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.

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.

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.

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.

Airports are highly complex systems that can generate economic growth on their own. Accordingly, airports should take proactive actions to create a status quo between the stakeholders(the airport itself, airlines, passengers) in the tactical planning of the aircraft stand allocation. Namely, the harmonization between the stakeholders’ interests is either reactively or not at all considered, so one cannot be certain that the objectives of the stakeholders are met. For that reason, a methodology is developed using Weight Space Search on a many-objective tactical stand allocation model to establish a reference performance set from which decision alternatives are created using the k-means clustering algorithm. Decision makers then can proactively assess and choose decision alternatives on the performance of the tactical stand allocation to identify how the different stakeholders can achieve their goals in (partial) synergy. The airport can also apply the concept of empathetic negotiation to establish a favorable status quo.

The master thesis is about testing the effectiveness of the Airport demand management strategies. Based on the problem of increasing pressure on airport capacity, due to the increase of the demand on flying. The goal is to find a system which supports planning of air traffic control by generating knowledge about the throughput of arrivals. A Discrete Event Simulation approach is used to simulate the behaviour of arrivals in case of capacity issues. The model uses random generated slot allocated schedules to test the effectiveness of several strategies. Eventually the result of the strategies is presented in an optimum ratio between delays and cancellations of the tested strategies. The main conclusion of the research is that the model is capable of testing the effectiveness of airport demand management strategies. It also provides knowledge on expected bottlenecks of the arrival operations by generating the average delay per hour and queue propagation for each strategy.

Commercial greenhouse gas emissions from aviation are proliferating, as is the concern among freight carriers to minimize their carbon footprint. From a corporate point of view, the United Nations International Civil Aviation Organization ( ICAO) expects aircraft emissions to triple by 2050, with aviation accounting for 25% of the world's carbon budget . While ICAO and the International Air Transport Association (IATA) release annual overview statistics on the aviation industry and its related business economy, relatively few research data on fuel consumption, fuel quality and carbon emissions are available at global and regional levels, respectively. Policymakers and top decision-makers at transportation and logistics companies such as PostNL cannot determine the exact amount of carbon emissions associated with departing flights and needs a more robust model to determine marginal emissions due to cargo freight. To solve this problem, the research predominantly aims to answer the following research question: "How can PostNL be facilitated in calculating aircraft specific carbon emission factors, which can be used for accounting purposes, to promote sustainable purchasing and green market positioning?". Using empirical data from public, private-owned confidential data sets, and the PianoX aircraft emissions modeling, this research outlines a consistent and globally dispersed methodology for estimating CO2 emissions for air freight. An extensive review of the literature was carried out in the field of the emergence of "Sustainability Concept" and antecedent research in the Netherlands. This was followed by evaluating the current situation and the emergence of the supply chain processes. The study also describes and analyzes the operational process at PostNL and discusses the current methodology used at PostNL, i.e. DEFRA method for carbon emission calculation. Later, the flaws in the model were evaluated and addressed. Based on the review of the literature on antecedent research and the analysis of different carbon emissions calculation methodologies being used internationally in various institutions, a method was proposed for the calculation of Co2 emissions due to air freight. In order to measure commercial fuel consumption, many publicly available data sources were collected and incorporated with Piano X, an aircraft performance and design platform from Lissys Ltd. The data on the fuel-burning process and projected Co2 emissions were then compared and validated with the ICAO dataset and later implemented in the model proposed. This was followed by the creation of a conceptual simulation and optimization model build using VBA in Excel, which helps the company in making data-driven air transport procurement decisions taking into account tradeoffs between carbon emission, lead time, and cost to gain a strategic business advantage. Strategic goals were broken down into the priorities of the individual divisions at PostNL, expressed with the goal values of the lead time and the performance metrics for cargo costs. A graphical comparison was followed with the EU ETS datasets and the DEFRA datasets to compare and correlate the results obtained using the proposed methodology. The result also helps PostNL drive business sustainability in their partnerships while maintaining their flexibility and bargaining power with suppliers. The limitations and errors with the research were acknowledged in the areas of uncertainty due to the use of publically available information and the absence of the inclusion of dynamically changing time-dependent variables, including privately owned airline data. It was also concluded that logistics companies such as PostNL should always bear in mind that, often drastically, the logistics networks may shift. New ways of doing business, such as coopetition and better modeling, can help to increase effectiveness. Scenario planning and better business management approaches will have an advantage in improving the transportation and logistics industry to face the demands of the future and become ever more competitive and sustainable.

