Probabilistic Prediction and Multi-Resource Stochastic Optimized Scheduling of Departure Operations on the Airport Surface

In response to the intricate interplay of uncertain factors within the airport system, which leads to challenges in predicting and optimizing departure operations in the airport, this study investigates a probabilistic prediction of operation times for departure flights, as well as an integrated stochastic optimized scheduling method considering multiple resources on the airport surface under the impact of these uncertain operation times. A probabilistic predictive model for departure operations is formulated, employing a combination of random forest regression and kernel density estimation. This model was used to obtain the prediction results of the probabilistic distribution of various departure operation times under uncertainty. Chance constraints are introduced to control the stability of the scheduling strategies including the push back time and takeoff time scheduling. Taking minimizing operational delays and maximizing the probability of satisfying the chance constraints as the optimization objectives, a comprehensive integrated stochastic scheduling model is developed for airport departure operations under uncertainty. Lastly, the method is validated and analyzed using Kunming Changshui International Airport as a case study. Experimental results firstly demonstrate that the proposed method is capable of effectively predict the probability distributions of estimated push back times and taxi times for departure flights. The continuous ranked probability scores of the estimated push back times and taxi times are 9.25 and 1.21 respectively, indicating that the estimated push back times of the departure flights is more uncertain than the taxi times. Then, results of the integrated airport surface departure scheduling model show that the model can comprehensively consider the minimization of flight delays and the satisfaction of the two chance constraints, and obtain the Pareto optimal solutions achieving balance between strategy efficiency and strategy stability.