Resilience can be understood as the intrinsic ability of a system to adapt its functioning prior to, during, or following changes and disturbances, so that it can sustain required operations and performance under both expected and unexpected conditions. The need to explore the concept of resilience, in the context of socio-technical systems, is widely acknowledged in literature and industry. Nevertheless, this research topic is still relatively unexplored and no formal definition and metrics for resilience exists. It is encouraged by the current state of research, to develop a better understanding of mechanisms present in socio-technical systems that underlie the ability to be resilient. One of the resilience mechanisms identified is the mechanism of anticipation. Anticipation reflects the ability to predict future disruptions and consequences, and provide adaptive solutions to avoid performance loss. This research aims to develop a formalized model of an anticipation mechanism and analyses the working of this mechanism through a computational agent-based simulation study of airport security operations.

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.

Aircraft base maintenance consists of hundreds of tasks including preventive, corrective and modification tasks which have to be scheduled to a few check moments per year. The allocation of tasks to checks represents a complex scheduling problem due to the constraints involved. A mixed integer linear programming model was developed to optimize the allocation of the complete base maintenance workload and maintenance checks over a four year period. The model optimizes for resource capacity and interval efficiency. CPLEX has been used as solver. An iterative solution technique loops around the model which yields an approximate optimal solution. The model was developed for a large MRO service provider. Its outcome provides the MRO with an integral insight into the complete workload distribution over a tactical planning horizon for all checks. This insight can help to optimize the allocation of maintenance tasks and resource capacity which will contribute to a higher fleet availability.

The most common operational measure in aviation to reduce the effect of environmental noise pollution is noise abatement procedures. Noise abatement procedures are recommended flying techniques based on noise optimal trajectories, which aim at reducing the noise impact on local communities as much as possible. Atmospheric conditions influence sound propagation, however the noise abatement procedures are often fixed and independent of weather conditions. This research has been set up in order to analyse whether the implementation of weather-adaptive noise abatement procedures result in different noise optimal trajectories in different weather conditions. The research objective is to assess the influence of weather on optimised noise abatement departure procedures for one fly over, by developing an optimisation tool that allows for the synthesis of weather adaptive noise abatement procedures within the terminal manoeuvring area. To analyse the influence of different atmospheric conditions on the noise optimal trajectories in various weather conditions, a sound propagation model has been developed with the inclusion of variable air temperature and lapse rate, relative humidity, wind profile and ground surface type. Together with an aircraft performance model enhanced with varied atmospheric conditions and a noise impact model, the noise optimal trajectories in different weather conditions are obtained using optimal control theory in GPOPS. The model can be adapted to various requirements and aircraft types. The resulting trajectories are used to evaluate whether weather-adaptive noise abatement procedures should be implemented or not and to evaluate the performance of optimal control theory in this framework.

Ever since the attacks on the World Trade Center, airport security has been a topic of interest. The United States was caught by surprise and the attacks triggered a renewed interest in aviation security. It was well recognized that airport security was one step behind on intelligent attackers and this created the need to developed better risk assessment methods. Unfortunately, none of the risk assessment methods developed has the possibility to quantify the vulnerabilities in an airport checkpoint. One of the main challenges in developing a method which can do this, is to find a technique that can account for the complexity of the airport environment from which the vulnerabilities emerge. Furthermore empirical research has shown that security operators do not necessarily follow protocol, but behave as autonomous agents which regularly bend or break the rules. A method which can potentially identify vulnerabilities in such a complex environment is agent based modeling. This modeling technique has proven to be very powerful in modeling complex systems that emerge from the behaviour of autonomous agents. Some work has been done in this area, but until now this technique has not been used to quantify vulnerabilities in an airport checkpoint. Therefore, the aim of this project is to develop an agent based model of an airport checkpoint, quantify the vulnerabilities emerging from this checkpoint and analyze the effect of employee behaviour on these vulnerabilities. To do this, the behaviour of the security operators is modeled using models that are rooted in behavioural psychology and have strong empirical backing. The employees decision making is modeled using Decision Field Theory and the employee performance is modeled using the Functional State Model. With the model developed, a set of experiments is performed to calibrate the model and analyze the vulnerabilities. These experiments result in the quantification of vulnerabilities for a predefined set of threat scenarios. The analysis of these threat scenarios shows that employee behaviour mainly impact threats scenarios in which a weapon is hidden in the carry-on baggage. The reason that security operators have a large influence on the outcome of this screening process is that it is the process in which employees have to perform multiple activities and make multiple decisions. It is found that the vulnerabilities in this screening process are mainly dependent on the speed/accuracy trade-off as made by the employees. The perceived risk of the detected prohibited items plays a limited role, since most prohibited items are only seen as a small risk. The performance of the employees played a less important role on the outcome of the screening process and differences in performance are mainly caused by the personality type of the agents. The personality type that put more effort into a task, outperformed the other agents. The skill level of the operators however, did not significantly effect the outcome the simulation. This suggests that the effort an operator puts in is more important than the skill level of the operator. Finally it is found that it is beneficial to minimize the number of steps in the screening process. This benefits overall performance, since adding steps to the screening process means adding opportunities to make mistakes.

The Flight-to-Gate Assignment Problem (FGAP) covers the optimal assignment of flights, or rather the aircraft serving these flights, to the available aircraft stands. Existing FGAP models in literature do not consider airside and landside processes simultaneously. We propose a FGAP model with three specifications, where only airside constraints are active, both airside and landside constraint are active and passenger demand cannot exceed the fixed declared landside capacity, and both airside and landside constraint are active, and the landside passenger constraints are driven by the expected maximum passenger waiting time. The implementation of landside constraints results in a gate assignment at a increased cost, compared to gate assignment derived using inactive landside constraints. Imposing the constraint that passenger demand cannot exceed declared capacity creates a gate assignment at an increased cost com- pared to implementing a maximum passenger waiting time, but does o↵er a superior passenger experience since waiting times are non-existent.

