Intelligent C-17 Load Planning for Flight Optimization

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.