SVRPBench: A Realistic Benchmark for Stochastic Vehicle Routing Problem
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.
6837e3400aa18c6f96fe4876
Ahmed Heakl
claimed_verified
2025-05-29T07:41:49.456Z
656864e12d73834278a8dea7
https://cdn-avatars.huggingface.co/v1/production/uploads/656864e12d73834278a8dea7/sfAWS2eyPtFHb_2GZIypp.jpeg
Ahmed Heakl
user
ahmedheakl
6837e3400aa18c6f96fe4877
Yahia Salaheldin Shaaban
6837e3400aa18c6f96fe4878
Martin Takac
6837e3400aa18c6f96fe4879
Salem Lahlou
6837e3400aa18c6f96fe487a
Zangir Iklassov
SVRPBench: A Realistic Benchmark for Stochastic Vehicle Routing Problem
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.
SVRPBench introduces a new benchmark for vehicle routing under uncertainty, simulating realistic urban conditions and highlighting the limitations of state-of-the-art RL solvers.
vehicle routing SVRPBench time-dependent congestion log-normal delays probabilistic accidents multi-depot multi-vehicle state-of-the-art RL solvers POMO AM distributional shift classical methods metaheuristic methods