The Netherlands has positioned itself as a transit nation within the context of military mobility on the European continent. By making its dense and well-developed infrastructure system available, the country facilitates rapid deployment from its seaports to destinations in neighbouring countries. However, using civilian infrastructure for military transits could potentially cause serious disruptions to the travel time of civilians due to capacity limitations. Recognising this tension, the Dutch Ministry of Defence (DMoD) is responsible for balancing the simultaneous use of transportation infrastructure by civil and military vehicles and seeks interventions in the system that can aid them.

The study follows a three-phase methodology:
1. The system analysis examines the transportation system with influences of military mobility by combining insights on the Dutch infrastructure’s characteristics, military movements, and possible intervention strategies for the DMoD.
2. The model development results in a mesoscopic agent-based model (ABM) in NetLogo that simulates simultaneous infrastructure use by civil and military vehicles on synthetic Dutch-like infrastructure networks.
3. The experimentation tests different intervention strategies by simulating their effects on military transit time and travel time loss in the transportation system.

The simulation experiments show that using trains and barge ships is very effective in minimising travel time loss for civilian vehicles, but results in a longer duration of the military transit operation. Combining the road, rail and inland waterway modalities, applying short intervals between vehicles and using long road convoys, are effective in minimising that duration, but cause more travel time loss. The combination of these latter interventions with the avoidance of rush hours and/or daytime balances the trade-off between travel time loss and transit time. Additionally, operating short road convoys should be avoided as they serve neither of the two goals and reserving roads for military vehicles only does not significantly change either of the two goals. This differentiation stresses the importance of setting out the intent of operations - minimising travel time loss or the transit time - before planning them.

The sensitivities and limitations of the research, however, should be kept in mind when interpreting these conclusions. Especially the assumption that the speed flow of highways is capped at 80 km/h on two-lane roads when there are military convoys, strongly affects model outcomes. Other limitations are that the research neglected traffic congestion caused by accidents and road works, and the availability of transport vehicles. Microscopic research on the interaction between civil vehicles and road convoys is suggested to gain insights into these parts of the system.

In addition to partly filling the knowledge gap of simultaneous infrastructure use by military and civilian vehicles, this research also contributes to the scientific fields of managing multi-modal transport and traffic and modelling of transportation systems. Regarding multi-modality, four different management categories were studied, of which three are concluded to be effective: traffic demand management, congestion avoidance, and integrating multiple transport modalities. Prioritisation of roads for military vehicles, however, did not result in significant system-wide benefits in this context.

In the field of transportation modelling, this research confirms the applicability of Agent-Based Modelling (ABM) to this field and introduces a network generation algorithm used before the transportation simulation. The benefits of this algorithm are that it requires less data about the network, which was useful due to the sensitive character of this research topic, as well as a broader applicability of the model, because adjustments in the network generator allow simulation in a different context.

Neonatal care in the Netherlands is under significant strain due to severe bed capacity and staffing shortages, particularly in the southwestern region, where 36% of hospitals have closed neonatal beds. The current neonatal care system, structured into three escalating levels of specialization—neonatal medium care, high care, and Neonatal Intensive Care Units (NICUs)—struggles to meet demand, resulting in high occupancy rates and frequent patient transfers. This study developed a discrete-event simulation model to analyze and address bed capacity shortages within these staffing limitations. The model, based on regional perinatal birth data, evaluated the impact of various interventions on bed occupancy and transfer rates across different care levels.

Key findings revealed that reducing the length of stay, adjusting admission rates, and altering patient pathways could alleviate capacity pressures. While individual interventions offered modest improvements, a combined strategy integrating multiple approaches significantly reduced occupancy rates and patient transfers. This research underscores the need for a multifaceted approach to address neonatal care challenges and highlights the potential of simulation modeling as a tool for informed decision-making in healthcare resource management. The study’s insights offer a pathway toward sustainable neonatal care despite ongoing constraints.

The integration of air and rail travel has been increasingly adopted by major European airlines such as Air France-KLM and Lufthansa, as part of their efforts to reduce short-haul flights within Europe. This in order to improve local air quality, reduce greenhouse gas emissions, and contribute to the European Commission's goal of achieving Net Zero emissions by 2050. While much of the existing literature has focused on the substitution of short-haul flights with high-speed rail for direct origin-destination travel, this study explores the integration of air and rail transport in multi-legged journeys. Specifically, it examines the incorporation of high-speed rail as spokes in the hub-and-spoke model used by major airlines.

This study aims to quantify the preferences of potential air/rail travellers in order to provide airlines, airports, and other stakeholders with insights into the key features such as service, comfort and operational factors of an integrated air/rail service. A stated preference survey, distributed among the Dutch Railways (NS) panel, was used to collect data, with a total of 541 respondents. The data is analysed using linear regression analyses and a multinomial logit model to estimate coefficients for the included parameters. Additionally, a latent class choice model was used to capture heterogeneity among the respondents in order to identify several consumer segments.

This study found that integrated ticketing was the most attractive service attribute, while priority lanes provided the highest comfort. Travel cost emerged as the most crucial determinant for choosing air/rail over air/air journeys, followed closely by the spend at the airport and train station in an air/rail journey. The latent class choice model revealed that younger, less experienced travellers were most likely to switch from air/air to air/rail, particularly in scenarios involving high levels of service and comfort integration.

