Airports and surrounding airspaces are limited in terms of capacity and represent the major bottlenecks of the air traffic management system. This paper addresses the problems of terminal airspace management and airport congestion management at the macroscopic level through the integrated control of arrivals and departures. Conflict detection and resolution methods are applied to a predefined terminal route structure. Different airside components are modeled using network abstraction. Speed, arrival and departure times, and runway assignment are managed by using an optimization method. An adapted simulated annealing heuristic combined with a time decomposition approach is proposed to solve the corresponding problem. Computational experiments performed on case studies of Paris Charles De-Gaulle airport show some potential improvements: First, when the airport capacity is decreased, until a certain threshold, the overload can be mitigated properly by adjusting the aircraft entry time in the Terminal Maneuvering Area and the pushback time. Second, landing and take-off runway assignments in peak hours with imbalanced runway throughputs can significantly reduce flight delays. A decrease of 37% arrival delays and 36% departure delays was reached compared to baseline case.
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This paper presents an innovative approach that combines optimization and simulation techniques for solving scheduling problems under uncertainty. We introduce an Opt–Sim closed-loop feedback framework (Opt–Sim) based on a sliding-window method, where a simulation model is used for evaluating the optimized solution with inherent uncertainties for scheduling activities. The specific problem tackled in this paper, refers to the airport capacity management under uncertainty, and the Opt–Sim framework is applied to a real case study (Paris Charles de Gaulle Airport, France). Different implementations of the Opt–Sim framework were tested based on: parameters for driving the Opt–Sim algorithmic framework and parameters for riving the optimization search algorithm. Results show that, by applying the Opt–Sim framework, potential aircraft conflicts could be reduced up to 57% over the non-optimized scenario. The proposed optimization framework is general enough so that different optimization resolution methods and simulation paradigms can be implemented for solving scheduling problems in several other fields.
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The capacity of the newly inaugurated airport terminal in Mexico City, opened in 2022, has sparked debates regarding its adequacy to accommodate future demand. To address this critical question, our study employs simulation-based analysis to assess the terminal's true potential. By simulating various scenarios, we aim to provide insights into its capacity to handle increasing passenger loads over the coming years and decades. Furthermore, our analysis identifies potential challenges and issues that may arise with the terminal's growth. This research seeks to offer valuable perspectives for stakeholders involved in the airport's planning and management, contributing to informed decisionmaking in ensuring efficient and sustainable aviation infrastructure.
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