The effectiveness of flight operations and the quality of airport services are directly impacted by airport gate assignment options. In real-world operations, unexpected events such as airfield accidents may lead to the temporary closure of local gates and an infeasible assignment plan. It is urgent to implement gate emergency reassignment under resource constraints. This paper proposes an Epsilon constraint-based column generation optimization algorithm for this problem. In particular, we develop a bi-objective optimization model based on set partitioning with the goal of minimizing assignment plan deviation and increasing solution efficiency. Then, an Epsilon constraint-based column generation optimization algorithm is designed to efficiently obtain high-quality solutions. Numerical experiments are conducted based on real-world operational data from an international airport. The results demonstrate that the proposed method performs well on main metrics such as the bridge boarding rate and flights adjustment efficiency. In particular, the cross-region adjustment proportion of our solution is 52.34%, which is significantly lower than the comparison methods, and effectively improves the airport operational efficiency in emergency scenarios.

Airport disruptions often pose challenges in assigning aircraft to gates, resulting in infeasible planned schedules. In particular, a large number of transfer passengers miss their connections in the context of disruptions, which cause huge economic losses to airlines and serious passengers’ dissatisfaction. This paper proposes a set-partitioning-based model to optimize Aircraft-Gate Reassignment with Transfer Passenger Connections (AGRP-TPC), which incorporates flexible gate-swap and aircraft-delay operations to mitigate the overall impact of disruptions. To efficiently solve the model, we introduce the concepts of additive-transfer and nonstop-transfer to handle passenger connections, and develop a Hierarchical Column-and-Row Generation (HCRG) approach guided by airport terminal space attribute. The column generation and row generation procedures solve iteratively until no new variables and constraints are generated. In addition, a follow-on strategy and a diving heuristic are designed to efficiently obtain high-quality solutions. We evaluate the proposed approach using various instances from a major Chinese international airport. Computational results demonstrate that our approach outperforms the comparison algorithms and produces good solutions within the time limit. Detailed results indicate that our approach effectively reduces overall losses in aircraft-gate reassignment following disruptions, and it can serve as an auxiliary decision-making tool for airport operators and airlines.

Flight delay prediction has attracted great interest in civil aviation community due to its significant role in airline planning, flight scheduling, airport operation, and passenger service. Flight delay is affected by numerous factors and irregularly propagates in air transportation networks owing to flight connectivity, which brings critical challenges to accurate flight delay prediction. In recent years, Graph Convolutional Networks (GCNs) have become popular in flight delay prediction due to the advantage in extracting complicated relationships. However, most of the existing GCN-based methods have failed to effectively capture the spatial–temporal information in flight delay prediction. In this paper, a Geographical and Operational Graph Convolutional Network (GOGCN) is proposed for multi-airport flight delay prediction. The GOGCN is a GCN-based spatial–temporal model that improves node feature representation ability with geographical and operational spatial–temporal interactions in a graph. Specifically, an operational aggregator is designed to extract global operational information based on the graph structure, while a geographical aggregator is developed to capture the similar nature among spatially close airports. Extensive experiments on a real-world dataset demonstrate that the proposed approach outperforms the state-of-the-art methods with a satisfying accuracy improvement.

The advancement of autonomous technology makes electric-powered drones an excellent choice for flexible logistics services at the last mile delivery stage. To reach a balance between green transportation and competitive edge, the collaborative routing of drones in the air and trucks on the ground is increasingly invested in the next generation of delivery, where it is particularly reasonable to consider customer time windows and time-dependent travel times as two typical time-related factors in daily services. In this paper, we propose the Vehicle Routing Problem with Drones under Time constraints (VRPD-T) and focus on the time constraints involved in realistic scenarios during the delivery. A mixed-integer linear programming model has been developed to minimize the total delivery completion time. Furthermore, to overcome the limitations of standard solvers in handling large-scale complex issues, a space-time hybrid heuristic-based algorithm has been developed to effectively identify a high-quality solution. The numerical results produced from randomly generated instances demonstrate the effectiveness of the proposed algorithm.