The continuous innovation within the telecommunications industry is compounded by changing risks for the consumer. This is the case within the Netherlands, where the use of the internet has a pervasive influence on society, necessitating robust telecommunication networks and services. The telecommunications industry is regulated by the Radiocommunication Agency Netherlands, which currently use a method of risk-based regulation to monitor relevant organizations. This is characterised by allocating resources based on the estimated level of risk that is posed by a monitored organization. While these risk analyses have developed over the years, there is no standardised system heightening the possibility for cross-departmental inconsistencies. An overview of academic literature reveals a lack of research on the creation and implementation of data-driven model for risk-based regulation. Existing studies lean towards the merits of risk-based regulation, in addition to the risks associated with data-driven risk-based regulations. When this literature gap is compounded with the rising pressure for the regulator to perform quality supervision and ensure the uninterrupted continuity of the telecommunication networks and services in the Netherlands, it provides a suitable opportunity to explore the applicability of a data-driven solution. The use of data-driven risk detection models have the potential to identify companies that pose higher social risks to create a more efficient process for the allocation of resources. It can illuminate the relevant risk criteria and characterise the risks that certain organizations may pose. This information supports the decision-making process of regulation, which could lead to a more cost-effective process of supervision. Furthermore, given the inconsistencies that exist between the various supervisory divisions of the Radiocommunication Agency Netherlands, a data-driven model could increase internal cohesiveness. As a result, there is significant interest in the possibility of developing a risk detection model that can be adapted to the current organizational structure and makes use of the readily available data within the organization. The research objective of this study is to create and implement an exploratory data-driven model for risk-based regulation, specifically on the subject the continuity of the telecommunication networks and services within the Supervisory Field of Information Security of the Radiocommunication Agency Netherlands. Thus, the primary research question that will be explored is: How could a data-driven risk analysis for risk-based regulation support the process of risk-based regulation executed by experts? There are four primary challenges to the creation and implementation of data-driven models: the ability to turn data into useful information, the ability to assess the quality of the data, the availability of the data within the organization and the ability to create a process with a high cost-effectiveness ratio. Additionally, there are specific issues associated with the use of data-driven models in regulatory bodies. Firstly, the use of algorithms and data metrics can be distorted when applied incorrectly. In addition, this who create data-driven models or use the outcomes of the model are prone to interpret the data so that it will fit the pre-existing narratives The second issue is implementing the data-driven model so that it generates acceptance and can be integrated into a well-functioning working culture within the organization. The research utilizes data that is sourced from two different departments of the Radiocommunication Agency Netherlands; survey responses from supervised organization and self-reported data regarding network incidents. The final data source consists of social media mentions naming network failures of monitored organizations. The development of an Exploratory Model based on the available data within the organization highlighted several modelling challenges. The process of developing the Exploratory Model is preceded by an Exploratory Data Analysis which determines the quality and significance of the available data. This allows for a summary of the main characteristics of the data and supports the development of the Exploratory Model. The Exploratory Model involves clustering the data, and developing correlations between the various datasets. The execution of a K-means algorithm segments the organizations into groups that provide similar responses to the surveys. The correlations can be used for facilitating the initiation of risk profiles. The data from network incidents and failures are highly important when creating a model for data-driven risk-based regulation as they are key in defining the different risks posed by supervised organizations. The results are validated through interviews with experts of the relevant divisions in the Radiocommunication Agency Netherlands. Two primary challenges that exist for the implementation of the model are identified; ensuring all necessary data is available for the creation of the model, and defining the owner of the model. The existence of an institutional boundary between the Supervision Division Information Security and the Spectrum Division Continuity, which hinders data sharing regarding the reports of failures that caused discontinuities of the telecommunication services and networks. This poses challenges for the implementation of a data-driven model, as it is crucial for the relevant teams to have access to all necessary data within the organization. There are two possible solutions to overcome this challenge; either dismantling the institutional boundary, or relocating the data management of reports of failures that caused discontinuities of the telecommunication services and networks from the Spectrum Division Continuity to the Supervision Division on Information Security. The second challenge is defining the most suitable owner of the model. As the model is intended to support regulation for the Supervision Division on Information Security, it is necessary for the owner to have expertise in this area. Moreover, as this will be a data-based model, knowledge on quantitative analyses is preferable. Currently, the Monitoring & Analysis Centre of Radiocommunication Agency is most suitable, due to their expertise in analysing data and working on data-driven models. Moreover, they already have access to all necessary data, which means no organizational restructuring is needed. However, there will be challenges stemming from their lack of knowledge regarding the process of supervision on the continuity of the telecommunication networks and services. Two directions for future research are identified based on the outcomes of this research. The first step would be to further develop the Exploratory Model, which requires a higher volume of information about failures that caused discontinuities. In addition, the data currently within the databases must be frequently updated. Whilst this is certainly viable, it would be a time intensive and a bureaucratic process. In the meantime, it is possible to execute the clustering algorithm on the whole dataset of both of the surveys with the responses from around 1000 organizations and to compound this with qualitative data from experts and inspectors within the Supervision Division Information Security, to aid in the determination of the risk profiles.