In this paper, we introduce the Air Freight Flight Schedule Development Problem (AFFSDP). In this problem, a cargo-only carrier has to adjust an existing flight schedule based on changes in the predicted demand. The result of this research is a model that combines airport selection, fleet routing and cargo routing, together with the use of a (random) mandatory flight list and timetable setting for all other optional flights. The model is formulated as a Mixed Integer Linear Program (MILP). To reduce the pre-processing time of the model two meta-heuristics have been implemented that reduced the run time from several hours to minutes. To improve the performance of the model, all fractional numbers have been reshaped to integer numbers. The symmetry of the model was brought to a minimum by implementing a path-preference model. The model was tested with 3 aircraft types, for 3 different demand scenarios and 3 different freedoms. This sums up to 27 unique test cases. Overall, the results show that against a benchmark solution the total serviced demand could be increased by 20-50%.

Airports and airlines are examining and committing to the electrification of Ground Support Equipment (GSE). To be able to estimate the required quantity of eGSE, the charging requirements of eGSE, the change of airport electricity requirements, and the scheduling possibilities of eGSE charging for the existing turnaround procedures, a model was developed to simulate and optimize the GSE operations at airports. This was done by means of a Task Scheduling Problem (TSP), that is optimized using Mixed-Integer Linear Programming (MILP). A case study was performed on KLM's GSE fleet at Amsterdam Airport Schiphol. Based on this, it was concluded that there is no difference in the capacity that can be achieved for GSE types that can last an entire day on a single battery charge. However, another group of GSE types experiences battery depletion before the day concludes, requiring measures to maintain the capacity. The results indicate the model's suitability for strategic decision-making. Next to that, the model is effective on an operational level. The use of the model has the potential to make the use of resources in the operation more efficient.

Due to a projected shift in the majority of the world’s population towards urban areas and the existing traffic congestion in today’s cities, the possibility of using the third dimension for transportation of people and goods is being increasingly explored. The resultant congestion from an estimated demand of unmanned and Urban Air Mobility flights(UAM) in urban airspace would warrant a centralized mitigation framework. Additionally, the multi-stakeholder nature of UAM calls for a traffic mitigation approach that addresses the major concerns of UAM - safety and environmental impact. In order to address this problem, an Air Traffic Flow Management (ATFM) framework that follows a multi-path approach has been developed as a Mixed Integer Linear Programming (MILP) model in this paper. Compared to flights that follow a single fixed path, this multi-path approach is seen to show a delay cost reduction of upto 96.3%. On incorporating an impact mitigation part to the original ATFM framework, it was seen that delay cost increased by 25% compared to the multi-path model. On the other hand, the impact of traffic densities in urban airspace was seen to reduce by 43.47%. This further leads to trade-off studies between delay cost and the impact of traffic densities in airspace.