This latent class choice model identified three customer groups: waiting time disfavourites (young, less experienced travellers), plane lovers (middle-aged, experienced travellers), and air/rail service admirers (mostly elderly, retired). Waiting time disfavourites, who prioritize minimizing waiting time at airports and stations, choose air/rail in 53.0% of cases. Plane lovers strongly prefer air/air, with 85.1% favouring it. Air/rail service admirers, valuing service highly, choose air/rail in 97.9% of cases.

According to a real-life scenario analysis about the introduction of an air/rail terminal at Frankfurt Airport, the current offered services and comfort features increases the share of air/rail from 31.4% (102 passengers) to 45.7% (148 passengers), which equals a substitution rate of 14.3%. With further integration, the air/rail share could be increased to 76.4% (249 passengers) among the first group of young, less experienced, travellers. However, the impact on plane lovers is minimal, with a maximum substitution rate of 19.7%. The third group already favours air/rail over air/air, so a maximum substitution rate of 2.7% can be achieved. However, overall, a maximum air/rail share of 67.7% can be reached, equal to 173 additional passengers choosing for air/rail.

During the COVID-19 pandemic, many elective surgeries had to be rescheduled as resources suchas beds and ventilators were reallocated, causing significant delays in patient care. To avoid such disruptions in the future effective resource management and planning approaches for elective surgery are essential. Optimisation is a commonly used method to enhance elective operating theatre scheduling, it is a technique used for mathematical modeling. It can integrate various criteria to maximise benefits and minimise costs within specified constraints. However, optimisation alone often falls short in addressing the complexities and required flexibility of operating theatre scheduling. This is where simulation can build upon these limitations. Simulation techniques can model system behaviour by replicating real-world processes, introduce uncertainty, and evaluate responses to different policies, helping to identify bottlenecks and improve system efficiency. Sequencing optimisation and simulation can test theoretically sound solutions under real-world uncertainties and complexities. This leads to the following research question:

“How can Discrete Event Simulation evaluate optimisation-generated Master Surgery Schedules for operating theatres?”

The research question focuses on a Master Surgery Schedule, which coordinates different surgical specialities, sharing OTs and pre- and post-surgery resources. While this approach increases resource utilisation, it also adds complexity by needing to accommodate the varying requirements of each speciality. The research aims to evaluate different Master Surgery Schedule using Discrete Event Simulation to contribute to the optimisation model.

Overall, the use of the Discrete Event Simulation model provided detailed insights into the
performance of various schedules. Testing these schedules under different types of uncertainty
demonstrated their robustness, as the behaviour remained consistent across scenarios. By
comparing the schedules side by side, the simulation model effectively evaluated their
effectiveness, ensuring the intended purposes were served and identifying areas for improvement.

The research specifically considers Discrete Event Simulation as this method can queue patients for different resources and have them move throughout the system based on decision rules. It is also a standard applied method for modelling healthcare systems. Both Discrete Event Simulation and optimisation are standard methods for improving OT scheduling. Even sequencing the methods has been proven to contribute to OT scheduling. However, applying this approach to a Master Surgery Schedule is new and introduces additional complexity related to the shared OTs and pre- and postsurgery resources.

To explore this research question, a case study was conducted at Sophia Children’s Hospital in
Rotterdam. Previous research developed the Master Surgery Schedule using an optimization
model, resulting in four different schedules. These schedules either balanced ward leveling and operating theatre utilization equally or prioritized ward leveling. They also varied in computational requirements, as the model updated the bed availability either every 15 minutes or every hour. This research categorizes 18 different surgical departments into 50 groups based on their surgery duration and length of stay, assigning time slots to these groups in the schedules.

This research uses these groupings to fit different distributions for the input variables of length of stay and surgery duration. Five different types of distributions were tested for each group, and the best-fitting distribution was assigned. Additionally, previous research provides a schedule for ward capacity and establishes decision rules for patient management. In collaboration with the hospital, the probability of Intensive Care assignment and the various decision rules were validated.

The sensitivity analysis revealed a slight overestimation of surgery duration compared to the test data, but this did not significantly impact outcomes. The model is more vulnerable to ward capacity and length of stay, which were then selected for scenario analysis. The schedule prioritising ward levelling and checking availability every 15 minutes outperformed others, effectively addressing ward unavailability, the primary bottleneck. However, increasing capacity improves the number of successful surgeries but also leads to more cancellations due to operating theatre unavailability and increased overtime occurrences. This highlighted a trade-off between operating theatre utilisation and other Key Performance Indicators.

The sensitivity analysis revealed a slight overestimation of surgery duration compared to the test data, but this did not significantly impact the overall outcomes. The model was found to be more sensitive to ward capacity and length of stay, which were then selected for scenario analysis. Among the different schedules, the one prioritizing ward leveling and checking bed availability every 15 minutes outperformed the others, effectively addressing ward unavailability, which was identified as the primary bottleneck. However, while increasing ward capacity improved the number of successful surgeries, it also resulted in more cancellations due to operating theatre unavailability and increased overtime occurrences. This finding highlighted a trade-off between operating theatre utilization and other Key Performance Indicators.

In line with the literature, the research reveals that striving for increased operating theatre
utilisation puts excessive pressure on other resources. Ward unavailability is identified as the main reason for surgery cancellations, indicating that wards require even greater focus. An increase in ward capacity had the best outcomes during the scenario analysis, showing that this is the biggest bottleneck in the system.