More than 200 million Indians are without electricity, and the majority lies in the rural areas, where the central grid fails to reach due to unreliable distribution and inconsistent terrain. The solutions that reach include majorly, decentralized energy resources via solar micro-grids, or solar home systems. New projects of energy sharing are coming up which allows a household to become a consumer, as well as a producer of energy in case of excess and sell it to make profit. But these projects fail within few years due to number of social, technical, cultural, political, and environmental dynamic factors playing role in the community. Thus, the research question is raised that “Given the dynamics of rural communities of India, how can socio-technical systems of solar electrification be sustained?" This project develops an institutional innovation framework that helps answer this question. The framework is conceptualized and operationalized to understand that how the institutions around these factors can be changed by the community and individual actions in such socio-technical innovations and infrastructure systems. The study follows empirical analysis and develops simulation model (agent based model) for the case study of rural solar electrification in India, which helps in developing the institutions. The case study and the framework together emphasizes the importance of the institutional innovation approach, where institutions need to be adapted and diffused within the community to make the sustenance of the project possible in various domains. The final results show that new type of projects, labelled as hybrid projects, would be most sustained. These would be sharing projects and would only use the existing micro grids, when there is higher demand. Also, it emphasizes on the need to look at institutions development, not just as a collective perspective which happens with interactions of actions, but also a perspective of majority or collection of actions. The policies generated at two levels of usage (prioritization of resource usage, constraints on resource usage, etc.), and for acceptance of innovations (co-operative shops, in-house manufacturing, etc.) prove to help the sustenance of these electrification systems further.

Increasingly companies are facing tough challenges due to fast paced technological developments, shortening product life cycles and new forms of business models. Therefore innovating is crucial for companies to stay competitive and enter new markets. Large corporations have difficulty in breaking silos and developing breakthrough innovations due to pressures in the form of financial performance and resistance to change. It seems that due to volatility of markets and technological developments, companies need to “join the race” in order to survive. For the purpose of benefitting the innovation space network companies can interact with external parties. Research shows that collaboration with external actors lead to sources of higher innovation performance. Nevertheless, the innovation process is very intricate as it involves many complex interactions and challenges. Innovation depends on people´s expertise, capabilities and creative capacities. Companies need to recognize this and provide an environment that can foster innovation. Infineon Technologies AG Corporate Supply Chain Innovation department recognizes the need to innovate in a peripheries of its core technological developments. The department is involved in several co-funded European projects which aim towards digitalization improvements. The main company´s objective is to explore ways to better coordinate relationships between partners, to find ways to innovate and to foster knowledge management practices. Since the firm wants to improve its innovation performance, certain processes need to be develop to stimulate innovation. Infineon Technologies AG does not have processes for managing disruptive innovations therefore is in need to design a model that would enable the company to manage them, the main objective of this master thesis is to design: A model which prescribes processes for management of disruptive innovations between firms, on a case study of Infineon Technologies AG Corporate Supply Chain Innovation department. In order to reach objective of this master thesis, a following main research question is formulated: How to facilitate inter-firm innovation management processes of disruptive innovations in the context of Infineon Technologies AG Corporate Supply Chain Innovation Department? The thesis project takes a Design Science Project approach and is divided into five phases: 1) research motivation 2) requirements definition through exploration of theoretical foundation 3) design and development of the artefact 4) demonstration of the artefact through validation and 5) evaluation and conclusion. The overall conclusion for this thesis is: in order to facilitate inter-firm innovation management processes of disruptive innovations Infineon Technologies Corporate Supply Chain Innovation department should implement co-innovation management practices. The co – innovation model has a network structure composed of co-creation, collaboration, innovation platform and convergence affordances. By expanding these elements to its practical implications, the company is able to implement a comprehensive and implementable solution. Scenario with actionable steps was developed as a recommendation for the company.

