title: SVRPBench: A Realistic Benchmark for Stochastic Vehicle Routing Problem
summary: 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.
paper_authors._id: 6837e3400aa18c6f96fe4876
paper_authors.name: Ahmed Heakl
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-29T07:41:49.456Z
paper_authors._id: 656864e12d73834278a8dea7
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/656864e12d73834278a8dea7/sfAWS2eyPtFHb_2GZIypp.jpeg
paper_authors.fullname: Ahmed Heakl
paper_authors.type: user
paper_authors.user: ahmedheakl
paper_authors._id: 6837e3400aa18c6f96fe4877
paper_authors.name: Yahia Salaheldin Shaaban
paper_authors._id: 6837e3400aa18c6f96fe4878
paper_authors.name: Martin Takac
paper_authors._id: 6837e3400aa18c6f96fe4879
paper_authors.name: Salem Lahlou
paper_authors._id: 6837e3400aa18c6f96fe487a
paper_authors.name: Zangir Iklassov
paper_title: SVRPBench: A Realistic Benchmark for Stochastic Vehicle Routing Problem
paper_summary: 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.
paper_ai_summary: 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.
paper_ai_keywords: 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