Marta Norte Vale de Almeida (MSc) – Modelling of de-icing operations at Schiphol Airport
Winter weather is known to cause major disruptions at airports worldwide, including Schiphol. One of the main causes of this is the need for aircraft de-icing, which adds an extra layer of complexity to airport ground operations, forcing airports to operate at limited capacity. As both inbound and outbound traffic have to be reduced, many flights are either cancelled or delayed.
An increase in the efficiency of the de-icing operations and in their coordination with the turnaround and runway scheduling processes could allow airports to operate at a less limited capacity despite severe winter weather conditions.
This project aims to model the de-icing operations at Schiphol Airport in severe winter weather conditions, with a focus on operational planning. The resulting model will be used to evaluate potential areas of improvement and will have the potential to serve as a tool for the evaluation of operational changes.
Wouter Cijsouw (MSc) – Airport and Network Aware Runway Forecasting Model for Schiphol Airport
Research question: How can improved flight and airport data be incorporated into an airport and network aware runway forecasting model for Schiphol Airport for usage in congestion prevention?
Within Air Traffic Control Netherlands (LVNL), there is currently a desire for more accurate predictions of the runway configurations. Due to the hub role of Schiphol along with the unique layout of its runways, the active take-off and landing runways alter multiple times per day which has large implications on daily operations. Knowing what runways will be in use ahead of time is crucial for preventing unnecessary delay and congestion within the airspace. If discrepancies exist between the forecasted and actual configurations, more flights may arrive within the FIR than can be accommodated for by the runway capacity, causing costly delays and increased fuel usage. Departing flights can also experience delays by such an issue, causing aircraft to remain on the ground for longer than necessary.
Currently, the runway configurations are predicted based on flight plan data, predicted load and meteo data. However, factors such as knock-on delay, load uncertainty modelling and gate occupancy are not yet taken into account and as such could provide an avenue for more accurate predictions. With a model taking this into account, new decision information is provided which can lead to more optimized capacity management along with more streamlined airport and airline hub operations. Within my thesis, I aim to identify the influence of these factors amongst others. After identification, modelling techniques shall be explored in order to find the best candidate for constructing an airport and network aware runway configuration prediction model, which will then be created. Lastly, a performance evaluation will be done on the resulting model.
Tim Honing (MSc) – Assessing Operational Impact of Taxiway Centreline Pushback Positioning at Schiphol using Agent-Based Simulation
As part of the Tug Release Points (TRP) project, LVNL is preparing to introduce
new pushback procedures at Amsterdam Airport Schiphol. Aircraft will be positioned straight on the taxiway centreline rather than diagonally, aiming to reduce fuel consumption and emissions at the stands (VOPs). While environmentally beneficial, this change introduces new operational constraints, particularly when multiple aircraft are aligned behind each other. Under these procedures, simultaneous pushbacks may lead to situations where tug drivers of trailing aircraft cannot exit once the leading aircraft has started its engines, potentially increasing pushback durations. The operational impact of these constraints is currently unclear, creating the need for a quantitative, system-level assessment.
This research aims to evaluate the impact of these new pushback procedures using an Agent-Based Model (ABM) currently under development. The model will be extended with detailed pushback logic to capture these operations, focusing on additional delays, downstream effects, and implications for ground controller workload.
Amine Nari (MSc) – Modeling Delay Propagation for LVNL’s Decision Support Tool (DST)
Air traffic flow management depends on accurate demand prediciton, yet a major source of error comes from knock-on delays, which are primary delays that propagate to subsequent flights. These cascading effects create significant uncertainty in the European network, affecting both planning and operational decisions. This research focuses on understanding how delays develop and spread over ti
me across flights and connections, using Schiphol Airport as the primary case study. By analysing large-scale delay data, the study aims to identify key patterns and mechanisms driving delay propagation.
The goal is to develop a model that represents how delays evolve throughout the network and can simulate their impact on traffic demand. The model will be integrated into LVNL’s Decision Support Tool (DST) to improve demand prediction and support operational decision making.