To address the protracted blood transportation time prevalent in contemporary urban settings, we proposed a location-routing optimization problem tailored to the distribution of blood within intricate road networks. This involved a comprehensive assessment that encompassed the judicious selection of sites for both stations and blood centers, coupled with the meticulous planning of delivery routes for unmanned aerial vehicles (UAVs) that orchestrate the transportation of blood. First, a model was formulated to minimize the overall cost, including transportation expenses, costs associated with the site, and other relevant costs related to blood transportation vehicles coordinated by UAVs. Subsequently, a two-stage hybrid heuristic algorithm was designed based on the distinctive characteristics of the problem at hand. Moreover, an enhanced k-means algorithm was employed to generate clustering schemes, utilizing the centroid method to address the challenge of location selection for delivery sites effectively. A genetic algorithm enhanced with adaptive operators was employed to address the challenging large-scale NP-hard problem associated with route planning in intricate urban road networks. The results indicated that, compared to the traditional blood delivery model using vehicles, the total blood transportation cost decreased by 12.65% and the overall delivery time was reduced by 37.5% with the adoption of drone-assisted delivery; ultimately, case and sensitivity analyses were conducted to investigate the impact of variables including the number of blood transportation vehicles, UAVs, driver wages, and unit costs of blood transportation vehicles on the location-routing problem.

为了解决当代城市环境中普遍存在的血液运输时间延长的问题，我们提出了一个针对复杂道路网络中血液分布的位置路由优化问题。这涉及到全面评估，包括明智地选择站点和血液中心的地点，以及精心规划用于协调血液运输的无人机（UAV）的运输路线。首先，制定了一个模型来最小化总体成本，包括运输费用、场地相关成本以及与无人机协调的血液运输车辆相关的其他相关成本。随后，根据当前问题的独特特征，设计了两阶段混合启发式算法。此外，采用增强的k-means算法来生成聚类方案，利用质心方法有效地解决送货站点位置选择的挑战。采用自适应算子增强的遗传算法来解决与复杂城市道路网络中的路线规划相关的具有挑战性的大规模 NP 难题。结果表明，与传统的车辆血液输送模式相比，采用无人机辅助输送，血液运输总成本降低了12.65%，整体输送时间缩短了37.5%；最终，通过案例分析和敏感性分析，研究了血液运输车辆数量、无人机数量、驾驶员工资以及血液运输车辆单位成本等变量对位置路径问题的影响。