Development Finance Institutions have been established to provide finance for long-term economic development in low and middle-income countries. However, these investments are exposed to climate-related risks. This means that Development Finance Institutions need to explore climate adaptation interventions, particularly in view of the fact that developing countries themselves have few resources for climate adaptation. This thesis aims to establish how, using an agent-based model, Development Finance Institutions can achieve long term investments by financing climate adaptation, while creating societal benefits and financial returns. This implies positively contributing to society, for example by creating jobs, generating a stable income, and supporting the resilience of households, while securing profitability for development banks. An investment by the Dutch Development Bank FMO in the cocoa sector in Ghana serves as a case study. Three main steps are followed in the study: (1) translating global climate scenarios at the local level; (2) capturing local behavior in a bottom-up model because understanding the specificity of the local context and individuals’ response is key to really make an impact; and (3) testing climate adaptation interventions. Downscaled climate scenarios are used as input in an agent-based model capturing the behavior of Ghanaian cocoa smallholders. Experimenting with climate adaptation shows that Development Finance Institutions have to make trade-offs in terms of priority, timing, and activity. This step-by-step approach could encourage DFIs to capture the impact of climate change and the dynamics at the local level, and to design tailored interventions. It is also a way to take a holistic perspective and look at the broader picture of economic and societal impacts of climate change, and the response across the whole value chain.

Agent-based modelling (ABM) is a widespread and commonly used technique for the understanding of social system behaviour. Despite ABM being a commonly used method for the analysis of complex social systems, simulated agents still can best be described as autistic before a specific set of social rules is ascribed to them. This discrepancy leads to model outcomes that can differ from reality. For improving agent-based models on this aspect, a social practice theory-based approach can be used as a theoretical framework. The main question to be researched is: How does enabling agents to reason about others' beliefs on social practices affect dynamics of belief and behaviour formation as opposed to action adaption models, with respect to transport mode choices? Two agent-based models have been created within this study to provide an answer to this research question. Through including a case study within the transport domain, model results can be hold against macro-behaviour within the state of the art of models on transport mode choices. The case study depicts a group of students making a decision for a transport mode to move from their university faculty to a compulsory seminar. The students influence each other, leading to belief adaption. Within the transport domain, usually this social influence is translated to compared and adapted actions instead. This study differs itself by letting beliefs be the reference point for social comparison. Therefore, belief dynamics and agent behaviour for the models with belief adaption are compared to the state of the art action adaption models. Lastly, the effect of limitations towards having insight to other agents' personal characteristics is analysed. The analysis provides the following conclusions. Within the field of transport mode choices, enabling agents to reason about others' beliefs on social practices results in the same opinion dynamics and behaviour as is presented by action adaption models. However, an agent-based model that allows agents to reason about others' beliefs on social practices with limited insight in others' private characteristics through a theory of mind capacity, does provide a different reaction to an increase in the social capital of agents. More specifically, enhancing social network connectivity through increasing the group size or similarity threshold does not lead to more consensus within the theory of mind model, which is the case in the other models presented within this study. This stresses the importance of considering the extent to which are capable of forming a just conjection on others' private characteristics when designing an agent-based model with social interaction.

