Research on slot allocation for airport network in the presence of uncertainty

Abstract

In this paper, a slot allocation model for airport network in the presence of uncertainty is proposed. The airport resource planning problem is modeled as a process from an aggregate level and an individual level for multiple days' slot scheduling in order to consider capacity uncertainty at airports characterized by meteorological scenarios. The aggregate level uses a two-stage model to minimize the sum of displacement cost and delay cost in the worst-case scenario using Benders’ Decomposition-based algorithm. At the individual level, an integer linear programming model is developed based on the results from the aggregate level to provide specific departure slot and arrival slot for each flight. An airport network composed of 15 coordinated airports in China is studied and shows that by experimental analysis the overall optimization method is capable of providing robust and efficient solutions for slot allocation and benefits from high computational efficiency. The outperformance of the two-stage optimization method from the aggregate level is also illustrated in comparison with the one-stage optimization method. Finally, recommendations for the slot allocation policy in practice are provided.