首个面向城市级随机车辆路径问题的真实基准，挑战现有算法泛化能力。
SVRPBench: A Realistic Benchmark for Stochastic Vehicle Routing Problem
- 构建高保真随机动态仿真，涵盖拥堵、延迟、事故等真实物流场景。
- 1000客户规模下，主流强化学习方法性能下降超20%，传统方法更稳定。
- 适合研究鲁棒优化、真实场景调度的学者与工业界从业者。
在现实物流中，应对不确定性是路径规划的核心挑战，但多数基准仍基于静态理想假设。本文提出SVRPBench，首个开源的城市级随机车辆路径问题真实基准，涵盖500多个实例，最多支持1000名客户。该基准模拟真实配送条件：随时间变化的交通拥堵、对数正态分布的延误、概率性交通事故，以及基于实证的时间窗（住宅与商业客户）。其生成管道可构建多样化的约束复杂场景，包括多枢纽、多车辆配置。基准测试显示，当前先进的强化学习求解器（如POMO和AM）在分布外场景下性能下降超过20%，而经典与元启发式方法表现更稳健。为促进可复现研究，我们发布数据集与评估套件。SVRPBench呼吁社区设计能超越合成假设、适应真实不确定性的求解器。
原文摘要 · Abstract (English)
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.
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