Constraint-Aware Modulation Boosts Neural Routing Solvers
A new paper on arXiv proposes Constraint-Aware Residual Modulation (CARM) to improve Heavy-Encoder-Light-Decoder (HELD) neural routing solvers for complex vehicle routing problems (VRPs). The authors identify that current state embedding mechanisms restrict observation space during attention computation, limiting solution quality. CARM adaptively modulates embeddings to maintain global observation space while being constraint-aware, addressing a key bottleneck in HELD solvers.
Key facts
- Paper arXiv:2605.10122 proposes Constraint-Aware Residual Modulation (CARM) for neural routing solvers.
- Heavy-Encoder-Light-Decoder (HELD) solvers struggle with VRP variants with complex constraints.
- Current mechanisms restrict observation space during attention computation.
- CARM preserves global observation space while being constraint-aware.
- The approach is simple yet powerful, according to the authors.
- The paper systematically revisits neural solvers from the perspective of state embedding generation.
- Empirical analysis demonstrates necessity of global observation space.
- CARM adaptively modulates state embeddings during decoding.
Entities
Institutions
- arXiv