Using a data-driven approach, a heterogeneous populated agent-based model of an airport security checkpoint is calibrated and validated. It is found that average performance using the model can be replicated to a sufficient degree. The heterogenous population could become useful when different passenger fractions - with different performance per passenger type - are analysed for different security checkpoint configurations. Furthermore, time-dependent agent performance could improve the models validity.

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.

In operational airline crew scheduling, one of the major tasks is to allocate flight activities to individual crew members. This task is referred to as the crew rostering problem; an optimization problem that aims to assign flights to crew members in a cost-effective way, while also considering preferences of crewmembers. Flights are combined into multiple-day activities called pairings that consist of flight duty days and rest period days. For intercontinental long-haul destinations specifically, pairings may take more than a week. During flight duty days of these long-haul pairings, crew members are not situated at the airline base location. During rest period days, however, they have the opportunity to be at their homes or with their families. The pairings that crew members on long-haul crew divisions operate, thus, directly impact their life
apart from work as well as the locations at which they are situated during flight duty days. Therefore, crew preferences are highly valued by crew members, as these provide their only means to influence duties. This is a critical reason for crew preferences to be a recurrent topic in negotiations on collective labour
agreements between crew unions and airlines that influence crew resources. As crew costs are the biggest expense for airlines next to fuel (Belobaba et al., 2015), crew preferences are considered in an environment that is predominantly designed to manage crew resources as efficiently as possible. For long-haul cockpit crew in European airlines, crew preferences can be expressed via requests for the operation of specific pairings. However, the problem with these pairing requests is that the requests concern pairings that commence months later than the pairings considered by the crew rostering problem. This
problem is typically solved only one month before operation of the pairings. It is impractical to consider the pairing requests as part of the crew rostering optimization problem, since the problem size of the optimization problem becomes too large to be solved within practical computation time. In other words, a decision on whether or not to grant a certain pairing request has to be made with limited information available on the consequences of that decision since it concerns a part of the schedule that is not optimized for. When a pairing request is granted for a crew member, this invokes a pre-assigned pairing for this crew member. Such pre-assigned pairings constrain the construction of this crew member’s roster in the crew rostering problem, as only a limited number of options exist to efficiently construct this crew member’s roster. When many of these pre-assigned pairings exist in the schedule, this leads to inefficiencies in the schedule. Such inefficiencies cause loss days without productive activity in the schedule, which are an indicator of inefficient use of crew resources leading to higher crew costs.

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

Tilt-rotor aircraft combine the helicopter’s benefit of VTOL capability and the flight performance that turboprops possess on range, speed and endurance. Tilt-rotor aircraft have the potential to decrease airport congestion and average flight delay in passenger transportation, while benefits are numerous in i.e. search and rescue, disaster relief and military application. Time, money and risk can be significantly reduced through the application of flight trajectory optimization and assessment prior to flight, as this can accurately simulate flight and its related performance limits. A numerical three-dimensional pointmass model for a tilt-rotor type aircraft has been derived to fill the gaps that currently exist in tilt-rotor modelling and the understanding of their flight mechanics. After validation, the model is applied to optimize integral flight trajectories using optimal control theory in GPOPS. It was concluded that the derived model is valid under its assumptions and limitations. From the tilt-rotor flight behaviour it was assessed that specific nacelle tilting behaviour could be observed, as the tilt-rotor can exploit its unique rotor tilting capability. Since the power required is the driving factor in most flight optimization, the nacelle angle is driven by the aircraft’s velocity and altitude. This model can be used in theoretical flight trajectory optimization studies. The model can be adapted to account for specific requirements and aircraft types.

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.

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.

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 two performance areas “security” and “efficiency” in airport security operations cannot be analyzed apart from each other, since influencing one has a large impact on the other. In this research agent-based modelling is used to analyze dynamic relations and trade-offs between security and efficiency within airport security operations. Detailed decision making models and theories are used to model autonomous and diverse agents and to place them in a dynamic airport security environment. Results of experiments with the model are put in context of trade-off theories and a link with practice is made by comparing results with a real airport.

