Stochastic programming model and algorithm for flight schedule optimization

Abstract

To solve the strategic flight schedule optimization problem for multiple airport and multiple operation days, a two-stage stochastic programming model is established. Flight schedule optimization is made in the first stage of the model, and the tactical flight delay decision is made in the second stage with consideration of the impact of the uncertain operational airport capacity at the tactical stage. The arrival and departure service rates are also optimized as decision variables in the second stage so as to simulate the actual operation characteristics of the airports and more accurately estimate the operation delay at the tactical stage. The model’s objective function considers the minimum deviation of flight scheduled time in the strategic stage and the expected delay in the tactical stage. A hybrid evolutionary algorithm is designed to solve the model by two-stage decomposition. The model is simulated and verified using the flight plan data of Beijing Capital Airport and Guangzhou Baiyun Airport. The effect of the model in optimizing flight scheduled time and reducing the mean value of operational delay in the tactical phase under different capacity scenarios is analyzed, and the effectiveness of the model and the hybrid evolutionary algorithm is verified.