Commercial electric aircraft operations are foreseen in the coming five to ten years. For an airport to be ready for this introduction, energy infrastructure requirements have already been subject to various research topics. Most research in this field only focused on cost minimization of the number of chargers given a fixed flight schedule. This research however implements flexibility into the flight schedule and incorporates the energy provision in terms of renewable energy sources in combination with battery storage. Considering energy infrastructure costs as well as operational costs, the goal of the newly proposed Mixed-Integer Linear Programming model remains cost minimization. Besides a daily operational model, an additional energy balance focused optimization has been applied for data of an entire year. In this second model, energy from the local grid may be drawn and returned to come to a cost optimized solution. Both models have been run for a case-study on Bonaire International Airport where
inter-island flights to and from its two neighboring islands were electrified. A combination of solar panels and battery energy storage was found to be most cost efficient while sensitivity analysis showed many insights into possible energy business cases for airports.

With (e-)grocery retailers striving to increase their efficiency and subsequently reduce their costs, the opportunities that lie in optimising the order picking process of the supply chain is of key importance. The complex nature of assigning articles to an optimal storage location, having efficient routing of order pickers, and optimised grouping of orders calls for an integral approach to these problems. The objective of the research is to reduce the travelled distance of order pickers and subsequently the costs of the order picking process. Next to the integral approach, a new method of Multiple Storage Locations (MSL) is introduced. Historic order data of an e-grocery retailer is used, together with information on stock keeping units (SKUs), to implement SKU allocation with MSL possibility, routing, and batching into models. This ensures that the cost saving effects of these measures can be quantified. A benchmark Mixed Integer Linear Programming model (MILP) is developed and compared to a meta-heuristic Adaptive Large Neighbourhood Search model (ALNS) to determine how much travelled distance would be saved. The ALNS has multiple destroy- and repair heuristics, some of which are novel, that are specific to the problem at hand. The ALNS is able to handle bigger instances than the MILP, whilst ensuring quality of the solution. The MILP model outperforms the ALNS for small instances, however for large instances (instances of 400 orders or more) the ALNS performs, on average, 12.5% better whilst reducing computational time by 14.8%. Finally, areas of improvement are suggested in the ALNS model as well as other effects that should be studied when introducing MSL.

Due to the growing demand in air transport, measures need to be taken to reduce the impact of noise and emissions on local communities and the environment. The generic nature of current departure procedures leaves room for improvement in the form of aircraft type dependent departure trajectories. In this study a trajectory optimization model is implemented that uses a multi-objective evolutionary algorithm to optimize departure trajectories for fuel consumption and awakenings from noise. A parameterization technique is applied that reduces the number of optimization parameters by splitting the horizontal and vertical profiles into segments, thereby reducing the complexity of the problem. The model is used to optimize a specific departure trajectory at Schiphol Airport. A 15% reduction in fuel and a 60% decrease in number of awakenings is possible when an aircraft flies a fuel or noise optimal trajectory respectively. An intermediate trajectory provides a trade-off between the two objectives that is still superior to the reference. Medium aircraft can share an optimal trajectory without significant deterioration in performance while this does not apply for heavy aircraft. The achieved fuel savings translate to lower CO2 emissions which contribute to reaching EU climate goals, while the reduction in number of awakenings results in fewer restrictions on the maximum number of aircraft movements. Both aspects allow the air transport industry to continue growing in a sustainable manner in the future.

The buildup phase at KLM Cargo requires cumbersome planning actions because of a slow data handover at the warehouse and an uncertain initial state of the buildup process at the beginning of the shift. This study presents a model to improve scheduling of future buildup shifts according to flight departure times of KLM. The most important objectives are to minimize the delays and equally distribute the workload. For this purpose, a MILP model has been created. The problem is modeled as a scheduling problem with time windows. Next to the branch-and-bound method, an adapted tabu search is implemented as an alternative optimization technique. Both methods show adequate performance in terms of computation time and model convergence. With the help of a real-time data architecture, scheduling can now be standardized, take less time, incorporate more detailed information and in addition, bottlenecks can be identified early.

All major airport operators face a similar challenge, namely ensuring maximum throughput and maintaining high runway utilisation. A key part of this is accurately planning aircraft movements on the ground to avoid queueing and associated delays. A primary indicator of the operator performance in this area is the Taxi-Out Time. The research objective of this article is to review whether the application of machine learning can be used to model the departure process in such a way as to provide accurate prediction of TXOT taking into account a wide range of variables. A regression tree type machine learning model is developed using actual data from Vienna Airport and a selected set of significant predictor variables. The taxi-out times of the test set of flights are closely predicted with an RMSE of 2.03 minutes for normal taxi-out and 3.75 minutes for extended taxi-out.

Aircraft manufacturers are increasingly exploring emission-free flight or emission reduction for larger passenger aircraft. The low energy density of state-of-the-art battery technology limits the application to small, electric, fixed wing aircraft up to a flight time of approximately one hour. To overcome these limits, a combination of fuel cells and batteries to exploit the benefits of battery power density and hydrogen energy density was studied. Current Lithium-Ion battery cells reach approximately 1.6 kW/kg of maximum power density, much higher than fuel cell systems. On the other hand, the energy storage capacity of suitable hydrogen storage methods is much larger than battery cells, the latter have an energy density of 240 Wh/kg.

Because most demonstrated applications are for fixed wing aircraft, the unmanned GeoCopter GC-201 helicopter was used for performance requirements, weight and volume analysis. The study focuses on the preliminary sizing of the powertrain and the optimization of fuel cell and mission profile variables for this vehicle. Helicopter performance modelling, fuel cell static behavior as well as a battery discharge simulation are combined with lower fidelity models for other components.

The study results in a comparison of battery-only and fuel cell-battery configurations through payload-range diagrams, allowing for a quick evaluation of application areas. These mainly show that batteries excel at high payload, low range applications whereas a fuel cell-battery combination shows clear advantages at low payload, longer range applications. Liquid hydrogen will be shown to be comparable to the current micro gas turbine powered rotorcraft, with 400 and 500 km range capabilities respectively. Range capabilities for 300 bar and 700 bar compressed gas tank storage options show 140 and 180 km, with battery-only reaching a maximum range of 80 km.