Aiming at the shortage of the airport gate resources and the disturbance caused by the actual flight arrival and departure time deviation from the planned time, a gate assignment scheduling method was proposed by adding buffer time between the adjacent flights in the same gate. Firstly, a robust gate assignment model with a goal to achieve minimum gate idle time and apron occupancy time was established. Then, a Lagrangian relaxation optimization algorithm based on double targets was designed, and the dual problem in the Lagrangian algorithm was solved by using the subgradient algorithm. Based on the operation data of a hub airport in China, the simulation results show that, compared with those of the original gate assignment scheme, the gate usage amount and the gate idle time of the proposed method is respectively reduced by 15.89% and 7.56%, the gate occupancy rate of the optimization scheme of proposed method is increased by 18.72% and the conflict rate is reduced to 3.57%, proving that the proposed method achieves the purpose of effectively improving the utilization and robustness of airport gates.

Concerning the problems of estimating the service time of airport flight support, and the particularity, complexity, and influence factors' uncertainty of flight support service process, an estimation model of flight support service time based on Bayesian Network (BN) was proposed. The knowledge of aviation experts and the machine learning of historical data were combined by the proposed model, and the incremental learning characteristic of BN was used to adjust the BN model dynamically, so as to make itself adapt to new conditions and constantly update the service time estimates of flight support. By using the data selected from a large domestic hub airport information system, the proposed BN model was trained via the Expectation Maximization (EM) algorithm to obtain the test results. The analysis of experimental results and model evaluation show that the proposed method can effectively estimate the service time of flight support and has higher accuracy. In addition, the sensitivity analysis demonstrates that the flight density during flight arrival time has the strongest influence on flight support service time.