气象约束下山区生鲜电商无人机协同集货路径优化

Collaborative Pickup Path Optimization for Mountain Fresh E-Commerce UAVs under Meteorological Constraints

摘要: 为解决山区生鲜电商“最先一公里”物流中时效与损耗的核心矛盾，本研究采用一种卡车与无人机协同的集货新模式。构建了一个集成气象约束与站内并行作业的一体化决策框架，将天气因素对无人机效能与生鲜品质的动态影响量化并纳入路径调度与鲜度控制。通过同步优化物流节奏，模型旨在最小化由运营成本与货损成本构成的总系统成本。基于标准算例与分级天气场景的数值实验表明，该协同优化方案能在中高风级等不利条件下，显著降低货损并有效控制总成本，同时保持稳定的计算性能。本研究为生鲜电商平台在复杂地形下运营低空物流提供了可操作的决策支持工具，对提升供应链韧性与效率具有重要的管理启示。

Abstract: To address the core contradiction between timeliness and loss in the “first-mile” logistics of fresh produce e-commerce in mountainous areas, this study proposes a novel collaborative pickup mode integrating trucks and unmanned aerial vehicles (UAVs). An integrated decision-making framework incorporating meteorological constraints and in-station parallel operations is established, which quantifies the dynamic impacts of weather factors on UAV efficiency and fresh produce quality, and incorporates them into route scheduling and freshness control. By synchronously optimizing the logistics rhythm, the model aims to minimize the total system cost consisting of operational costs and cargo damage costs. Numerical experiments based on standard test instances and graded weather scenarios demonstrate that the proposed collaborative optimization scheme can significantly reduce cargo damage, effectively control the total cost, and maintain stable computational performance under adverse conditions such as moderate to high wind speeds. This study provides an operational decision support tool for fresh produce e-commerce platforms to operate low-altitude logistics in complex terrain, and offers important managerial implications for enhancing supply chain resilience and efficiency.