Modelling and simulation is at the heart of Digital Twin technology, which is revolutionising many industries. Organisations have access to legacy models that are too complex to maintain, preventing them from operationalising complex systems like Digital Twin. By creating an automated tool for model decomposition, it is possible to breathe life into complex models by extracting their embedded functionality as managable components. This thesis presents NewMODE as a network theory approach to model decomposition; a novel methodology that aims to automate the tedious task of model decomposition. Important contributions include the network theory metamodel specification, the decomposition criteria and the adaption of the Girvan Newman algorithm to identify components of the model. NewMODE has been implemented for models in developed in LSAT (Logistics Specification and Analysis Tool). In partnership with TNO and their Embedded Systems Innovation (ESI) group, NewMODE has been evaluated quantitatively and qualitatively with promising results to aid model developers in model decomposition.

In the future, the energy supply will primarily consist of energy from renewables, which makes energy producers and grid operators fear that grid security and reliability could be threatened. On top of a more fluctuating supply, energy demand is also expected to increase in the coming years, especially if heating will be electrical when gas is phased out. Demand-side management, and more specifically demand response, could be a way to make demand match supply better and limit the foreseen energy shortages. A smart grid and smart metering technology can be implemented to overcome the hurdles of grid integration of renewables. Consequently, the implementation of a smart grid could potentially steer consumer electricity usage and allow for automated demand shifting. This thesis analyzes consumer participation in demand response in an electricity grid with a high share of renewables. The effect of multiple price- and incentive-based interventions are tested for different consumer populations and supply scenarios. The aim is to improve the understanding of the effect of consumer heterogeneity on the participation in demand response. An agent-based model was constructed that represents the energy system as a copper plate model and incorporates the rule-based behavior of different consumer types. This model was used to answer the main research question: How can a simulation model be used to analyze the influence of consumer heterogeneity on the success of demand response enhancing policies in future electricity networks? The consumer types that were included are the green, the cost-conscious, the convenience-oriented and the indifferent energy consumer. Subsequently, five personal values were identified as important moderators for consumer participation, namely the values of price, environment, comfort, safety and social norm. Different interventions and combinations of interventions were compared based on the three model KPIs energy shortage, total electricity cost and required storage. The comparison of the interventions and intervention combinations showed that the differences in energy shortage between the policies were limited and were mainly determined by supply. For a constant supply, the results revealed that the simultaneous implementation of all policies was most effective to reduce shortages, followed by the policy combinations that included the cost comparison with a reduction. Naturally, these combinations also resulted in lower electricity costs for the consumers. The cost comparison and reduction policy and default shifting could increase consumer participation most when used as a separate intervention. Some main conclusions can be drawn from the analysis of the interventions. First of all, supply was the most important influence on energy shortages. Depending on the shortage period characteristics, shortages could not always be solved completely by demand response, but a higher amount of demand shifters can improve the situation significantly. However, not all consumer types could be motivated to switch to a shifting contract, which means that the consumer population is an important influence on the effectiveness of policies. From this thesis it can be concluded that a simulation study is a successful way to create an inclusive model of the technical, economic and behavioral system components. The model was able to provide a system-level overview of the system dynamics and improved the understanding of interactions between interventions and consumers. Therefore, this model can be used in future case studies to analyze the potential consumer participation and recommend suitable interventions to increase this.

Novelty search is a state-of-the-art approach focusing on behavioural novelty, rewarding diverging, as opposed to pursuing static objectives. This is relevant for exploratory modelling and analysis, which focuses on exploration of model through open exploration or directed search. Novelty search as an open exploration strategy is being tested against proven methods such as latin hypercube sampling. Using existing evolutionary algorithms and a developed novelty function, the experiments focus on comparisson, impact of the number of functional evaluations and the impact of the goals of the evolutionary algorithm. Finally it can be concluded that novelty search finds novelties in the lake problem, which makes it a relevant search strategy, but not suited for indiviual exploration. That means that it would still be advised to use latin hypercube sampling for earlier exploration.