The purpose of this work is as follows. Military aircraft are enormous investments for a nation. The systems lifecycle for aircraft spans decades wherein aging effects increase maintenance and operations costs over time. At some point, the deterioration of a fleet of aircraft erodes the capability of those assets below an acceptable threshold, thus triggering retirement planning by a military. Questions arise about how to retire a fleet, including how many aircraft should be retired, when those aircraft should be retired and which aircraft should be chosen. There are few military aircraft fleets that are retired each year, and even fewer managers who understand the aircraft retirement puzzle. This work addresses these questions. The purpose was to provide fleet managers with a comprehensive framework to guide decision-making, as well as to build tools and a standard guidance framework for fleet managers to implement.

In terms of methodology, in the absence of directly applicable existing research in this field, fleet management concepts and modelling approaches were studied in related fields and then applied to the military fleet retirement problem. The vital first approach to the problem required the baselining of military aircraft fleets given structural loading data and utilization histories. Database analysis and trending algorithms were written to draw correlations between existing data and structural fatigue effects. This work then implemented a greedy algorithm model to solve the individual aircraft retirement scheme. That led to a mixed-integer linear programming approach to optimize a fleet utilization and rotation model. Combined, these methods provided concrete steps for the fleet retirement decision framework, which followed established methods for designing a decision support framework. Throughout the work, a consistent case study fleet (United States Air Force’s A 10 Thunderbolt II) was utilized to provide validation of the methods, while secondary case studies and validation techniques were employed to test applicability of the methods to other military aircraft fleets and other capital asset types.

In terms of concrete research results from the work carried out, this dissertation discovered that a framework for military aircraft fleet retirement decisions was a needed contribution to the field. In the process of building that framework, other valuable results were obtained. It was found that aircraft utilization information could be correlated to cyclic loading data on an individual aircraft level. This revealed patterns in aircraft fleets showing which mission types and basing locations either increased or decreased structural degradation. Using that information led to the result that a fleet manager could determine which aircraft to retire prior to others while optimizing an objective function related to fleet cost, fleet utility or the ratio thereof. It was also found that a fleet manager could selectively utilize individual aircraft at particular bases flying particular missions to prolong or hasten the structural degradation of those aircraft. This led to the result that a fleet manager could therefore forecast retirement dates for an entire fleet, subpopulations within that fleet or individual assets.

From the research carried out, it is emphatically concluded that the results imply that a fleet manager beginning with only aircraft usage data can actively manage a fleet of aircraft to extract residual value from the fleet prior to retirement. This work showed that resource allocation could be improved by utilizing a mixed integer linear program to schedule asset retirements. Further, this work illustrated how a management strategy could impact future usage levels in a way to extend useful lifetime. With a capital asset as critical to national defense and as expensive to acquire, operate and retire as military aircraft, focusing on the end-of-life phase of the systems lifecycle not only promotes forward thinking but also provides potential cost savings. This work’s limitations included its focus on military aircraft instead of all capital assets and that the methods were not implemented in an actual fleet environment. This dissertation demonstrated that a flexible framework with core modelling elements is a tool capable of solving the problem of aircraft fleet retirement decisions. Fleet managers both military and otherwise should investigate the applicability of the methods and findings in this dissertation to their own challenges. Future research must include application of the methods to an actual operating fleet. Also, the methods should be applied to other capital asset classes including military equipment and commercial equipment.
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In this thesis, a contribution has been made to the development of a simulation framework which is capable of realistically simulating market dynamics with respect to competition and passenger choice in the European aviation market. In total, 22 routes have been included into the simulation framework, with airports including those in Amsterdam, Copenhagen, Frankfurt, London and Madrid. To achieve this, a demand generation model, demand allocation model and market competition model have been combined. Additionally, the simulation framework is compatible with exogenous competitor inputs to create a game environment while hosting dynamic competition by the computer player.

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.

