Machine Learning-Enhanced Aircraft Landing Scheduling under
  Uncertainties
Yutian Pang Peng Zhao Jueming Hu Yongming Liu
This paper addresses aircraft delays, emphasizing their impact on safety and
financial losses. To mitigate these issues, an innovative machine learning
(ML)-enhanced landing scheduling methodology is proposed, aiming to improve
automation and safety. Analyzing flight arrival delay scenarios reveals strong
multimodal distributions and clusters in arrival flight time durations. A
multi-stage conditional ML predictor enhances separation time prediction based
on flight events. ML predictions are then integrated as safety constraints in a
time-constrained traveling salesman problem formulation, solved using
mixed-integer linear programming (MILP). Historical flight recordings and model
predictions address uncertainties between successive flights, ensuring
reliability. The proposed method is validated using real-world data from the
Atlanta Air Route Traffic Control Center (ARTCC ZTL). Case studies demonstrate
an average 17.2% reduction in total landing time compared to the
First-Come-First-Served (FCFS) rule. Unlike FCFS, the proposed methodology
considers uncertainties, instilling confidence in scheduling. The study
concludes with remarks and outlines future research directions.