Intelligent C-17 Load Planning for Flight Optimization
Name
MIT-LIN-151369.pdf
Description
Main Report
Size
2.59 MB
Format
Adobe PDF
Checksum (MD5)
bba6a78b94f2f2724ffaa526484ca60a
Author(s)
McAlister, Catherine
Jones, Mathew
McConville, Sean
Date Issued
February 17, 2026
Abstract
C-17 Globemaster III cargo capacity is significantly
underutilized, with many sorties transporting only a few pallets
despite the aircraft’s 170,900-pound payload capability. Historical
flight data analysis reveals inefficient scheduling practices that
increase operational costs, crew workload, and overall negatively
effect mission capability. This paper details the development
of an AI-powered optimization model to improve C-17 cargo
utilization and reduce required flight operations. We analyzed
historical C-17 transportation data and created both traditional
optimization algorithms and predictive AI models to determine
optimal flight scheduling for 3-week operational periods. The AI
model achieved 97.9% accuracy in predicting optimal flight count
requirements and 89.3% accuracy in predicting optimal flight
assignment for specific cargo, representing a 23% reduction in
total flights and a 15% increase in average cargo utilization.
These results demonstrate that data-driven flight scheduling
can significantly improve C-17 operational efficiency, reduce
costs across the airlift community, and enabling additional time
towards advanced training, contingency support, and critical
warfighter operations, ultimately increasing the lethality and
readiness of the Department of Defense.
Subjects
Artificial Intelligence, military aircraft, predictive models, machine learning, neural networks
MIT Department
Lincoln Laboratory
Persistent DSpace Link
