mlsanityData to 7 Oct 2026

Survey on Neural Routing Solvers

arXiv 2602.21761 ↗First posted 25 Feb 2026math.OC cs.AI cs.LGcited by 4

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Abstract (arXiv)

Neural routing solvers (NRSs) that leverage deep learning to tackle vehicle routing problems have demonstrated notable potential for practical applications. By learning implicit heuristic rules from data, NRSs replace the handcrafted counterparts in classic heuristic frameworks, thereby reducing reliance on costly manual design and trial-and-error adjustments. This survey makes two main contributions: (1) The heuristic nature of NRSs is highlighted, and existing NRSs are reviewed from the perspective of heuristics. A hierarchical taxonomy based on heuristic principles is further introduced. (2) A generalization-focused evaluation pipeline is proposed to address limitations of the conventional pipeline. Comparative benchmarking of representative NRSs across both pipelines uncovers a series of previously unreported gaps in current research.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 87 of its 156 references that are in the map. First posted on arXiv 25 Feb 2026.

Builds on

87 of 156 references.

References · 87

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38 more
  1. 2023DIFUSCO: Graph-based Diffusion Solvers for Combinatorial Optimizationcited by 216
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50 most cited of 87 All 87 (JSON)

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 4.

4 papers.

  1. 2026Rethinking Constraint Awareness for Efficient State Embedding of Neural Routing Solvercited by 1
  2. 2026SPACE: Unifying Symmetric and Asymmetric Routing Problems for Generalist Neural Solvercited by 1
  3. 2026Compute Time Scaling with Recursive Models for Combinatorial Optimizationcited by 0
  4. 2026MiLoop: Selective Memory Propagation for Neural Combinatorial Optimizationcited by 0