Trajectory Optimization with Constraints Using Neural Networks and Genetic Algorithms

Improving the flight trajectory in climb phases, such as in the continuous climb operation, has the potential to reduce fuel consumption. In this paper, we propose an approach that combines a neural network and genetic algorithms to determine the fuel-optimal vertical climb profile under a given flight envelope. As a case study, this method was utilized for the climb phase of a Boeing 787. The results indicate that, from a fuel-consumption perspective, a steep climb with a climb rate of approximately 3000 ft/min to the cruising altitude is desirable. This implies that staying at a high altitude for a long time is effective in reducing fuel consumption. Plotting the vertical profile on the map as a case study of climb trajectory for Narita International Airport indicates that the profile is possible with a vertical separation of 1000 ft with arrival traffic and overflight around the airport. Finally, we discuss the limitations of the optimization method and future challenges.