Collaborative Decision-Making for Departure Pushback and Taxiing to Enhance Airport Surface Efficiency

Airport surface scheduling at multi-runway airports is a complex system engineering task that balances efficiency, safety, and sustainability, making it a key research focus in the field of air traffic management. This study proposes a cosine curve-based pushback rate control strategy and a collaborative pushback and taxiing decision-making method for departure aircraft pushback and taxiing. Additionally, the Markov decision process under dynamic pushback control at dual-runway airports is analyzed. An adaptive departure operation optimization model is established. This model considers path conflicts, fuel consumption, taxiway queuing, and runway occupancy during an aircraft departure process, enhancing the operational efficiency in the temporal and spatial dimensions. In the aircraft departure process, a genetic simulated annealing algorithm with nested Markov state transitions is proposed as the optimization algorithm, utilizing a Q-learning algorithm to adaptively adjust crossover and mutation parameters. The effectiveness of the proposed model and algorithm is validated through simulation experiments at Beijing Capital International Airport. Results indicate that this approach can significantly reduce the estimated taxiing fuel-related operating cost by 17.53% in the simulated case, shorten average taxiway waiting time by 56.24%, and decrease average taxi completion time by 20.81%, thereby improving overall airport operational efficiency.