Enhancing Airline Operational Efficiency through an Optimized Mixed Fleet Deployment Methodology
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Abstract
Rising fuel prices and increasingly restrictive aviation emissions regulations necessitate airlines to optimize their diverse fleet operations under complex operational constraints, ensuring high operational efficiency. The multidimensional complexity of airline flight scheduling necessitates a robust, data-driven framework that systematically optimizes fleet deployment to meet growing demands for passenger and cargo transport while minimizing environmental impact. To address this challenge, this research introduces a methodology that utilizes aircraft cost-curve models based on operating stage lengths, overlaid on current flight schedules, to visualize underperforming fleet deployments. An objective function is formulated to balance network fuel efficiency and passenger throughput, incorporating real-world scheduling and logistical constraints. Fleet redeployment is achieved by solving this constrained optimization problem through aircraft swaps, implemented using a mixed-integer linear programming approach enabled by the commercial solver Gurobi. This approach facilitates the dynamic reassignment of aircraft within the existing schedule, targeting minimal fuel burn and emissions while maintaining service levels. Finally, the proposed methodology is validated using operational data from a major U.S. airline, demonstrating a 3 to 5% reduction in network fuel consumption and associated CO2 emissions compared to the original deployment on a single-day flight schedule. Hence, this framework provides airlines with a practical decision-support tool for strategic fleet redeployment, promoting network efficiency and sustainability.
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2026-01
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