Survey on Neural Routing Solvers
Fuente:
arXiv
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| Autores principales: | , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866910053515132928 |
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| author | Ba, Yunpeng Lin, Xi Zhou, Changliang Zheng, Ruihao Wang, Zhenkun Liang, Xinyan Lu, Zhichao Sun, Jianyong Qian, Yuhua Zhang, Qingfu |
| author_facet | Ba, Yunpeng Lin, Xi Zhou, Changliang Zheng, Ruihao Wang, Zhenkun Liang, Xinyan Lu, Zhichao Sun, Jianyong Qian, Yuhua Zhang, Qingfu |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_21761 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Survey on Neural Routing Solvers Ba, Yunpeng Lin, Xi Zhou, Changliang Zheng, Ruihao Wang, Zhenkun Liang, Xinyan Lu, Zhichao Sun, Jianyong Qian, Yuhua Zhang, Qingfu Optimization and Control Artificial Intelligence Machine Learning Neural and Evolutionary Computing 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. |
| title | Survey on Neural Routing Solvers |
| topic | Optimization and Control Artificial Intelligence Machine Learning Neural and Evolutionary Computing |
| url | https://arxiv.org/abs/2602.21761 |