The main objective of a Master Plan is to develop an integral long-term development plan to guide the future of an airport. One important element is the capacity analysis as a first step in strategic airport planning. The research commissioned by NACO is boiled down to the main research objective: to contribute to the development of Strategic Stand Planning for airports, by finding an optimal required Stand Capacity using mathematical optimisation techniques. To give answer to this question a model developed which is called the Strategic Stand Capacity Model. This determines the amount of stands, and the standtype in terms of size and sector. As a case study Schiphol Airport is selected. The mathematical optimisation model at heart of the model can be described as a set of two classic linear optimisation problem, integrated in a larger software architecture. For both models the primal simplex algorithm is used to search for the optimal solution. As an optimiser IBM CPLEX is used. The input of the model is a flight schedule. The output is the amount of stands of each standtype is required, along a Pareto optimal front between operational cost and capital cost. A set of optimal stand capacity solutions for Schiphol is determined and used to analyse current capacity, which is proven to be sub-optimal in terms of standmix where a reduction in operational cost of 18% is possible. The impact of expansions at A-pier and H-pier in 2019 are analysed which show improvements but sub-optimal results in terms of the mix of standtypes, where a reduction of 24% in operational cost is possible. Then, the model is used to divide demand in airline segments to analyse impact of clustering airlines and alliances. When a hard split is made in airlines 14% more stand capacity is needed to find a solution. The model is then used to optimise airline division, which showed that when shifting a part of alliance-free airlines between two larger segments an optimal stand capacity can be found. Historic flight schedule data is used as input and historic stand allocation data is used to validate the results of the model. The result of the model is not a single solution of stand capacity but a range of optimal solutions of different capital investment and operational cost. A web-based supporting tool is developed as part of this research project to make practical application possible. This creates the opportunity for airport planners to make a data-driven trade-off between optimising many performance indicators, such as stand utilisation, bus movements, towing and remote parking. The results of this study will allow airport planners at NACO to develop a flexible master plan with respect to changing demand, and will support decision making to achieve the required stand capacity.

On-board electric motors can be used to drastically reduce the fuel usage during the taxiing phase of aircraft, leading to cost reductions for airlines and lower amounts of harmful emissions. This study analyses the current state of this innovation and its potential impact on aviation. On a global level, full adoption of electric aircraft taxiing is expected to cause a reduction in jet fuel usage of 846 million kg per year, equivalent to 186 million euros of reduced costs and 2.67 million tonnes of carbon dioxide emissions. This results in a reduction of 0.3% of the total global carbon dioxide emissions of the aviation sector. Locally, airports and their surroundings will benefit significantly from the reduced emissions, because a substantial fraction of airport emissions are due to the taxiing phase. Analysis of the effect of electric aircraft taxiing to key stakeholders such as airlines shows that American airlines would reap substantially larger benefits than European competitors because of consistently higher taxi times in the United States. Low-cost carriers are expected to see smaller impact than traditional hub-and-spoke airlines, due to short taxi times in the secondary airports they predominantly fly to. KLM could save 17.3 million kg of jet fuel annually, representing a cost of 3.8 million euros, which would potentially increase profits by 3%, and a carbon dioxide emission of 55 million kg. Since the road to full adoption is still long, a strategic analysis of the fleet shows the marginal yearly cost reduction per installed electric taxiing system starts at 82 thousand euros for the first product, which reduces to 10 thousand after 100 systems have been installed. Especially the flights between Amsterdam and London, Paris and Manchester should be assigned to aircraft with electric taxiing systems, because these flights would have the most impact given their relatively low flight distance and high taxi times.

The runway usage of complex airports with multiple runways is currently prescribed by a preference list. The preference list mostly focuses on minimizing noise and it provides a more manageable flow for ATC. However, it does not consider the fuel burn or current demand of flights. This study presents a MILP scheduling model which optimizes for fuel burn and noise disturbance. This is done by a flexible allocation of flights to runways, therefore removing the use of a runway preference list. Furthermore, a separation method is developed to capture the dependencies within a complex runway system such that the model can be easily applied to every airport. The model is tested at Amsterdam Airport Schiphol and it is concluded that flexible scheduling has a positive impact on both objectives, resulting in more efficient airport operations and the possibility to expand operations within the current regulations.