The simulation model identified new parameters for the optimisation model and highlighted
weaknesses in the system to be improved upon. Future research should explore ward capacity in greater detail and develop better methods for sharing resources across different operating theatres and wards to reduce the differences in utilisation. Additionally, further research could investigate other causes of surgery cancellations and refine the definitions of surgery groups

The transition to renewable energy is vital for the Netherlands to meet the National Climate Agreement's goal of a 95% reduction in CO2 emissions by 2050. Residential energy, accounting for 20% of the nation's total, is a key area for local municipalities, which are crucial in implementing decarbonisation measures. Solar photovoltaics (PV) have rapidly emerged as a leading technology, achieving the government's target of 7 TWh from small onshore projects by 2022, eight years ahead of schedule.

However, the rapid adoption of solar PV presents several challenges for policymakers, particularly at the municipal level. These challenges include grid integration, financing, and ensuring equitable access to solar technology. Addressing these issues requires a deep understanding of the factors influencing solar PV adoption and the dynamics at play within different municipalities. Inverse modelling presents a methodology to explore these complexities. This technique involves working backwards from observed outcomes to identify and understand the underlying processes and parameters that generated those results. The application of inverse modelling is, however, very novel. Therefore, this thesis aims to lay the groundwork for future applications of inverse modelling by exploring the factors that influence solar PV adoption in Dutch municipalities. To achieve this goal, the following research question has been formulated:

How can inverse modelling contribute to uncovering plausible explanations for residential solar PV adoption dynamics in Dutch municipalities?

The study employs inverse modelling combined with agent-based modelling to analyse adoption patterns across different municipalities, utilising Random Search and Bayesian Search algorithms. Findings indicate that social factors are often more influential than economic incentives in driving solar PV adoption, emphasising the role of community engagement and peer effects.

These insights underscore the potential of inverse modelling to reveal the complex interplay of factors influencing solar PV adoption. This study demonstrates that inverse modelling can effectively identify patterns and key determinants within municipal data. The research suggests that future efforts should refine this approach, considering multiple agent-based models and alternative methodologies to enhance robustness and accuracy, paving the way for more comprehensive and accurate research in the future.

In conclusion, inverse modelling provides a powerful tool for uncovering the dynamics of solar PV adoption in Dutch municipalities by providing a systematic approach to understanding the underlying factors of the system and its intentions. Through IM, the key determinants that drive solar PV adoption can be inferred by analysing observed patterns and behaviours within the municipalities. This approach is additionally powerful because it does not rely solely on pre-existing theories or assumptions, as with forward modelling. Instead, it derives insights directly from the data.

Model-based testing (MBT) is applied for the verification of simple simulation models used in modeling & simulation for decision-making. It is found that MBT of simulation models is not discussed much in literature, and that existing case studies on this topic do not give guidelines for test model design. It is established that testing simulation models is a difficult problem, as simulation models have many states, a time component, reactiveness, and inherent stochasticity. It is hypothesized that MBT could lead to more efficient and effective testing, and could fit within a modular approach to verification of simulation models. A focus is made on verification that discrete-event simulation models meet their functional requirements, by using automated test packages.

Options for test model design are explored in three case studies where automated test suites are developed for three simple simulation models that form the systems under test (SUT). Options from existing taxonomies are first selected for this, based on their relevance to the properties of simulation models that make dynamic verification difficult. In the process, an existing MBT software tool is selected and evaluated.

The main aspects of MBT that are explored are: nondeterministic test models and SUTs, transition-based test models, online and reactive test execution, specification of oracles at different levels, time advancement of the SUT, the distinction between black-box and white-box approaches, and the problem of test abstraction. Examples are made where the test model, an extended finite state machine (EFSM), is an abstract version of the SUT, that includes its most relevant state transitions. An approach to testing is used, where the test model follows the SUT’s state and selects relevant assertions accordingly. This approach limits the use of stochasticity in the test model, that is often done in MBT.

It is found that MBT can be useful for three types of dynamic verification: of the dynamic behavior, of the (end) results, and of the response to inputs. The first type is explored with a step-by-step approach where oracles are checked after each time advancement in the SUT. The second type is implemented without the use of MBT techniques, by extending the functionality of an existing MBT tool to allow for analysis of multiple simulation runs. The third type is not explored but approaches for it are defined.

The project shows the feasibility of MBT of simulation models and shows different options for test model design. Not discussed are the adequacy of the developed test packages in detecting faults, and the efficiency, effectiveness and quality of MBT for simulation models in general.

As one of the largest innovation hubs in the world, the Brainport Eindhoven region experiences significant economic and population growth. However, with this success comes a challenge: the transportation infrastructure is increasingly strained, with new housing developments and population growth escalating the transport demand. The economic growth includes plans by hightech giant ASML to roughly double its workforce in Veldhoven to 35,000 by 2030, which poses significant consequences for accessibility, environmental quality, and safety unless effective policy measures are implemented...

The digitalization of European universities’ tool infrastructure has transformed how institutions operate and deliver education to students, from sharing content on learning management systems to hosting lectures on video-conferencing platforms. However, despite many new benefits of digital education technologies (DETs) and their contribution to reaching the United Nations Sustainable Development Goal 4 targets for Quality Education, it also comes with new challenges like user privacy, environmental impacts, and shifting power dynamics between institutions and service providers. Additionally, concerns have been raised regarding the responsible development and longevity of the university’s digital infrastructure given the recent rapid digitalization trend and how new DETs are selected.

