A data-driven flight schedule optimization model considering the uncertainty of operational displacement

The slot allocation mechanism aims to match flight demand and airport resources from a strategic perspective. However, current research mainly focused on airlines' interests, ignoring the influencing factors that lead to primary delays, which makes the temporal and spatial distribution of flight schedules unreasonable. This paper proposes a data-driven approach to reduce operational delays at a strategic level by considering operational efficiency and airline interests. The displacement probability distribution between the actual execution time and the scheduled time is first mined from the historical operation data. We then develop a model with the ultimate objective of improving the punctuality rate and reducing the actual operational delays by minimizing the total operational displacement. In addition to considering the basic operational restrictions of airports, the model also introduces the corridor capacity of the terminal airspace surrounding an airport, reducing the delay caused by the corridor flow control to a certain extent. The proposed model is applied to Hangzhou Xiaoshan International Airport in China. The experimental results suggest that the optimized flight schedule can significantly reduce flight delay, conforms to airport operational restrictions, and maintains flight connectivity.