To address the issues of inefficient resource allocation and severe ground congestion at hub airports during aircraft turnaround operations, a two-stage optimization model is constructed to coordinate the scheduling of stands and ground support vehicles. The model focuses on analyzing the scheduling rules, operational patterns, and collaborative mechanisms between stand allocation and ground support vehicles, taking into account the coupling relationship between airport operational and support resources. The pre-allocation of stands is conducted under the constraints of limited support resources, and the results are used as inputs for ground support vehicle scheduling. This combined optimization of stands and vehicle resources enhances the overall resource efficiency. The NSGA-II algorithm, combining local search strategies (LS-NSGA-II), is used to solve the model. Computational experiments conducted at Shenzhen Airport show some improvements: For the stand allocation model, the model incorporates ground support service constraints for tow tractors and driving distances for ferry buses, thereby avoiding potential service conflicts and resource wastage. Secondly, for the scheduling of vehicles, by analyzing the operational patterns and service characteristics of different vehicles, the model improved vehicle utilization efficiency by 37.5%, reduced travel distance by 20.4%, and decreased waiting times by 57.6%, compared to the first-come-first-served strategy currently employed at airports.

The runway system is more likely to be a bottleneck area for airport operations because it serves as a link between the air routes and airport ground traffic. As a key problem of air traffic flow management, the aircraft runway scheduling problem (ARSP) is of great significance to improve the utilization of runways and reduce aircraft delays. This paper proposes a large neighborhood search algorithm combined with simulated annealing and the receding horizon control strategy (RHC-SALNS) which is used to solve the ARSP. In the framework of simulated annealing, the large neighborhood search process is embedded, including the breaking, reorganization and local search processes. The large neighborhood search process could expand the range of the neighborhood building in the solution space. A receding horizon control strategy is used to divide the original problem into several subproblems to further improve the solving efficiency. The proposed RHC-SALNS algorithm solves the ARSP instances taken from the actual operation data of Wuhan Tianhe Airport. The key parameters of the algorithm were determined by parametric sensitivity analysis. Moreover, the proposed RHC-SALNS is compared with existing algorithms with excellent performance in solving large-scale ARSP, showing that the proposed model and algorithm are correct and efficient. The algorithm achieves better optimization results in solving large-scale problems.

Accurate prediction of taxiing time is important in ensuring efficient and safe operations on the airport surface. It helps improve ground operation efficiency, reduce fuel waste, and improve carbon emissions at the airport. In actual operations, taxiing time is influenced by various factors, including a large number of categorical features. However, few previous studies have focused on selecting such features. Additionally, traditional taxiing time prediction methods are often black-box models that only provide a single prediction result; they fail to provide effective practical references for controllers. Therefore, this paper analyses the features that affect taxiing time from different data types and forms a taxi feature set consisting of nine key features. We also propose a taxiing time prediction method based on adaptive scenario matching rules. This process classifies the scenarios into multiple typical historical scenario sets and adaptively matches the current target scenario to a typical scenario set based on quantified rules. Then, based on the matching results, a pre-trained model obtained from the corresponding scenario set is used to predict the taxiing time of an aircraft in the target scenario, aiming to mitigate the impact of data heterogeneity on prediction results. Experimental results show that compared to baseline methods, the mean absolute error and root mean square error of the proposed method decreased by 4.8% and 12.6%, respectively. This method significantly reduces the fluctuations in results caused by sample heterogeneity and enhances controllers’ acceptance of prediction results from the model. It can be used to further improve auxiliary decision making systems and enhance the precise control capabilities of airport surface operations.

Since the surface operation of hub airports was facing severe congestion and aircraft delay under the de-icing mode, some practical problems of improving aircraft operation and support efficiency are put forward. A two-phase model is constructed to coordinate the scheduling of aircraft surface operation (ASO) and de-icing support resources operation (DSRO). An optimized method is used to schedule the aircraft surface operation. On the basis of the scheduling results of surface operation, an aircraft and de-icing resource collaborative scheduling (ADCS) mechanism is established to optimize the assignment of aircraft and de-icing resources. The algorithm combining receding horizon control strategy and CPLEX solver (RHC-CPLEX) is designed to solve the model. Computational experiments performed on case studies of Beijing DaXing airport show some potential improvements: Firstly, for the ASO model, the RHC-CPLEX algorithm can reduce the objective function value by more than 20% compared with the FCFS algorithm. And the results shows that not only the delay distributions under different snow conditions are reasonable, but the spatial distributions of the de-icing zones of aircraft are closer to the location of their apron and allocated runway. Secondly, for the DSRO model, the RHC-CPLEX algorithm can reduce the objective function value by more than 6% compared with the algorithm based on the principle of proximity and availability. The de-icing vehicles are used efficiently and the number of refilling de-icing fluid and the free time of the de-icing vehicles can be significantly reduced.

The development of a Multiple Airport Region (MAR) could lead to fierce competition. To evaluate the level of homogenization between airports in a MAR, a homogenization evaluation method based on multi-dimensional indicators was developed. A multi-dimensional indicator system is proposed that takes into account infrastructure, integrated support, operational efficiency and airline networks. Then the Critic method and the Delphi method are used to assign hierarchical weights for each dimension of the indicators. The multi-layer homogenization matrix of the airport pairs within the MAR is derived. For airport pairs with high comprehensive homogenization, suggestions are provided according to analysis of the indicators. This study paper selected three typical MARs internationally to demonstrate the advantage of the proposed approach. The airport pairs with high a homogenous coefficient (greater than 0.5) were selected to analyze the reasons causing high homogeneity. Results show that the multi-dimensional indicators and hierarchical fusion captured the characteristics of the homogenization of MAR. Most airport pairs in New York MAR and in London MAR had strong differentiation of route network layout, airport pairs in Greater Bay MAR had ambiguous division of labor and low homogenization of route network, except CAN and SZX airports. Suggestions are discussed separately to mitigate the homogeneity of the airports in the MAS, thus, to improve the operation performance of the MARs.