交通运输系统工程与信息 ›› 2026, Vol. 26 ›› Issue (3): 166-175.DOI: 10.16097/j.cnki.1009-6744.2026.03.016

• 综合交通运输体系 • 上一篇    下一篇

基于模糊最大覆盖模型的无人机应急配送中心选址研究

万莉莉* ,徐舒梦,黄嘉慧,张庆阳,袁振宇   

  1. 南京航空航天大学,民航学院,南京211000
  • 收稿日期:2026-01-14 修回日期:2026-02-14 接受日期:2026-03-12 出版日期:2026-06-25 发布日期:2026-06-23
  • 作者简介:万莉莉(1981—),女,江苏东台人,讲师。
  • 基金资助:
    江苏省基础研究专项资金 (BK20253036)。

Fuzzy Maximal Covering Model for Drone Emergency Distribution Center Selection

WAN Lili*, XU Shumeng, HUANG Jiahui, ZHANG Qingyang,YUAN Zhenyu   

  1. College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 211000, China
  • Received:2026-01-14 Revised:2026-02-14 Accepted:2026-03-12 Online:2026-06-25 Published:2026-06-23
  • Supported by:
    Natural Science Foundation of Jiangsu Province (BK20253036)。

摘要: 针对突发灾害救援中物资需求及无人机覆盖半径的不确定问题,本文提出一种基于模糊理论的无人机应急配送中心选址优化方法。基于地理数据,利用k-means算法识别需求点及候选设施位置。采用三角与梯形模糊数刻画参数不确定性,引入可信度约束,构建以覆盖需求量最大、总成本最小和空间公平性最优为目标的模糊最大覆盖选址模型(FMCLP)。设计嵌入模糊模拟的混合模拟退火算法,利用蒙特卡洛采样处理模糊参数,通过局部搜索与自适应冷却机制实现全局寻优。以南京市江宁区为例进行对比实验和灵敏度分析。结果表明:相较于经典确定性模型,该方案在同等设施规模下覆盖需求量提升82个单位,总成本降低6.14万元,最大未覆盖距离缩减7.63km,且在设施失效时具有更优的鲁棒性;确定覆盖需求量、总成本与公平性的最优权重组合为(0.7,0.2,0.1),并识别出区域核心枢纽设施。研究结果验证了模型在复杂不确定环境下的适用性,可为城市无人机应急物流网络规划提供科学决策依据。

关键词: 航空运输, 设施选址, 模糊最大覆盖模型, 无人机(UAV), 应急物流, 模拟退火算法

Abstract: To address uncertainties in material demand and drone coverage radii during disaster relief, this paper proposes a fuzzy optimization method for locating drone emergency distribution centers. Using k-means clustering on geographical data to identify candidate sites, the study uses triangular and trapezoidal fuzzy numbers to characterize parameter uncertainty. A Fuzzy Maximal Covering Location Problem (FMCLP) is defined with credibility constraints, aiming to maximize covered demand, minimize total costs, and optimize spatial equity. A Hybrid Simulated Annealing (HSA) algorithm, embedded with fuzzy simulation and Monte Carlo sampling, is designed to achieve global optimization via local search and adaptive cooling. A case study in Jiangning District of Nanjing city demonstrates that compared to deterministic models, the proposed method increases covered demand by 82 units, reduces total costs by 61 400 yuan, and shortens the maximum uncovered distance by 7.63 km, while exhibiting superior robustness against facility failure. The optimal weight combination for coverage, cost, and equity is determined as (0.7, 0.2, 0.1). The findings validate the model's applicability in complex, uncertain environments, providing a scientific basis for urban drone logistics planning.

Key words: air transportation, facility location, fuzzy maximal covering model, unmanned aerial vehicle (UAV), emergency logistics, simulated annealing algorithm

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