Ransomware has evolved over the years, shifting from widespread attacks targeting individuals to focused attacks on businesses and agencies. These attacks are performed by ransomware gangs while establishing interaction within the ransomware ecosystem. In this thesis, the ransomware ecosystem is posited as being constructed of three separate sub-ecosystems: the attacker sub-system, the defender sub-system, and the governance sub-system. Since ransomware gangs put in an effort to hide their internal communication and operation from the outer world, difficulties arise in correctly understanding the ransomware ecosystem and a ransomware gang’s establishment of interactions within this ecosystem. As a result, current interventions are ineffective. While earlier research has been conducted on ransomware, we observe two knowledge gaps: 1) there is a lack of understanding of how ransomware gangs establish interactions with actors in the ransomware ecosystem, and 2) There has been a lack of research that uses ground truth data due to ransomware gangs keeping their internal communication and operations hidden. This thesis uses the leaked internal communication data of the Conti ransomware gang to fill these knowledge gaps and answer the research question: To which extent can the ransomware ecosystem be reconstructed using ground truth communication data of the Conti ransomware gang?”. To answer this question, a novel methodology is proposed that uses Latent Dirichlet Allocation (LDA) topic modeling to empirically determine overarching topics in Conti's internal communication. It is then researched how these overarching topics map to Conti’s tactics, techniques, and procedures (TTP) which is a commonly used methodology to better understand how ransomware gangs operate. Subsequently, these TTP are leveraged to reconstruct the ransomware ecosystem while taking the perspective of how the Conti ransomware gang establishes interactions within the ransomware ecosystem. The findings of this thesis indicate that Conti is a large and professional organization that incorporates and adjusts services of service-providing cybercriminals in the attacker ecosystem rather than developing their ransomware themselves using scarce IT talent. In addition, reconnaissance is one of the most critical activities that ransomware gangs perform to get to a successful ransomware attack. While researching Conti's TTP, this thesis identifies novel TTP of ransomware gangs, such as Conti's attack chain, reconnaissance procedure, and money laundering procedure. We conclude that the ransomware ecosystem can be reconstructed from the attacker ecosystem, the defender ecosystem, and the governance ecosystem, in which ransomware gangs establish interactions within each sub-ecosystem while operating from the attacker ecosystem. In the attacker ecosystem, ransomware gangs establish interactions with service-providing cybercriminals to outsource sub-commodities of their ransomware value chain. This allows them to strengthen their attack vectors by relying on the expertise of others and have a more varied set of attacks. The defender ecosystem is comprised of defenders that defend themselves against ransomware. Ransomware gangs establish interactions by performing extensive reconnaissance on defender territories and valuable information and open-source tools that strengthen their attack vectors. The governance ecosystem comprises governance actors that create and maintain the governance framework that influences the attacker ecosystem and defender ecosystem. Ransomware gangs establish interactions with actors in the governance ecosystem to observe the regulatory frameworks in place and adjust their TTP based on the involved risks of getting caught.

Energy storage systems (ESS) are expected to be one of the main pillars for a future renewable based power system. Since all sectors are electrifying and variable renewable energy (VRE) is being deployed more quickly than transmission and distribution operators are able to connect them to the electricity grid. Flexible capacity on multiple positions along the electricity value chain is needed to maintain stability of the network. However, the original design of the grid was not based on decentralized and variable generation. Together with uncertain regulations that are not up-to-date with innovations in the grid. Results in multiple barriers that the ESS needs to overcome and therefore have not been widely integrated yet. Since investment decisions by decision-makers are not only based on technical and economic features but also on social and environmental features better insight in the impact of different power system design is needed. Several studies have been performed on the tech-economic modelling of singular ESS services, often using perfect foresight for prices and market behavior for different parts of the electricity value chain. This work presents foundation for valuation of ESS for industrial sites in the Netherlands in a future scenario with large increase of VRE in the energy mix. Since energy systems are highly complex systems. Modelling and simulation is performed to get a better understanding of the system by simplifying interactions and functional relations within the industrial power system. Hence, the model provides insights that help to reevaluate the power operation of an industrial cluster. As a result it helps to reshape the power system investment strategy for the future. Considering that ESS are multi-applications this study analyses how different operations strategies including multiple services influence the performance of the industrial power system. The operation strategies of enhancing self-consumption (greedy) and peak shaving are compared on the following performance indicators: self-consumption, self-sufficiency, utilized and unutilized VRE, size of network connection, capacity for flexibility and influence of transit power generation. The greedy and peak shaving operations are simulated in combination with the services of providing transit power for back-up generation and ability to provide flexibility service for the imbalance market. The model is run for three different industrial loads (scenarios) for five different configurations of either a Photo-Voltaic (PV) system or a PV system in combination with an ESS. In all scenarios, the power systems performance is better when ESS is integrated in comparison to the PV-system. In the scenarios the scoring varies considerably between the configurations, primarily due to the heterogeneity of the used load profiles. Hence, allowing to identify the characteristics of industrial load profiles more suitable for a combined PV and ESS system. The results of this study are relevant for decision-makers considering to adapt their power system design strategy by providing a better understanding of their system. It shows how different services run in parallel allow the ESS to be operated less idle. Nevertheless, should decision-makers establish operational priorities since some services can be competitive and result in a sub-additive value. Furthermore, can the findings help policy makers and utilities to see what the influence is of increased VRE generation at the industrial load side of the network on the larger system.

