多智能体无训练城市食品配送系统使用抗灾UMST网络
Multi-Agent Training-free Urban Food Delivery System using Resilient UMST Network
AI总结：
本文提出UMST方法构建高效且稳健的配送网络，通过随机边扰动生成多个MST并合并，减少边数同时保持多条路径。UMST在多个美国城市中实现比全连接网络少20-40倍的边数，且订单捆绑率达75-83%，性能优于MADDPG等基于学习的基线方法。
摘要：
配送系统已成为城市生活的核心部分，支持食品、药品等需求。然而传统物流网络仍脆弱，难以适应道路封闭、事故和需求变化。在线食品配送（OFD）平台已成为城市物流的基石，预计2030年市场规模将超过5000亿美元。设计高效且稳健的配送网络仍是重大挑战：完全连接图提供灵活性但计算上不可行，而单一最小生成树（MST）效率高但易受破坏。我们提出联合最小生成树（UMST）方法构建配送网络，生成多个MST并通过随机边扰动合并，产生边数远少于完全连接网络但保持多个配送热点之间替代路径的图。在多个美国城市中，UMST实现比完全连接图少20-40倍的边数，同时允许显著订单捆绑，参与率75-83%。与包含MADDPG和图神经网络在内的基于学习的基线方法相比，UMST在无需训练的情况下实现88-96%的成功率和44-53%的距离节省，执行速度达30倍快，同时保持可解释的路由结构。其结构效率和操作灵活性的结合为城市配送网络提供了可扩展且稳健的基础。
英文摘要：
Delivery systems have become a core part of urban life, supporting the demand for food, medicine, and other goods. Yet traditional logistics networks remain fragile, often struggling to adapt to road closures, accidents, and shifting demand. Online Food Delivery (OFD) platforms now represent a cornerstone of urban logistics, with the global market projected to grow to over 500 billion USD by 2030. Designing delivery networks that are efficient and resilient remains a major challenge: fully connected graphs provide flexibility but are computationally infeasible at scale, while single Minimum Spanning Trees (MSTs) are efficient but easily disrupted. We propose the Union of Minimum Spanning Trees (UMST) approach to construct delivery networks that are sparse yet robust. UMST generates multiple MSTs through randomized edge perturbations and unites them, producing graphs with far fewer edges than fully connected networks while maintaining multiple alternative routes between delivery hotspots. Across multiple U.S. cities, UMST achieves 20--40$\times$ fewer edges than fully connected graphs while enabling substantial order bundling with 75--83% participation rates. Compared to learning-based baselines including MADDPG and Graph Neural Networks, UMST delivers competitive performance (88-96% success rates, 44-53% distance savings) without requiring training, achieving 30$\times$ faster execution while maintaining interpretable routing structures. Its combination of structural efficiency and operational flexibility offers a scalable and resilient foundation for urban delivery networks.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
