Optimizing the runway capacity allocation under uncertain capacity constraints

In the air traffic management (ATM), uncertainty is everywhere, since the air transport activities are subject to different intrinsically uncertain parameters, like demand, weather and unpredictable operational constraints impacting the airport capacity. So, this study's intent is to deal with one of these major constraints: the airport capacity utilization. It has been treated in different researches over the years, most of them using linear programming, fuzzy optimization and dynamic programming. So, a new approach is proposed, combining different contributions from the literature and introducing a Minimax Regret model to account for the runway capacity uncertainty in the problem of optimizing the airport capacity utilization. The results are compared to the best-case and the worst-case scenarios to evaluate the model performance. It shows that the model can provide interesting and efficient results from the point of view of reducing the regret in relation to both scenarios in terms of total delays.