适应调节上下文嵌入以增强约束意识，提升神经路由求解器在复杂约束下的性能和扩展性。
英文摘要：
Heavy-Encoder-Light-Decoder (HELD) neural routing solvers have emerged as a promising paradigm due to their broad applicability across multiple vehicle routing problems (VRPs). However, they typically struggle with VRP variants with complex constraints. To address this limitation, this paper systematically revisits existing neural solvers from the perspective of the generation mechanism for state embeddings (i.e., query vector prior to compatibility calculation) during decoding. We identify that current mechanisms restrict the observation space during attention computation, introducing a key bottleneck to achieving high-quality solutions. Through detailed empirical analysis, we demonstrate the necessity of preserving a global observation space. To overcome the constraint-agnostic drawback inherent to global observation spaces, we propose a simple yet powerful Constraint-Aware Residual Modulation (CARM) module. By adaptively modulating the context embedding with constraint-relevant variables, CARM effectively enhances constraint awareness, enabling the neural solver to fully leverage the global observation space and generate an efficient state embedding. Extensive experimental results across two single-task and five multi-task neural routing solvers confirm that the CARM module consistently boosts baseline performance. Notably, solvers equipped with our CARM achieve substantial improvements in scaling to large-scale instances and in generalizing to unseen VRP variants. These findings provide valuable insights for the architectural design of neural routing solvers.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