Sustainability assessment can be a useful model to evaluate an institution’s DET selection process as it provides a holistic evaluation through a multidimensional perspective to develop a more responsible and future-proof approach to digital education infrastructure. However, a multidimensional sustainability analysis has not been applied in the context of DET selection. Therefore, it is unclear to decision-makers what sustainable DET looks like and what role sustainability plays in the DET selection process. This study addressed this gap by answering the following question: How are European higher education institutions incorporating sustainability into selecting digital education technologies?

The sustainability dimensions of DETs were formulated by conducting a literature review of contemporary models, encompassing the environmental, social, and technological aspects. A more sustainable DET increases the positive impact along each of these dimensions. An environmentally sustain- able DET preserves and protects natural resources by reducing the environmental impact through its hardware and software. A socially sustainable DET increases equal access to education for all learners, regardless of socioeconomic status, disabilities, or geographic location while preserving individual privacy. A technologically sustainable DET is long-lasting, possesses the necessary functionalities, and balances a tool’s simplicity, openness, and ownership. While most sustainability models include the economic dimension, due to the university’s non-profit nature and the common prioritization of economic factors above other criteria in decision-making, this study excluded the economic dimension to examine the other dimensions more closely. Furthermore, the pedagogical dimension was omitted due to its sustainability considerations typically arising after the implementation of a DET, rather than during its selection stage and therefore is beyond the scope of this research.

Four key actors involved in the DET selection process were identified through an actor analysis. These include the university’s Head of IT who oversees the institution’s infrastructure system and their IT tool specialists who provide technical expertise, service providers whose products comprise the DET market, and education associations who help universities procure DETs. Ten semi-structured interviews were conducted with European university Heads of IT to gather data on the current DET selection process and the challenges institutions face when incorporating sustainability into DET selection.

The sustainability dimensions were used in conjunction with grounded theory open and axial coding analysis to evaluate the sustainability of current DET selection processes. The results showed that decision-makers predominantly utilize the EU-regulated tendering process to select DETs, which comprises minimal sustainability criteria while assigning significant importance to the economic factor (i.e., DET price). Additionally, interviewees shared they prioritize social and technological sustainability, specifically the privacy, data security, and functionality of DETs over other sustainability criteria. On the other hand, environmental sustainability is underrepresented in DET selection criteria. This is primarily due to the lack of available data and initiatives collecting DET environmental impact metrics, making it difficult for decision-makers to create relevant requirements and kickout criteria to compare DET options based on environmental sustainability. Finally, the analysis illustrated the three most common challenges that hinder sustainable DET selection are the limited financial and human resources, the insignificant or lack of sustainability criterion weighting, and the long and inflexible tender process.

Overall, this study contributes to filling the knowledge gap in understanding the sustainability of current European universities’ DET selection process and highlights key challenges decision-makers and researchers should focus on to improve the sustainability of digital education technologies. Future research can build on this work by expanding the scope beyond Northwestern European institutions, interviewing other decision-maker actors, and developing a standardized selection process for sustainable DET selection.

Additionally, recommendations were made to the four actor groups as well as general advice for universities to increase DET sustainability. The Head of IT should prioritize the environmental aspect in DET criteria and collaborate with service providers to address environmental impact metrics. They should also encourage the development of new tools by teachers and students. The IT tool specialist should engage in co-development with service providers for better tool support and to ensure a secure and functional digital infrastructure. Service providers need to align their products with sustainability criteria, propose pilot projects to universities, and share environmental impact metrics with relevant stakeholders. Education associations should organize collective efforts to enhance the sustainability of the DET tendering process and offer streamlined services like joint procurement and model contracts to simplify the selection process. Universities could transition to renewable energy to reduce DET’s carbon footprint, implement e-waste recycling and disposal programs, and support research into sustainable DET.

This master thesis aimed to develop an optimized operating strategy for an electricity grid incorporating hydrogen storage, while considering three key performance indicators: financial costs, reliability, and sustainability. The central research question guiding this study was: "How can the electricity demand be reliably met by a system combining solar power, conventional power supply, and hydrogen systems, optimized for sustainability, cost-efficiency, and reliability through adjustment of the operating strategy?"

The study quantified all key performance indicators in terms of costs, with sustainability measured by the price of carbon credits, providing a quantifiable measure of CO2 emissions. A discrete event model was utilized to simulate an electricity system on an hourly basis, integrating solar energy, a hydrogen system, and non-sustainable energy sources. The particle swarm optimization algorithm, enhanced with linear decay, was employed to identify the most optimal operating strategies within this model. Four distinct scenarios were formulated to analyze the optimal operating strategy under varying circumstances.
The findings revealed that when the price of hydrogen was significantly higher than that of conventional resources, the optimal strategy favored the avoidance of hydrogen electricity due to its high costs. Electrolysis costs and fuel cell expenses were identified as the primary cost drivers. Conversely, in scenarios where hydrogen was competitive with conventional resources, the optimal operating strategy incorporated a small portion of non-sustainable electricity while maximizing the use of hydrogen-based systems.

In conclusion, this master thesis addressed the research question by optimizing the operating strategy of an electricity grid integrating hydrogen technology. Presently, the financial advantages of non-sustainable electricity outweigh the sustainability benefits of hydrogen electricity. The study highlighted the need for substantial cost reductions in hydrogen system components and an increased price of carbon credits to realize a future scenario where hydrogen electricity competes with natural gas.

