  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.