The newest generation of aircraft has seen a strong increase in sensor data generated on-board. The available data has the potential to indicate the health state of individual components based on which their maintenance requirements can be determined, a maintenance strategy called Condition Based Maintenance. Predictive Maintenance is a specific condition based maintenance strategy that aims to determine these requirements in advance by predicting failures from the sensor data. It has the potential to reduce costly unanticipated maintenance or unnecessarily conservative maintenance. In the short term it could add significant value for components that are currently subject to a reactive maintenance policy. In the long term it could potentially disrupt the traditional maintenance practice of periodic inspection. One of the main challenges in applying Predictive Maintenance in the aviation industry is translating the large amounts of sensor data into a reliable failure prediction, a process called prognostics. In this study, state-of-the-art machine learning, and specifically deep learning models, have been investigated for their potential for prognostics. A case study has been performed at KLM Royal Dutch Airlines on the Boeing 747 Bleed Air Valves, traditionally some of the most challenging components from a maintenance perspective. It has been shown that fully self-learning algorithms can be used for prognostics, enabling the implementation of one of the first real-life predictive maintenance implementations.

This MSc thesis presents a stochastic modelling approach to the multi-period airline fleet planning problem. Approximate Dynamic Programming (ADP) is used to model the impact of demand uncertainty on fleet decisions. The proposed ADP algorithm applies local value function approximations resulting from Gaussian kernel regressions to estimate future airline operating profits. A case study at a reference airline is used to show the effectiveness and practicability of the proposed approach. Optimal solutions are achieved for the deterministic case of the problem, while obtained policies excel the most-likely deterministic solution on stochastic versions.

Margins for cargo airlines that were already thin have been put further under pressure, which stresses the importance of operating a profitable schedule. Research on modelling passenger operations has proven that integrating different steps in the schedule planning process can yield a significant increase in profit. For cargo operations no integrated model exists that can provide an integrated schedule planning from scratch within reasonable computation time. The model proposed in this work aims to integrate schedule design, fleet assignment, aircraft routing and cargo routing for both express freight- and general freight airlines. A dynamic programming optimisation framework is introduced that decomposes the schedule planning problem for a week of operations into sub-problems, that each aim to optimise the rotation of an individual aircraft. The model takes important operational constraints and airline requirements into account, such as maintenance and a minimum service frequency per flight leg, while optimising for minimum cost, maximum profit, or connectivity. Tests were conducted on three real-life case studies, that reflect the applicability of the methodology developed. The results show that the generated schedules meet imposed requirements and reflect real airline operations, according to industry experts. Furthermore, the model provides results in reasonable computation time, which for a small airline is under 10 minutes.

There is a growth in the use of composites for the new generation of wide-body aircraft such as the Boeing 787 and Airbus A350. This shift from using aluminium as the primary material is motivated by the benefits of using composites in design, manufacturing and operations. Composites offer the aircraft manufacturer the ability to create more complex shapes and optimise the design such that it is light-weight. This, in tandem with other design improvements, leads to lower fuel burn. Consequently, airlines see the advantage of these new aircraft to reduce their operational cost. Therefore, as airlines continue to renew their ageing fleets of aluminium aircraft, there is going to be an increased need for composite maintenance. However, fulfilling the increased demand for composite repairs is impeded by limited availability of historical damage data, due to the young operational age of these aircraft. Composites are particularly sensitive to impact damage, and understanding the likelihood and the consequence of this type of damage is valuable for maintenance processes such as repair decision-making. The purpose of this dissertation is to predict the risk of impact damage for future composite aircraft and use it to substantiate maintenance decision-making in an operational setting...@en

Airports are important transportation hubs that reside in the heart of modern civilizations.They are of major economic and symbolic value for countries but are thereforealso attractive targets for adversaries. Over the years we have observed successful andunsuccessful terrorist attacks at airports, of which the recent Brussels Airport attack andIstanbul Atatürk Airport attack are two examples.A widely-used method to defend airports against these types of events is that of securityrisk management. Following this approach, security risks are quantified based onthreats, vulnerabilities, and consequences. These risks are then used as a basis to implementsecurity measures that can reduce the risks to acceptable levels. Several securityrisk management approaches were proposed before, such as attack trees and securitygames, but they struggle to include diverse human factors in their analysis. These factorsare inherently present in modern airports, as passengers, employees, and visitors areall humans. Furthermore, existing methods struggle to take other performance metrics,such as efficiency, into account.This thesis addresses these limitations by proposing a novel security risk managementapproach that relies on agent-based models and Monte Carlo simulations. Thisapproach builds on the existing security risk management framework but exploits theadvantages of the agent-based modelling paradigm. Agent-based models allow for theinclusion of rich cognitive, social and organizational models that enable the modellingof human behaviour. Furthermore, agent-based modelling is a suitable paradigm to estimatea variety of performance indicators, including airport efficiency.Two case studieswere performed to assess the performance of our agent-based securityrisk management approach. In these case studies we apply our approach to managesecurity risks at a regional airport, as well as an international airport.@en

With a significant impact on the development of the global economy and society, air transport is predicted to rapidly grow in the coming years. Unfortunately, while delivering positive global economic and social benefits, air transport has also generated an adverse influence on the environment, especially on the quality of life of communities around airports. Air transport is the main cause of noise nuisance in the vicinity of airports, which has been linked to various human health effects, such as cardiovascular diseases, sleep disturbance, hearing loss, and communication interference. This research aims to develop an optimization framework that can effectively address the problems of route design and flight allocation in a linked manner to reduce noise impact and fuel consumption. @en