Military decision-making involves art and science and aims to translate strategic goals into tactical actions for achieving specific outcomes. It relies on a commander’s capacity to navigate complex and sometimes ambiguous situations to develop detailed plans and actionable orders. At the military tactical level, decisions focus on executing tactical mission tasks that contribute to achieving operational and strategic objectives. Tactical decision-making within the Dutch Army involves a step-by-step rational decision-making process, mainly focused on analytical preparations for battle. An operational analysis or wargame of the developed plans is crucial in military decision-making. Such an analysis offers valuable insights into the most favorable courses of action compared with the presumed plans of other actors. The execution of a wargame empowers a commander to determine the optimal course of action.

Operational analysis, or wargaming, is a formalized military process that technology can support. Modern wargaming originated from the Prussian Army in the 1820s and has since spread to many other armed forces. Wargaming assesses the forces’ positions, strengths/weaknesses, opportunities, and environmental factors using doctrine, experience, and intuition. It is an iterative process highlighting critical tasks and tactical possibilities, often aided by modern information technology to increase efficiency.

Armed forces are using information technology to support decision-making, whereby Modeling and Simulation systems offer valuable tools for better situational understanding and assessing interventions’ effects. These simulations can provide the forces with crucial data to develop and assess possible courses of action. Military simulation spans various domains, from large-scale field exercises to abstract computational models. While simulations can accelerate the development of military knowledge and experience, it is essential to ensure that increased computing power aligns with a comprehensive understanding of warfare.

Within contemporary military simulation, there is a move from comprehensive monolithic systems to component-based simulation systems to facilitate innovation, flexibility, and cost-effectiveness. While reusability and composability are crucial for military simulation, they have been challenging. Reuse offers various technical, business, and economic benefits. Composability is vital for the system of systems that form contemporary military simulation environments, where linkage is challenging due to differing resolutions and levels of abstraction. High-resolution models help military staff to understand phenomena, while low-resolution models aid analytical understanding qualitatively. In these simulation environments, aggregate-level simulations represent military units with multiple entities, while entity-level simulations depict individuals. However, composing both types of simulation within military wargaming to support decision-making remains challenging. Based on this analysis of contemporary military simulation, the following main research question is formulated:

What are design principles for composable elements that can be used in a simulation system for military wargames?

Because military decision-making processes are not intentionally designed to be represented by simulation systems, the wargaming elements derived from relevant perspectives on military wargaming provide insight into which underlying principles may be usable when designing a wargaming simulation. The following principles have been deduced from military wargaming: a representative wargaming experience (principle of recognizability); a user interface that is designed for the users’ intended purpose for the system (principle of utilizability); traceable simulation results that create insightful knowledge (principle of traceability); and adaptable systems that can integrate changes and developments within military doctrine (principle of adaptability).

The challenges arising from Modeling & Simulation research provide rigorous knowledge for the design of military wargaming simulation systems. The following principles are derived from the selected scientific literature: consistency in the models and the attribute states in the wargame simulation system to maintain its validity (principle of consistency); a designer’s awareness and understanding of the implications of design choices for performance (principle of coherence); an ability to combine, recombine, configure, and reconfigure sets of components (principle of composability); and a possibility to represent interactions between different military units and echelons (principle of interoperability).

A system requirements structure links requirements to the above principles to assess the extent to which the identified design principles are usable, ultimately aligning with the defined objectives. Evaluation of the requirements reveals that a wargame simulation system must have a utilizable design that represents real-world wargaming, traceable results that create insightful knowledge, adaptable models to assimilate to evolving military doctrine, combinable, configurable, and interoperable components; it should have a recognizable user interface and consistent model behavior; while a such a simulation system could have a coherent design that incorporates the used Run-Time Infrastructure.

The wargame simulation system prototype within this research is designed to support a specific activity within a military decision-making process, the operational analysis, rather than replacing the entire process. While promising, it is still a prototype with limited functionalities. The prototype is tailored to evaluate design principles by a concise military scenario; however, it can be extended to encompass additional tactical tasks or units, showcasing the flexibility of the flow-based design for creating a Course of Action. While the prototype is intended for evaluating the identified design principles, its current utility for practical military operational analysis remains limited.

This research provides both military practitioners and Modeling & Simulation scientists the opportunity for further field experiments and theoretically explore the identified design principles for composable simulation elements that can be used in a flow-based wargame simulation system. Future scientific research should further improve the envisioned simulation concept into a usable wargame simulation system so military commanders and their staffs can exploit the power of simulation while conducting a wargame.

Being able to timely access and share accurate information to all stakeholders that can influence a supply chain is referred to as supply chain visibility. Supply chain visibility is crucial for increasing the resilience of a supply chain as this helps to proactively plan and design interventions to undesirable events. Various methods are available to increase supply chain visibility, often by increasing the accuracy of estimating future events. Many of these methods are relianton (large volumes of ) data, which is problematic because data on supply chains is often not accessible, expensive to acquire, and difficult to process. Therefore, methods to increase supply chain visibility in situations with limited data availability are needed.

 Modeling and simulation can be applied to situations with limited data availability, but has a limited predictive power for complex systems. Data assimilation in discrete event simulation (DES DA) is a method to increase the predictive power of modeling and simulation. This method aims to approximate system states based on real-time measurements to increase the accuracy of simulation models. DES DA has not yet been applied to supply chains and the dependency on data availability still needs to be explored. The goal of this research isto develop a DES DA algorithm that can accurately estimate future events in supply chains and test this algorithm for different levels of data availability. This will give insight in the relation between data availability and the accuracy of the proposed DES DA algorithm. During the development of this algorithm the typical supply chain challenges of high dimensionality and future state estimation are addressed.The proposed DES DA algorithm shows to have a higher predictive power (accuracy) than the results of a simulation exercise without assimilation. To allow for maximal reproducibility and facilitate future work, all adjustments to the simulation algorithm are extensively described in the body of this thesis.

