SVRPBench: A Realistic Benchmark for Stochastic Vehicle Routing Problem
Authors: name: Ahmed Heakl, name: Yahia Salaheldin Shaaban, name: Martin Takac, name: Salem Lahlou, name: Zangir Iklassov
Organization: Mohamed Bin Zayed University of Artificial Intelligence

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.