SVRPBench：一個針對隨機車輛路徑問題的真實基準測試平台

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

在不確定性下的穩健路徑規劃是現實物流的核心，然而大多數基準測試都假設了靜態、理想化的環境。我們提出了SVRPBench，這是首個捕捉城市規模下車輛路徑規劃中高保真隨機動態的開放基準測試。涵蓋超過500個實例，最多可達1000名客戶，它模擬了真實的配送條件：時間依賴的交通擁堵、對數正態分佈的延遲、概率性事故，以及基於經驗的住宅和商業客戶時間窗口。我們的流程生成了多樣化且約束豐富的場景，包括多倉庫和多車輛設置。基準測試顯示，如POMO和AM等最先進的強化學習求解器在分佈偏移下性能下降超過20%，而經典和元啟發式方法則保持穩健。為了促進可重現的研究，我們公開了數據集和評估套件。SVRPBench向社區發起挑戰，要求設計出能夠超越合成假設並適應現實世界不確定性的求解器。

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.