Analysis of the performance of the proposed algorithm under different levels of data availability shows that limited data availability does not necessarily result in less accurate estimations. Less observations can can result in similar or even more accurate estimations. However, this can only be the case if the missing data is not part of the set of crucial data sources. If the missing data points are part of these crucial data sources the  accuracy of the data assimilation algorithm detoriates and the algorithm starts to under fit. Under fitting occurs when a model uses input variables that are not significant enough to determine a meaningful relationship between the input and output variables. 

If limited data availability is the result of omitting data sources that are not part of the crucial data sources, the accuracy of the data assimilation algorithm can increase. Using less datapoints helps combatting the curse of dimensionality which results in more accurate estimations. This research has shown that crucial data points for supply chains are the data sources that serve as arrival sensors of entities at the system boundaries. The arrival sensors reduce the uncertainty of future state estimations by accurately estimating the number of the entities in the system and the moment of arrival of these entities. Especially the accurate estimation of the moment of arrival reduces the variation in the estimations of the particles and thereby increases the accuracy of the estimation of future events.

Covid-19 has proven how uncertain the future can be. However, traffic models fail to fully acknowledge this uncertainty. Therefore, decisions made based on these models are not very robust. In this research, Robust Decision Making (RDM) method is applied on a macro-level traffic model made for the municipality of Groningen. This method, consisting of 5 steps, can make the decision making process more robust. First RDM frames the system in which the decision is taken. Then, within the boundaries of the system, the model in run on a large set of possible futures. These futures are picked based on Latin hypercube sampling. Finally, with the results obtained, scenario discovery and global sensitivity tools can be applied. These tools help to find regions of interest within the sample space and to analyse how different variables influence each other. Thereby, based on robustness metrics the most robust policy can be chosen. With the application of RDM in traffic models good policies and interesting scenarios are found. However, due to the fact that traffic models are often over fitted, the run time of the model is quite long and the level of interaction quite low. This makes the application of RDM both difficult and hard to fully exploit. The discussion of this research hands some possible tools to manage the run time. Multi resolution modelling and the use of providers like Amazon web services are two of the most prominent options. Thereby, more in-depth deep uncertainty, like Multi Objective RDM (MORDM) methods are possible when the run time can be better managed.

Emerging technologies are revolutionizing organizational growth, productivity and in- vestments more than ever before. One such technology that has come into the limelight in the past few years is the Digital Twin. The so-called ’digital twin’ is a real-time virtual replica (representation) of any given physical asset/object. The full-potential of a digital twin lies in its ability to not only communicate with the physical asset, but also control it remotely. Although the concept of digital twins is more than a decade old, digital twin initiatives are now been deployed in the manufacturing, automotive and healthcare industries among others. One such industry, experimenting with digital twins is the Energy sector. The objective of this research was to perform an exploratory investigation into the adoption of Digital Twins in the Dutch Energy sector predominately by Trans- mission System Operators (TSOs) and Distribution System Operators (DSOs). The investigation comprised of four elements: establishing the most fitting technology adoption model when it comes to digital twins, determining the relevant adoption variables, exploring the perception of digital twins in the industry and investigating the relationship between absorptive capacity and organizational characteristics. The research method deployed for the former two elements was desk research, whereas the latter elements were probed by conducting (semi-structured) interviews and targeted questionnaires respectively. There were a total of nine participants involved in this research which included technology adoption decision-makers having a wide range of work experience (1-5 years to 20+ years) from the five of the eight TSO/DSOs of the Netherlands.
The research found that the most fitting technology adoption model when it comes to digital twins in the Dutch energy sector was the Technology-Organization-Environment (TOE) Framework with the following relevant (non-exhaustive) adoption variables: Complexity, Compatibility, Perception, Technological Characteristics, Availability, Organizational culture, Organizational size, Budget size, Incentives, Management support, Ab- sorptive capacity, (decision-maker’s) Demographics, Attitude towards technology, Regulations, Competitive pressure and Network effects. In addition, the overall perception of digital twins was found to be positive across the Dutch Energy sector, however, there was no consistent relationship established between organizational characteristics and the levels of digital twin perception. Similarly, the research suggested that organizational characteristics and absorptive capacity were not correlated. Nonetheless given the limitations of having a low number of study participants and the potential of bias amongst respondents towards their employer, the strength (significance) of these discovered relationships are indicative and should be further investigated in future research prior to making any additional claims that are conclusive.

In the military field, detailed simulation models are often used to get insight in the consequences of choices regarding the composition of a force and the extent to which it can be kept deployable. With detailed models, the main idea is to capture all real-world complexity relevant for decision-making processes. However, detailed models tend to be ‘bad’ in the evaluation of uncertainty in a system. This can be problematic, as the outcomes of detailed models usually depend on numerous dubious assumptions that are too buried to be understood by hand. As simplified models can better cope with uncertainty, this study examined the combined use of a detailed model and a simplified model (this approach is also known as ‘multi-resolution modelling’) to improve the robustness of a naval force support system. In order to work with models with different levels of detail, an existing detailed model that reflects certain elements of the force support system of the Dutch Royal Navy is used to develop a new simpler model.

