# SVRPBench: A Realistic Benchmark for Stochastic Vehicle Routing Problem

arXiv: https://arxiv.org/abs/2505.21887 · HTML: https://mlsanity.com/paper/2505.21887/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 38 references that are in the map.
- First posted on arXiv 28 May 2025.

## Builds on (most cited first, 10 of 10)

- [Attention, Learn to Solve Routing Problems!](https://mlsanity.com/paper/1803.08475/) (2018)
- [POMO: Policy Optimization with Multiple Optima for Reinforcement Learning](https://mlsanity.com/paper/2010.16011/) (2020)
- [Reinforcement Learning for Solving the Vehicle Routing Problem](https://mlsanity.com/paper/1802.04240/) (2018)
- [Neural Combinatorial Optimization with Heavy Decoder: Toward Large Scale Generalization](https://mlsanity.com/paper/2310.07985/) (2023)
- [Hybrid Genetic Search for the CVRP: Open-Source Implementation and SWAP* Neighborhood](https://mlsanity.com/paper/2012.10384/) (2020)
- [PyVRP: a high-performance VRP solver package](https://mlsanity.com/paper/2403.13795/) (2023)
- [Self-Guiding Exploration for Combinatorial Problems](https://mlsanity.com/paper/2405.17950/) (2024)
- [Reinforced Lin-Kernighan-Helsgaun Algorithms for the Traveling Salesman Problems](https://mlsanity.com/paper/2207.03876/) (2022)
- [Reinforcement Learning for Solving Stochastic Vehicle Routing Problem](https://mlsanity.com/paper/2311.07708/) (2023)
- [Adaptive Memory Procedure for Solving Real-world Vehicle Routing Problem](https://mlsanity.com/paper/2403.04420/) (2024)

## Built on by (most cited first, 2 of 2)

- [Latency-Aware Bid Acceptance under Operational Feasibility: A Public Benchmark with Hindsight Ceilings](https://mlsanity.com/paper/2607.07343/) (2026)
- [When Evidence Changes the Subject: Subject-Typed Claim Licensing for Learned Routing](https://mlsanity.com/paper/2609.33505/) (2026)

## Abstract (arXiv)

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.