Network scheduling and fleet assignment are essential tasks for airline operation. In order to generate an optimal flight plan, the flight route and the flight schedule of each aircraft in the fleet requires deliberated consideration and planning. Compared with commercial airlines, which are in pursuit of maximal benefit during the operations, the humanitarian air services have different goals and therefore different strategies are designed. The humanitarian air service dedicates to fulfilling maximal passenger requests by reacting in a relatively short time frame, and the overall cost efficiency needs to be maximised. In this research, the United Nations Humanitarian Service’s (UNHAS) South Sudan mission is taken as the case to study and a metaheuristic method on top of the multi-integer linear programming (MILP) model is designed to solve the optimisation problem. The optimisation process consists of two stages: the tabu search process to assign the flight routes among the fleet, and a variation of a Fleet Size and Mix Vehicle Routing Problem (FSMVRP) model to finally determine the time schedule of each aircraft. The model is able to unlimitedly split the passenger requests and recaptures passenger spillage. It considers much fewer assumptions during the solving process and it provides large flexibility for the planner to manually modify the model based on their purpose. A dynamic balance of aircraft utilisation time regarding the Minimum Guaranteed Hours (MGH) within the fleet is also discussed. The result of this method is compared with the previous study of S.P. Niemansburg, which shows 1% to 11% of cost saved on a single day's operation regarding different levels of passenger spillage.

Airline reserve fleet capacity is a strategic resource that provides airlines the means to add robustness to their schedules. This paper focuses on leveraging past data on airlines' execution of planned schedules (resulting delays, cancellations and missed connections), along with the disruptions that prohibited the flawless execution of the planned schedules, in order to assess the impact of reserve fleet composition changes on past days of operations. The fleet schedule is modeled as a set of parallel time-space networks, and a Mixed Integer Linear Programming model is defined and solved with the objective of minimising passengers' disruption costs incurred during execution of the planned schedule. By adjusting the reserve fleet within the model, the resulting impact on disruption costs and operational performance indicators is quantified. The approach presented can be applied on heterogeneous fleets and to hub-and-spoke airlines. Its performance is discussed using a case study on the network of a Full Service Carrier operating in Europe. Simulated results of all reserve fleet scenarios considered are compared with recorded passenger disruption data from the same period, demonstrating the model's capacity to emulate historically observed operational performance. Notably, the model (considering the same reserve fleet as was operated) exhibits an average deviation of 7.9% in disruption costs when compared with recorded data. The analysis of resulting disruption costs stemming from reserve fleet composition changes reveals that adding an A220 aircraft to the airline's reserve fleet would result in a slightly higher reduction (-5.7%) of disruption costs compared to adding an A320 aircraft (-5.3%). Conversely, removing an A320 aircraft from the reserve fleet would lead to a significantly higher increase in disruption costs (+20.5%) compared to removing one A220 aircraft as reserve (+16.9%). Consequently, when considering changes to the reserve fleet operated, the airline should prioritise alterations on the A220 portion of their fleet.

Airline reserve fleet capacity is a strategic resource that provides airlines the means to add robustness to their schedules. This paper focuses on leveraging past data on airlines' execution of planned schedules (resulting delays, cancellations and missed connections), along with the disruptions that prohibited the flawless execution of the planned schedules, in order to assess the impact of reserve fleet composition changes on past days of operations. The fleet schedule is modeled as a set of parallel time-space networks, and a Mixed Integer Linear Programming model is defined and solved with the objective of minimising passengers' disruption costs incurred during execution of the planned schedule. By adjusting the reserve fleet within the model, the resulting impact on disruption costs and operational performance indicators is quantified. The approach presented can be applied on heterogeneous fleets and to hub-and-spoke airlines. Its performance is discussed using a case study on the network of a Full Service Carrier operating in Europe. Simulated results of all reserve fleet scenarios considered are compared with recorded passenger disruption data from the same period, demonstrating the model's capacity to emulate historically observed operational performance. Notably, the model (considering the same reserve fleet as was operated) exhibits an average deviation of 7.9% in disruption costs when compared with recorded data. The analysis of resulting disruption costs stemming from reserve fleet composition changes reveals that adding an A220 aircraft to the airline's reserve fleet would result in a slightly higher reduction (-5.7%) of disruption costs compared to adding an A320 aircraft (-5.3%). Conversely, removing an A320 aircraft from the reserve fleet would lead to a significantly higher increase in disruption costs (+20.5%) compared to removing one A220 aircraft as reserve (+16.9%). Consequently, when considering changes to the reserve fleet operated, the airline should prioritise alterations on the A220 portion of their fleet.