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【背景】无人机凭借高度灵活的机动能力，正逐渐成为灾后应急物资运输的重要手段。应急物资需求通常呈现显著的优先级差异，为无人机物资配送方案的设计带来了严峻挑战。【目标】针对灾后“以卡车为基站、无人机多轮配送优先级应急物资”的应用场景，实现高优先级任务的快速响应，并兼顾整体配送效率。【方法】构建了一个集成优先级响应、容量约束与能量限制的应急物资配送模型。根据模型特征，设计迭代混合整数规划（IMIP）算法，在多轮调度层面实现优先级驱动的节点分配；设计定制遗传算法（CGA），在单轮路径内部进一步细化任务排序，使高优先级物资在更靠前次序被配送。【结果】IMIP在首轮优先级总和上略优，但频繁往返换电站导致总配送时间较长；CGA显著缩短高优先级需求点的配送时间并减少换电次数。CGA的全局优化策略在小规模场景下显著减少三级需求点平均配送时间、总配送时间；而IMIP在大规模场景下计算效率、加权优先级分数方面表现更佳。【应用】本研究为应急管理部门建立高效的车辆-无人机协同响应体系提供了理论依据与算法支撑，有助于实现灾后应急物资配送的高效、精准响应。
Abstract：[Background] Unmanned aerial vehicles(UAVs) are increasingly being employed in post-disaster emergency supply delivery owing to their high maneuverability. However, the demand for emergency supplies often exhibits pronounced priority differences, posing significant challenges for the UAV delivery scheme design. [Objective] Aims to optimize the scenario of post-disaster operations, in which trucks serve as base stations and UAVs conduct multi-round deliveries of prioritized emergency supplies, to ensure a rapid response to high-priority tasks while maintaining overall delivery efficiency. [Methods] An emergency delivery model was developed that integrated priority responsiveness, capacity constraints, and energy limitations. Based on the model characteristics, an iterative mixed-integer programming(IMIP) algorithm was designed to achieve priority-driven demand point allocation at the multi-round scheduling level. Simultaneously, a customized genetic algorithm(CGA) was proposed to refine task sequencing within individual routes, ensuring that high priority supplies were delivered earlier. [Results] The IMIP achieved a somewhat better performance in the cumulative priority of the first round; however, frequent returns to the battery-swapping station resulted in a longer total delivery time. By contrast, the CGA significantly reduced the delivery time of high priority demand points and lowered the number of battery swaps. Its global optimization strategy substantially shortened the average delivery time of third-priority demand points and total delivery time in small-scale scenarios, whereas IMIP demonstrated superior computational efficiency and weighted priority score in large-scale scenarios. [Application] This study provides theoretical and algorithmic support for emergency management agencies to establish an efficient truckUAV collaborative response system that facilitates a fast and precise delivery of emergency supplies post disasters.
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基本信息:
DOI：10.19961/j.cnki.1672-4747.2025.06.036
中图分类号:F251;U116;X4
引用信息:
[1]李钢,杨正烨,俞礼军.考虑需求优先级的基站卡车-无人机应急物资配送模型[J].交通运输工程与信息学报,2025,23(04):62-74.DOI:10.19961/j.cnki.1672-4747.2025.06.036.
基金信息:
中央高校基本科研业务费项目（2023ZYGXZR056）