SVRPBench: A Realistic Benchmark for Stochastic Vehicle Routing Problem
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866912401853513728 |
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| author | Heakl, Ahmed Shaaban, Yahia Salaheldin Takac, Martin Lahlou, Salem Iklassov, Zangir |
| author_facet | Heakl, Ahmed Shaaban, Yahia Salaheldin Takac, Martin Lahlou, Salem Iklassov, Zangir |
| contents | Robust routing under uncertainty is central to real-world logistics, yet most benchmarks assume static, idealized settings. We present SVRPBench, the first open benchmark to capture high-fidelity stochastic dynamics in vehicle routing at urban scale. Spanning more than 500 instances with up to 1000 customers, it simulates realistic delivery conditions: time-dependent congestion, log-normal delays, probabilistic accidents, and empirically grounded time windows for residential and commercial clients. Our pipeline generates diverse, constraint-rich scenarios, including multi-depot and multi-vehicle setups. Benchmarking reveals that state-of-the-art RL solvers like POMO and AM degrade by over 20% under distributional shift, while classical and metaheuristic methods remain robust. To enable reproducible research, we release the dataset and evaluation suite. SVRPBench challenges the community to design solvers that generalize beyond synthetic assumptions and adapt to real-world uncertainty. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_21887 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SVRPBench: A Realistic Benchmark for Stochastic Vehicle Routing Problem Heakl, Ahmed Shaaban, Yahia Salaheldin Takac, Martin Lahlou, Salem Iklassov, Zangir Artificial Intelligence Computational Engineering, Finance, and Science Machine Learning Robust routing under uncertainty is central to real-world logistics, yet most benchmarks assume static, idealized settings. We present SVRPBench, the first open benchmark to capture high-fidelity stochastic dynamics in vehicle routing at urban scale. Spanning more than 500 instances with up to 1000 customers, it simulates realistic delivery conditions: time-dependent congestion, log-normal delays, probabilistic accidents, and empirically grounded time windows for residential and commercial clients. Our pipeline generates diverse, constraint-rich scenarios, including multi-depot and multi-vehicle setups. Benchmarking reveals that state-of-the-art RL solvers like POMO and AM degrade by over 20% under distributional shift, while classical and metaheuristic methods remain robust. To enable reproducible research, we release the dataset and evaluation suite. SVRPBench challenges the community to design solvers that generalize beyond synthetic assumptions and adapt to real-world uncertainty. |
| title | SVRPBench: A Realistic Benchmark for Stochastic Vehicle Routing Problem |
| topic | Artificial Intelligence Computational Engineering, Finance, and Science Machine Learning |
| url | https://arxiv.org/abs/2505.21887 |