SVRPBench: A Realistic Benchmark for Stochastic Vehicle Routing Problem
Ahmed Heakl, Yahia Salaheldin Shaaban, Salem Lahlou, Martin Takáč, Zangir Iklassov
Abstract
Robust routing under uncertainty is central to real-world logistics, yet most benchmarks assume static, idealized settings. We present \texttt{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 ([Huggingface](https://huggingface.co/datasets/MBZUAI/svrp-bench)) and evaluation suite ([Github](https://github.com/yehias21/vrp-benchmarks)). SVRPBench challenges the community to design solvers that generalize beyond synthetic assumptions and adapt to real-world uncertainty.
BibTeX
@inproceedings{
heakl2025svrpbench,
title={{SVRPB}ench: A Realistic Benchmark for Stochastic Vehicle Routing Problem},
author={Ahmed Heakl and Yahia Salaheldin Shaaban and Salem Lahlou and Martin Tak{\'a}{\v{c}} and Zangir Iklassov},
booktitle={The Thirty-ninth Annual Conference on Neural Information Processing Systems Datasets and Benchmarks Track},
year={2025},
url={https://openreview.net/forum?id=yADrJyaBJl}
}