The key takeaway of this study is that multi-resolution modelling can be considered useful as the detailed model and the simplified model were found to be mutually dependent in their design, validity, and use to improve the robustness of a naval force support system. As the detailed model contains a more accurate depiction of the system of interest, it formed together with a set of aggregation functions and cross-validation tests the basis for the design and validity of the aggregated model. The detailed model also remains important for the evaluation of the real-world effectiveness of promising ‘high-level’ actions discovered by the simplified model. Vice versa, by means of exploratory analysis, the simplified model revealed some potentially problematic assumptions in the detailed model which has implications for its design and validity. Also, the simplified model gave insight into (a) problematic scenarios that lead to a low sailing availability, (b) patterns in the robustness of policies across different usage profiles, and (c) potential leverage points to increase the robustness of the naval force support system. This study concludes with a number of scientific research recommendations involving challenges in strong consistency and the composability of models at different levels of detail.

Urban cities are growing every day due to rising population, vehicular traffic, immigrants looking for opportunities and to accommodate this, cities are expanding and reshaping at an immense rate. People travel within a city for various purposes but the destination locations are the main reason for the movement. It is proved that locations influence a person's travel behavior. This research aims to study the influence of points of interest on human mobility available within a city and estimation of the travel flow between specified origin and destination locations. The city of London is chosen as the case study and the travel network is the London Rail. The research question this study aims to answer is "How to estimate human mobility by using points of interest?". There are sub-questions developed to guide the process of this study and they are used effectively throughout. In literature, there is not much research regarding intracity mobility and not much regarding estimating mobility with amenities/ points of interest (POI). There is research regarding mobility estimations at scales larger than cities and usually, it is travel flows from multiple origins to a singular destination. This research includes travel flows between both origin and destination locations. There are however implications in research that POI data can be used to quantify the influence of attractions on mobility and might prove to be better than the traditional gravity models. This research can help estimate the travel flows and aims to quantify the influence of points of interest in London. A new data preparation framework is designed from various data sources that lay the foundation for the data availability problem. The final dataset obtained contains the following variables: origin population, destination population, origin-destination distance, POI categories such as Commercial, Community, Educational, Entertainment, Financial, Government, Healthcare, Sustenance, and Transportation. For the model techniques, Negative Binomial and Poisson regression multivariate models are chosen. A series of experiments were conducted and the final experiments and the models within them were determined with the help of methods such as Akaike information criterion (AIC) and p-value. The model selection process was executed through a series of experiments to determine the most effective models. The model selection methods include the Akaike information criterion (AIC), p-value, Root-mean-square error (RMSE), and Coefficient of determination (popularly known as R-squared or R2). The RMSE and R2 methods were used to determine the best models among the sets of experiments created. Finally, model validation was conducted using the 'Sorensex Similarity Index' (SSI) method to quantify the similarity between the estimated and empirical data. The models were built separately for each of the days (MTT, FRI, SAT, and SUN) as time could not be included as a variable to the model. First, the process was executed for the day MTT and then it was replicated for the rest of the days. There were not many differences observed between the days as the POIs selected did not contain any difference between the days. If the timings of the POIs were available, then there could be clear differences observed between the days. Limitations and assumptions such as these were elaborated discussed throughout the thesis. The selected models are the first models for both Poisson and Negative binomial regression generalized linear models (GLM) and they perform better than the traditional gravity models. Though the differences observed are minimal, it is important to note that the Negative binomial models performed better on SSI while the Poisson models performed with the model selection methods such as RMSE and R2. The first models performed better using both the model selection and model validation methods. All the first models contain all the variables i.e. all the POI categories including the origin and destination populations along with the distance between them and the Differentiator variable. However, the SSI values are around 0.46 leaving a difference of 0.54 to reach the optimal estimations. This issue is discussed in detail and multiple methodologies including adding additional categorical variables such as economic/demographic indicators are suggested to overcome it. This research achieves the objectives presented in the paper and answers the main research question. The idea that POIs can be used to estimate mobility and they can be quantified within a city is novel and developing a new model satisfies and extends the research for both academic progression and policy implications. Regarding future work, there are limitless possibilities. The research can be extended for other cities, transport modes and also can be extended to other mobility models. The data availability problem is evident in the research and if that issue is not prevalent then the study could have been seamless. The POI categories are created using a subjective ideology and that can be improved using modern classification techniques. For policymakers and urban planners, this can help plan the city effectively and efficiently towards a sustainable future solving many problems such as ineffective traffic models, last-mile hike problem, congestion reduction, inequality minimization, irregular spatial designs, livability index, and much more. This study provides the foundation for a new thought process in Urban Science and it also discusses the possibilities this research could provide if extended further. Urban planning policymakers can understand the importance of amenities on human movement through this research and hopefully, allow them to make new creative decisions regarding urban planning. Keywords: human mobility, urban planning, intracity, amenities/points of interest (POI), gravity model

The global society is facing a number of Grand Challenges, problems that are often ill- defined and for which time is running out. Finding potential solutions requires the use of Modelling and Simulation (M&S) tools that can facilitate large scale collaborative and multi-perspective efforts. Ideally this is done by building across contexts on each other’s insights and contributions as if they were bricks. However, current M&S software does not support context independent bricks due to a variety of rigid bindings; the bricks are essentially glued to a context and cannot easily be reused. In this research we combine the fields of M&S and Computer Science to determine under which circumstances context independent bricks can exist. We took the current concepts about components and applied a low coupling high cohesion attitude to create a new flexible structure, the Brix; we formally defined this structure in the Brix Formalism. Furthermore we built a proof-of-concept system named BrixMS in JavaScript that can simulate models built in context independent bricks. BrixMS offers little in the way of features, yet it has the potential to simulate any computable system with an unmatched flexibility and reusability. While hard limits exist in terms of how we can represent Grand Challenges and how much we can compute, BrixMS proves that context independent bricks are a practical reality that we can use to more effectively find solutions to our Grand Challenges.


