Fast and Efficient Integer Linear Programming Method for Aircraft Recovery Problem

Flight schedules are an essential part of airline operations and impact all key elements, such as airplanes, crew, and passengers. Therefore, efficient flight scheduling is crucial for optimizing airline resource utilization and minimizing operational costs. Unfortunately, unexpected delays and interruptions can significantly affect even the most optimized flight schedules. Therefore, airlines must be able to recover efficiently from such situations to protect their revenue and reputation. In this context, artificial intelligence (AI) methods can be used to identify suitable recovery solutions when unexpected events occur. The methods proposed so far often leverage meta-heuristic methods, conventional optimization, and, more recently, machine learning. However, current works neither leverage integer linear programming (ILP) nor focus on simple scenarios that do not fully entail real-world complexities. This creates a significant gap, as ILP has the potential to provide effective rescheduling capabilities. To this end, we propose a novel approach to aircraft recovery problems based on integer linear programming. We formulate efficient equations that correspond to the restrictions, objective functions, and possible recovery actions. The model aims to minimize the total delays caused by disruptions by swapping aircraft and delaying flights as recovery options. Our experimental study involving two real-world airline datasets shows that the proposed method is effective in providing a significant reduction in delays while maintaining a limited execution time.