Physics-Aware, Operation-Aware Reinforcement Learning for Fuel-Saving Airline Fuel Loading Strategy

Airlines load more fuel than a flight is expected to burn, carrying contingency and discretionary reserves against delays, reroutes, and other operational variability. That margin is necessary for safety, but carrying it also costs fuel: extra weight burns extra fuel to carry, and current statistical fuel-planning methods do not adapt well to the specific conditions of an individual flight.

This project develops a physics-aware, operation-aware reinforcement learning framework that models the interdependence between fuel carried and fuel consumed, and integrates regulatory constraints and real operational variability directly into the planning decision. The aim is a smarter, more accurate way to determine how much fuel a specific flight actually needs.

Reducing unnecessary fuel loading in this way lowers fuel burn, operating costs, and carbon emissions, without compromising the safety margins the reserves exist to provide.

• Model the interdependence between fuel carried and fuel consumed across an individual flight, rather than treating the fuel load as fixed at departure.
• Integrate regulatory constraints and operational variability into the fuel-loading decision.
• Outperform current statistical fuel-planning methods under realistic operational conditions.
• Reduce unnecessary fuel consumption, operating costs, and carbon emissions without compromising safety.