Projects

• (COMPLETED) Models and optimization approaches for predictive aircraft maintenance (2020-2024)

This project aims to develop innovative scheduling models for aircraft maintenance that use i) data-driven prognostics about the condition of aircraft components and ii) stock levels for aircraft components. The scope of the project is scheduling of maintenance tasks at the fleet level, for a short to medium planning horizon. The optimization models are expected to take into account prognostics on the remaining useful life of components. The project will also contribute to the development of prognostics algorithms for the remaining useful life of components.

Co-promoter of PhD Student: Ingeborg de Pater (graduation date: 11th April 2024)

• (COMPLETED) EFRO Airport Technology Lab – Airside Operations (2019-2023) 

To support an efficient planning of airside operations at airports, there is a need for optimisation models that take into account potential flight delays and cancellations. This PhD project aims to develop machine learning algorithms to predict flight delays and cancellations. These results are further considered in optimisation models for airport operations. The goal of the project is to support early identification and monitoring of critical flights, and to efficiently plan airside operations.

Co-promoter of PhD student: Mike Zoutendijk (graduation date: 30th May 2024)

• (COMPLETED) HO2020 Advances Engine-off Navigation (2021-2022) 

This PhD project aims to propose optimisation models for the scheduling of electric taxibots at an airport. We consider the assignment of taxibots-to-aircraft, together with battery charging models for the taxibots. We adjust the assignment of taxibots to flights by taking into account potential flight delays. 

Co-promoter of PhD student: Simon van Oosteroom

• (COMPLETED) HO2020 Real-time Condition-based Maintenance for Adaptive Aircraft Maintenance Planning (2018-2022)

This PhD project proposes methods and tools to support the implementation of predictive maintenance for aircraft. Among others, the project aims to develop health diagnostics and prognostics of aircraft systems using innovative data-driven machine learning techniques; to integrate these prognostics into maintenance optimization models; and to evaluate maintenance strategies by means of Monte Carlo simulation.

Co-promoter of PhD student: Juseong Lee (graduation date: 5th December 2022)

• (COMPLETED) HO2020 ADVANCED PREDICTION MODELS FOR FLEXIBLE TRAJECTORY-BASED OPERATIONS  (ADAPT) (2017-2019)

The ADAPT project proposed a set of flight scheduling methods that assume the concept of en-route time-window flying, i.e., a temporal interval to which flights are recommended to adhere to so that sector capacities are met. Several scheduling models are proposed at a strategic level (to address demand-capacity imbalances), as well as at a pre-tactical level (to account for weather uncertainties). With these models, the ADAPT project aims to provide methods to increase flight flexibility and predictability.