In aircraft maintenance, the initial maintenance schedule of an aircraft is highly dependent on the maintenance planning document. This document, set up by manufacturers, regulators and operators, contains all the requirements for the initial maintenance schedule of a specific aircraft. It serves as a reference for the development of an optimised maintenance schedule by the operator. The initial workload distributions obtained by using the task data in this document, show a high and irregular fluctuation along the operational life of the aircraft. After the application of common task packaging methods, the operator can improve the initial maintenance schedule, but workload fluctuations often persist after these measures. This fluctuation is considered to be unfavourable because of several reasons; maintenance resources are used inefficiently, workforce scheduling operations on fleet level can become very complex and maintenance delays are more likely to occur as a consequence.
In this research, a generic optimisation method is developed focused on the minimisation of the workload fluctuation resulting from the initial maintenance schedule of the aircraft. This method uses a single task reallocation process that systematically eliminates workload excesses by locally reducing task intervals. A software application was built for a fast, a more dynamic and a more customized reallocation process, using several variable inputs.
This application has shown to be very effective in eliminating the workload fluctuation along the lifecycle of an aircraft. As an indication, from the data of the maintenance planning document of an A330, the average fluctuation of the workload among the C-checks over a lifecycle of 15 years, was reduced by more than 79%. Theoretically, this minimisation of the fluctuation could lead to a workforce size reduction of 41% for the C-checks only. These figures are even higher for the A-checks of the aircraft over the same period.
Together with the simplification of the workforce scheduling activities and a more efficient usage of maintenance resources with constant workloads, these advantages are thought to outweigh the negative effects associated with the task interval reductions in the reallocation process, especially when an entire fleet of aircrafts is considered.
Through the implementation of process improvements and additional features, the efficiency and the generic capacity of this application can be further improved with the objective to rapidly generate optimal maintenance schedules that are adaptable to different operational scenarios or constraints.

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.

Airports around the world continue to face issues related to the environmental impact of aviation. Mitigation measures have therefore been implemented at many of these airports. Attaining an optimal combination of these mitigation measures is a complex process and because of these complexities, this process does not always result in the most efficient solution in terms of environmental impact.
It is expected that some of the inefficiencies of mitigation measures can be eliminated by using a process that is based on three main principles: to use mathematical optimisation in order to select the best mitigation options, to evaluate multiple performance areas simultaneously, and to evaluate multiple mitigation options at multiple levels of aggregation simultaneously. These three principles have therefore been implemented as capabilities in an integrated decision support system, to determine whether such a system could help improve the airport environmental management process.
Based on the results obtained with the developed support system, the benefits resulting from each of the three capabilities are demonstrated. But ultimately, it is shown that especially the combination of these three capabilities, integrated into a single support system, contributes to improving the airport environmental management process.@en

This report is the final report in a series of four reports that deals with the design of an advanced regional aircraft. The first step in the design is to determine the overall configuration of the ARA. By identifying the feasible configurations based on a literature study and performing a trade-o_, the conventional low wing with GTF engines underneath the wings configuration is found to be the optimal configuration for the ARA. After selecting the aircraft configuration, the preliminary subsystem design is initiated. Class I and II weight estimations are performed and a MTOW of 34500kg is determined. The selected wing planform is a two-piece complex sweptback planform with a wing area of 105m2 and a wing span of 30.7m. The thrust will be provided by two PW1217G GTFs with a maximum thrust of 76kN each. For the fuselage design, several configuration options are analysed taking into account structural and aerodynamic considerations. A trade-o_ is performed and the 4 abreast configuration with cargo in the tail is found to be the best choice. The tricycle configuration is chosen for the landing gear. The main gear is positioned 17.1m from the nose, while the nose gear is positioned 3.6m from the nose. The control surfaces comprising ailerons, spoilerons, elevators and rudder, are sized for extreme load cases. For roll control at low speeds outboard ailerons are used and spoilerons are used for roll control at high speeds. The elevators are sized to meet take-o_ and trim requirements. The rudder is sized to counteract the yawing moment with one-engine inoperative. Furthermore, the high-lift devices are sized. It is found that in order to fulfill landing and take-o_ requirements double-slotted Fowler flaps are required...