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【背景】在低空经济持续发展的背景下，无人机依托其高机动性的优势，逐步应用在应急救援及高价值货物的配送任务中。然而，现有研究多聚焦于微观路径规划，针对设施布局、干线飞行速度与风险控制等要素协同优化的宏观网络设计研究尚不充分，且对时效-风险的非线性耦合机理尚缺乏深入探讨。【目标】旨在构建一个结合设施选址与网络规划的集合模型。该模型刻画了飞行速度与失效风险的耦合关系，并将由货物价值波动带来的风险敞口，纳入无人机高价值货物配送网络的规划决策过程。【方法】在混合整数线性规划框架下，将选址、分配与干线速度联合起来决策，并在目标函数中显式表征速度选择与风险损失之间的关联，以反映时效性与风险之间的权衡关系。模型引入预算式鲁棒优化方法，通过鲁棒预算参数对由货物价值波动带来的风险敞口进行调节约束，以便在统一框架下刻画运行决策与参数不确定性的耦合关系。【结果】数值实验表明，混合速度策略能够有效应对局部供需刚性约束，并量化刻画了以配送时效换取风险缓释的权衡机理。外样本蒙特卡洛检验显示，鲁棒方案在名义成本平均增加约0.33%的基础上，在约85%的随机算例中改善了尾部风险暴露水平，且高分位损失的平均降幅约为4.5%。【应用】所构建的鲁棒优化框架深入剖析了速度调节与设施布局在风险控制中的协同机制，能够为复杂空域环境下高价值货物无人机配送网络在时效、风险与成本间的多维权衡，提供科学的决策支持。
Abstract：[Background] Against the backdrop of the sustained development of the low-altitude economy， unmanned aerial vehicles (UAVs)， leveraging their high mobility， are increasingly applied in emergency response and high-value goods delivery. However， existing literature predominantly focuses on micro-level routing. Research on macro-level network design regarding the synergistic optimization of facility layout， trunk-flight speed， and risk control remains insufficient， with a notable lack of in-depth exploration into the non-linear coupling mechanism between timeliness and risk. [Objective] This study develops an integrated facility-location and network-planning model. The model explicitly represents the coupling between flight speed and failure risk， and incorporates risk exposure induced by fluctuations in goods value into the planning of UAV networks for delivering high-value goods. [Method] Within a mixed-integer linear programming framework， we jointly optimize facility location， customer allocation， and discrete trunk-flight speed choices， and explicitly model the interaction between speed decisions and risk-related losses in the objective function to represent the timeliness–risk trade-off. A budgeted robust optimization approach is embedded， where a robustness budget parameter offers adjustable control over the level of protection against fluctuations in goods value and allows operational decisions and uncertainty to be addressed within a unified modeling framework. [Results] Numerical experiments demonstrate that the mixed-speed strategy effectively addresses rigid constraints in local supply and demand， quantitatively characterizing the trade-off mechanism of trading delivery timeliness for risk mitigation. Out-of-sample Monte Carlo validation reveals that the robust solution improves tail risk exposure in approximately 85% of random instances with a marginal average nominal cost increase of 0.33%， achieving an average reduction of about 4.5% in high-quantile losses. [Application] The proposed robust optimization framework provides an in-depth analysis of the synergistic mechanism between speed adjustment and facility layout in risk control， offering scientific decision support for the multi-dimensional trade-offs among timeliness， risk， and cost in high-value goods drone delivery networks under complex airspace environments.
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基本信息:
DOI：10.19961/j.cnki.1672-4747.2026.02.002
中图分类号:V353;F252
引用信息:
[1]施浩然,蹇明,吴玥含.面向高价值货物的无人机配送网络鲁棒规划[J].交通运输工程与信息学报().DOI:10.19961/j.cnki.1672-4747.2026.02.002.
基金信息:
四川省重点研发项目（2025YFCY0020）
2026-02-26
2026-02-26
2026-02-26