Enhancing Airport Operations: An Integrated Framework for Optimising Aircraft Surface Movement and Airport Resource Management
Résumé
The ever-increasing volume of air traffic has significantly strained airport resources, intensifying congestion and emphasising the critical interface between airspace, airside and landside operations in airport management. As a result, optimising aircraft surface movement and resource allocation becomes essential to improving airport efficiency within these constraints. This paper presents an integrated optimisation framework designed to coordinate airport surface traffic with resource management at the tactical level, enhancing operational efficiency and resilience while maintaining the balance between demand and airport capacity. Unlike traditional gate allocation methods that address departures and arrivals independently, this study emphasises the efficiency of assigning paired arrival and departure flights of the same aircraft to a single gate, reducing the costs and delays associated with towing aircraft between gates. A hierarchical allocation scheme is proposed to assess gate and terminal overload, accounting for aircraft in-block and off-block times, rather than assuming fixed schedules. Further, this study integrates ground handling assignments into airport resource management optimisation, introducing an additional level of uncertainty while capturing the complexities of daily airport operations. A novel integrated Mixed Integer Programming (MIP) model is developed to formulate the problem mathematically. To ensure a robust and flexible solution that aligns with real operational scenarios, a metaheuristic approach is incorporated to manage uncertainties in aircraft surface movement and resource availability. The algorithm employs a combined probability- and performance-based decision-making process to guide the neighbourhood operator. Additionally, a sliding window technique, with a 2-hour window length and a 30-minute shift, is applied to optimise the model and validate the decision dynamically. The proposed framework and algorithm are validated through a case study using historical radar data from Paris Charles de Gaulle (CDG) Airport, demonstrating its applicability and effectiveness in real-world applications.