UAV Fleet Configuration for Wind-Exposed Truck–Drone Collaborative Delivery: A Paired Comparative Study
Chaofeng Wang, Shengming Dai, Jie Luo, Longfei ZhangUAV fleet configuration for truck–drone collaborative delivery requires deployment-oriented drone specification choice under wind. In this study, a UAV configuration is defined as a specific hardware parameter bundle comprising cruise speed, battery-block count, and associated mass and energy specifications. Nominal cruise speed or battery-block count alone do not predict synchronized system performance. Nine UAV configurations are compared under a common experimental protocol and the same heuristic on 60 mFSTSP instances (10, 25, and 50 customers) and nine wind scenarios, using paired makespan gaps relative to a baseline of six battery blocks and a 20 m/s cruise speed (Configuration 5). Wind affects drone outcomes through flight time and through expansion or contraction of the wind-feasible sortie set, which reshapes customer allocation and truck–drone synchronization. UAV performance is governed by a time–energy balance rather than any single nominal attribute, and no universally best drone configuration emerges. Balanced high-support UAV configurations are most robust at medium and large customer scales, though rankings remain scenario-dependent. Validation using real-world operational data under three wind conditions confirms the simulation findings, with model predictions achieving acceptable accuracy (3–6% deviation for makespan and energy consumption) and demonstrating wind-aware configuration superiority and practical applicability. The evidence supports predeployment drone fleet screening by expected wind exposure and service scale for effective drone deployment planning.