During the battle against COVID-19, sudden-onset disasters keep happening. These two consecutive events are different in nature and sometimes ask for counter-productive policy interventions: where the epidemic can be contained by ensuring social distancing practices, reducing the number of contacts, and offering hygienic conditions, the response to a natural hazard requires evacuation, which often leads to overcrowded shelters with a questionable hygiene. An exploratory agent-based model is constructed where the dynamics of these socio-technical systems are integrated to find high-level emergent behaviour over time during the response phase of a sudden-onset disaster. The study describes a stylistic agent-based model that combines three socio-technical systems, their interactions and system behaviour to find general trends and interdependencies. The three sub systems comprise of a COVID-19 component, based on the SEIR approach, a livelihood component, which mimics a microeconomic marketing mechanism and accounts for among others difference in occupation, and a hazard component, which is responsible for the evacuation process. The following research question is answered: What robust policy interventions can be identified that balance livelihood of rural communities and their exposure to COVID-19 during the response to a sudden-onset disaster in developing countries? The goal is to find robust policy interventions that can be executed by either local governments or humanitarian organizations and are suitable for poor rural communities in developing countries. Four policy interventions were compared: cash transfers, an awareness campaign, increasing the number of shelters, and varying the moment of imposing a lockdown. With the use of Exploratory Modelling and Analysis, an experimental design was constructed to identify uncertainties and model behaviour. Results show that using direct and unconditional cash transfers in combination with increased awareness have most beneficial effects for the average livelihood and also positively influence the COVID-19 trajectory. Increasing awareness shows best results for containing COVID-19, but is dependent on context factors such as regular testing and implementation before the the virus spirals out of control. Awareness also negatively influences the average livelihood. This research is a first step in modelling compound risk of COVID-19 and sudden-onset disasters. The modular way of building makes this research useful not only for searching trade-offs in developing countries, but can be used in other contexts as well.

Research background Plastic debris in the environment consists primarily of plastic consumer waste. One of its leading causes is the lack of waste collection systems in developing countries. Actions need to be taken to overcome this issue, and Extended Producer Responsibility (EPR) seems to be an effective solution for encountering plastic waste. Indonesia is the second-largest plastic polluter and is, therefore, a location of interest for implementing EPR systems. EPR is a policy approach in which producers become responsible for the whole life cycle of their product. In Europe, this is a standard tool that makes producers (mostly financially) accountable for the end-of-life stage of their product. EPR can also stimulate design-for-environment: optimizing products to become more reusable or recyclable. EPR consists of many instruments that can be implemented in different ways. Still, little research has been done on which instruments can be effectively applied in developing countries like Indonesia. There are no EPR instruments currently used, little waste management infrastructure exists, and most recycling activities are realized through informal waste collectors. These types of local factors are of great importance when implementing waste management policies like EPR. Two gaps were found in previous literature. First, the effects of individual and combinations of EPR instruments on developing waste management systems have not been researched before. Secondly, simulation studies have not analyzed EPR instruments in combination with contextual factors of the country of interest. Therefore, this thesis tries to answer the following research question. “What are the effects of 3 selected Extended Producer Responsibility instruments on the key actors in the plastic waste system of average cities in Indonesia?” Research approach A design-science research approach is combined with an agent-based model to answer this question, consisting of three parts. First, the Knowledge base was established by desk research. Literature was consulted to find the state of the art of the most critical aspects of the study: plastic waste in Indonesia and EPR. Secondly, the Environment is created by integrating semi-structured interviews with literature to find important factors that make the implementation of EPR interventions in the context of Indonesia more realistic. The third part of the design science research is to design the artifact. A conceptual model was made of the plastic waste system in Indonesia. This model was put into the programming language NetLogo, which can capture agent behavior in complex adaptive systems...

