Abstract: The existing sequencing and scheduling models for arrival flight rarely consider the influence of uncertain factors.
This paper establishes a two-stage arrival sequencing and scheduling model with enhanced robustness based on
the characteristics of the point mergen system (PMS) that the continuous descent approach time conforms to the
Gaussian distribution. In the first stage, the uncertainty brought by random variables is described, design a static
sequencing and scheduling model with additional buffer is designed based on chance constrained programming
theory, and the optimal additional buffer is determined by taking flight time and order change as indicators. In
the second stage, in order to compensate for the situation that the flight time exceeds the buffer capacity caused
by unpredictable reasons, a heuristic algorithm based on sliding time windows is applied to dynamically adjust
the scheme in the first stage. Through Monte-Carlo simulation of continuous descent approach time in uncertain
scenarios, the simulation results show that the proposed two-stage model exhibits good performance in balancing
robustness and fairness compared with other models under high, medium and low traffic densities.

Key words: air traffic flow management, arrival sequencing, point merge system (PMS), two-stage programming, MonteCarlo simulation