UAV Fleet Configuration for Wind-Exposed Truck–Drone Collaborative Delivery: A Paired Comparative Study

UAV 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.

Authors

Institutions

Publication Details

Journal
Drones
Published
2026-08-17
DOI
https://doi.org/10.3390/drones10080627
Primary Topic
UAV Applications and Optimization
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

UAV Fleet Configuration for Wind-Exposed Truck–Drone Collaborative Delivery: A Paired Comparative Study

WANG Chaofeng, Shengming Dai, Jie Luo, Longfei Zhang
Drones
UAV Applications and Optimization
article

UAV Fleet Configuration for Wind-Exposed Truck–Drone Collaborative Delivery: A Paired Comparative Study

WANG Chaofeng, Shengming Dai, Jie Luo, Longfei Zhang
article en

Abstract

UAV 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.

DronesVol. 10(8)
Civil Aviation Flight University of China (CN)
National Natural Science Foundation of China, Fundamental Research Funds for the Central Universities
Affordable and clean energy
Openalex Percentile: Top 6%
UAV Applications and Optimization
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.