In an effort to improve an airport operation optimization model, this research investigates the possibility of predicting probability distributions of flight delays with machine learning algorithms. The research is centered around Rotterdam The Hague Airport, a regional airport in the Netherlands. The first objective is to test how well machine learning classifiers can predict whether a flight will be delayed for a regional airport. This results in accuracies of around 70%, while taking precision and recall into account. The second objective is to predict the probability distributions of flight delays, for which three models are selected: a modified Random Forest Regressor, a Mixture Density Network and a Dropout Network. The main finding is that accurately predicting distinctive delay probability distributions for individual flights is possible. As a final objective, the predicted flight delay distributions are incorporated into an existing Flight-to-Gate Assignment Problem. It is found that this improves the robustness of the resulting schedules, although associated with a small reduction in their efficiency. The overall conclusion of this research is that machine learning-based prediction of flight delay distributions is possible, sufficiently accurate, and can improve at least one airport operation optimization problem. Further research will have to show whether this approach can be extended to other airports, other aviation optimization problems, or even optimization problems in other research areas.

In order to reduce aircraft emissions during on-ground operations, electric taxiing systems (ETS) have been intensively researched to take over or assist in part of the taxiing phase of a flight. One of these ETS is the TaxiBot, deployed by Smart Airport Systems (SAS). While a number of papers have been researching this vehicle routing problem (VRP) in order to minimise costs, fuel consumption or another metric, most research uses deterministic input data. However, sudden changes are inevitable, disrupting the resulting schedule. In this paper, we propose both a strategic model and a tactical model for airport surface movement, taking into account stochastic delays to the flight schedule. The subsequent tactical schedule can be produced with only little deviations from the strategic schedule. The tactical model can be generated in the order of seconds, making it useful for real-life traffic management decision support systems. We found that the tactical schedule does not worsen remarkably. Still 47.9% of the flights can be towed by a TaxiBot, which was 48.5% in the strategic schedule. Different case studies have been performed in order to determine the effect of e.g. the size of the flight schedule and the number of TaxiBots used, on the aircraft taxiing coverage and TaxiBot efficiency. With this, both busy and calm days, now and in the near future are assessed and the optimum number of TaxiBots necessary can be determined. An upper limit is reached at an asymptote starting from 34 TaxiBots, while a lower limit is dependent on a trade-off between flight coverage and spare TaxiBot capacity.