SVRPBench：随机车辆路径问题的现实基准测试平台

SVRPBench: A Realistic Benchmark for Stochastic Vehicle Routing Problem

May 28, 2025

作者: Ahmed Heakl, Yahia Salaheldin Shaaban, Martin Takac, Salem Lahlou, Zangir Iklassov

cs.AI

摘要

在不确定性下实现稳健路径规划是现实物流的核心，然而大多数基准测试都基于静态、理想化的设定。我们推出了SVRPBench，这是首个捕捉城市规模车辆路径规划中高保真随机动态的开放基准。该基准涵盖超过500个实例，最多涉及1000名客户，模拟了真实的配送条件：随时间变化的交通拥堵、对数正态分布的延误、概率性事故，以及基于实证的住宅与商业客户时间窗口。我们的流程生成了多样且约束丰富的场景，包括多仓库和多车辆配置。基准测试显示，如POMO和AM等最先进的强化学习求解器在分布偏移下性能下降超过20%，而经典方法和元启发式算法则保持稳健。为促进可重复研究，我们公开了数据集和评估套件。SVRPBench向社区发起挑战，旨在设计出能够超越合成假设、适应现实世界不确定性的求解器。

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.