应急物流中的多仓库卡车与无人机协同配送研究数据集

针对情景演变下的物资动态需求问题，面向航空、铁路、公路及无人机等多种运输方式建立应急物资动态调度模型，实现群智感知获取实时多维数据基础上的多演化阶段、多运输方式、多物资品类的物流优化调度。为研究“卡车+无人机”协同配送的路径优化问题，选取成都市青白江区、新都区、成华区等区域为配送点依据，并利用实际数据和仿真结合的方法进行参数设置。配送网络包括4个城市配送中心和40个临时配送点。各临时配送点有对应的经纬度、需求量、无人机可达性、小卡车可达性信息。配送点的经纬度通过百度地图获取，需求量及可达性基于实际需求设置，数据量12KB。

Aiming at the dynamic demand problem of emergency supplies under scenario evolution, this study establishes a dynamic emergency supply dispatch model for multiple transportation modes including aviation, railway, highway and unmanned aerial vehicle (UAV), to realize optimal logistics scheduling across multiple evolution stages, transportation modes and supply categories based on real-time multi-dimensional data collected via crowdsourcing perception. To investigate the route optimization problem of "truck + UAV" collaborative delivery, regions such as Qingbaijiang District, Xindu District and Chenghua District of Chengdu City are selected as the basis for delivery points, and parameter setting is conducted by combining actual data and simulation methods. The delivery network consists of 4 urban distribution centers and 40 temporary delivery points. Each temporary delivery point has corresponding information including latitude and longitude, demand quantity, UAV accessibility and small truck accessibility. The latitude and longitude of the delivery points are obtained via Baidu Maps, the demand quantity and accessibility are set based on actual demands, and the total data volume is 12 KB.