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A Probabilistic Model Based Optimization for Aircraft Scheduling in Terminal Area under Uncertainty

Abstract : Air traffic management relies strongly on the accuracy of prediction, while uncertainties such as weather, navigation accuracy, pilot operations, etc. may weaken the performance of predictive tools and cause potential safety issues or reduced capacity. Among all controlled airspace, the Terminal Maneuvering Area (TMA) is one of the most complex areas in which flight safety can be easily affected by unpredictable disturbances. This paper addresses an aircraft scheduling problem under uncertainty with the aim of providing a robust schedule for arrival flights. Uncertainty quantification and propagation along the routes are realized in a trajectory model that formulates the time information as random variables. Conflict detection and resolution are performed at waypoints of a predefined network based on the predicted time information from the trajectory model. By minimizing the expected number of conflicts, consecutively operated flights can be well separated. Apart from the proposed model, two other models-the deterministic model and a model that incorporates separation buffers-are presented as benchmarks. A meta-heuristic simulated annealing algorithm combined with a time decomposition sliding window is proposed for solving a case study of the Paris Charles de Gaulle (CDG) airport. Further, a simulation framework based on the Monte-Carlo approach is implemented so as to evaluate the optimized solutions obtained from the three models. Statistical comparison among the results shows instability of the model that incorporates the separation buffers, in contrast, the proposed model has absolute advantages in both stability and the conflict absorbing ability when uncertainty arises.
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Soumis le : lundi 13 septembre 2021 - 15:56:51
Dernière modification le : lundi 6 juin 2022 - 12:44:24


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Ying Huo, Daniel Delahaye, Mohammed Sbihi. A Probabilistic Model Based Optimization for Aircraft Scheduling in Terminal Area under Uncertainty. Transportation research. Part C, Emerging technologies, Elsevier, 2021, ⟨10.1016/j.trc.2021.103374⟩. ⟨hal-03342823⟩



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