Public transport networks (PTNs) have an impact on both travelers’ behavior and system operations. A meaningful approach to investigate PTNs is via their topological structure because it was found to be correlated to the operational performance, the total ridership, consumer experience, passenger flow distribution, and network resilience. A handful number of past studies characterized and compare PTNs structure, but little is known about broader or general classification worldwide. This study examined how PTNs can be clustered into groups when considering multiple features. Centralization, accessibility, robustness, service connectivity and directness are five main considered network features used in previous studies to analyze the network structure. K-means, hierarchical clustering and principal component analysis were performed to identify the cluster of PTNs defined from those five features. To illustrate the method, we conducted a case study of 20 real-life rail-bound networks worldwide generated by the up-to-date general transit feed specification (GTFS) data. As a result, we were able to identify four main meaningful clusters: tram, tram-related, metro and tram and mixed modes. Although modes of transportation and the size of the network were not parts of features, they heavily influence the clusters. The proposed method show automatic and reproducible tools to empirically identify topological patterns of PTNs.

The limitation of natural resources forces humanity to rethink its current habits of material use. Plastic product packaging, fossil fuels for transportation and energy generation are just a few examples of highly resource intensive processes in modern society. Urgency of this matter increases due to the fast-growing world population and big economies as India, China and Brazil. Understanding the current material flows and the system they flow through might generate knew knowledge of material use dynamics. This knowledge can help policy makers to stimulate material use efficiency. To generate the necessary knowledge this research takes an Urban Metabolism (UM) approach. In contrast to established material flow focused and analytical aggregated UM approaches, this research proposes a more comprehensive approach. Defining the UM not only as material flows and processes, but giving more weight to the Social, Economic, and Institutional system aspects as well as the role of actors. On top of the different system perspective the metabolism analysis is conducted on different time and spatial levels. The addition of disaggregated analytical levels enables the use of statistical analysis to discover material use patterns over time and identification of actor group related material-use behaviour. The statistical methods used are Time Series, Correlation and Geospatial Analysis in addition to the common Material Flow Analysis and a Life Cycle Assessment. The proposed UM approach is applied to the case of household waste management in Amsterdam. This research showes the potential of a deeper and holistic metabolism analysis enabled by increasing amount of available data. Opening up and troughing light into the up to this point Urban Black-Box.

As financial markets have evolved and become more digital, the ways of marketing, communication and customer service have also adapted to the times. Customers expect a higher standard from all industries, financial services included. On top of being a nuisance to unsatisfied customers, poor customer service costs industries globally ＄338.5 billion in potential revenue losses per year. The industry with the highest losses is financial services, with about ＄44 billion lost per year (Genesys, 2009). Systems utilizing customer data for the purpose of improving business relationships with customers through better customer communication and service are called Customer Relationship Management (CRM) models (Chen & Popovic, 2003). Part of CRM operations is lead management. Lead management in particular is the set of methodologies, systems and practices with the aim of helping to better service existing clients (retention) or discovering and bringing in potentially new clients (acquisition). This project is performed with the goal of improving the lead management system in the private banking department of ING. More specifically, the goal is to improve the identification of the best leads weekly from the pool of all available leads to be sent out to customer contact teams. The methodology consists of the integration of machine learning algorithms into the lead management infrastructure for the purpose of scoring leads in order to improve the selection process, leading to improved customer communication as well as revenue potential. Additionally, more information was put into the decision making by considering the performance and preference of the customer contact teams. Due to the usage of data modelling, a review of relevant compliance measures with regards to GDPR was performed, with an additional measure of decision explainability being proposed for this project as well as all projects using machine learning algorithms.Three different algorithms were tested, with the best one selected based on performance being a random forest model. The model was tested against the existing lead selection method for 6 weeks, and showed considerable and consistent improvement in performance from the third week onwards (up to 16%). The random forest was more flexible over the weeks and based on analysis of decision interpretability made on particular model decisions, benefitted largely from the inclusion of team performance. In fact, the team which was handling the lead proved to be the most important factor in the decision making of the model. Preference did not seem to have any particular impact on the performance of the leads and thus was omitted from the final model. Overall, based on the results of the testing, the use of machine learning algorithms was shown to significantly improve the performance of the lead management system, based on better lead selection and the consideration of team performance. For future research, it is suggested to implement machine learning techniques in the lead generation step (rather than after), in order to reduce the information restriction the algorithm faced in the case of this project.

Currently a new small and automated dairy factory for the onsite production of cheese at farms is under devel-opment. This new factory aims at the production of cheese without any human interaction, based on the (raw) milk from cows who are milked with a milking robot. At the moment only a small test factory exists, in which human interaction is still present. Of the new factory it was unknown what configuration of processing stations and transportation vehicles would be required to produce cheese. To find the optimal configuration of this new automated dairy factory, first a data analysis was used to determine the characteristics of the milk input of the factory. Next the present test factory was analyzed and a model representing the automated dairy factory was developed. The model was used to identify the opti-mal configuration of the dairy factory. Four different farm groups were researched, per farm group one opti-mal configuration of the dairy factory was identified.