Call Detail Record (CDR) data enables the analysis of human behaviour on a large scale and the information that it contains can be promising. Not only does it allow us to track the movements of many individuals throughout time, it uncovers patterns in a persons decision making process that potentially tell us a lot about the effects of different interventions. The opportunity of finding new information on human behaviour has been noticed in several research fields, but every researcher eventually finds the same blockade: privacy. The data represents a detailed track of individuals and therefore these individuals must give approval (almost certainly lowering the amount of data that can be collected), or the data must be aggregated to the point that user anonymity is guaranteed. As a consequence of aggregated data, potentially important information could be lost. Especially in the case that both the dimension of location and time are aggregated, as these two could be considered as the essence of the CDR data. There are however techniques that increase the aggregation level, by de-aggregating the data. Naive Bayes classification has shown to be a functioning method within Machine Learning to de-aggregate a dataset that has incorporated information on at least one of the two essential dimensions; location in this case. By using the same variables to describe the administrative areas within the country that were used to describe the rows within the data, Naive Bayes classification can find the area that is most likely to fit the row. Matching the variables of the areas to the variables within the displacement dataset represents the backbone of the process, as the de-aggregation is driven by the closeness of datapoints between the two datasets.

It is expected that the express delivery market will grow strongly in the coming years. This growth, however, is restricted by the shortage of express transportation capacity and is increasingly causing congestion. The transport sector is not only subject to this growing demand from a business perspective but also faces environmental challenges. The unsustainable nature of current transport combined with the limited capacity, have led to an interest for a new form of transport: the Cargoloop, a cargo application of the hyperloop. The fundamental concept of this idea is that cargo will be transported through an autonomous ground-based system that consists of vehicles within a low-pressure tube, in which high speeds of around 1000 kilometres per hour can be achieved. By reducing the air resistance in the tube, little energy is necessary to put the pressurized vehicles in motion which in turn, results in an energy efficient form of transport. Moreover, it is hypothesized that the Cargoloop could act as a stepping stone towards transporting passengers via the hyperloop. However, so far, little research has been done into this topic. This study therefore investigates the economic feasibility of the Cargoloop. In order to be able to do this, the Cargoloop must first be designed. This study therefore consists of a two-step approach: in the first step a potential design of the Cargoloop is established and an initial route is proposed, followed by the second step in which the economic feasibility of the Cargoloop is investigated by means of a cost-benefit analysis (CBA). An overview of the costs and the benefits was investigated for three different CBA scenarios, in which the project was financed in different ways. The results showed that there was no scenario in which the Cargoloop is economically feasible on the trajectory between the airports of Cologne-Bonn and Paris-CDG. A fundamental factor for explaining this, is that when looking at the number of vehicles that would be annual operational on the route between the airports of Cologne-Bonn and Paris-CDG compared to the annual capacity of the Cargoloop, it could be found that only a small percentage of the capacity of the Cargoloop is utilized on this route. Substantial more cargo would need to be transported to make the Cargoloop economically feasible on this trajectory. This could be realized by obtaining a larger modal share or by expanding the network, so the overall cargo flow will become larger.

Enhancing supply chain resilience is of vital importance in today’s business to manage and mitigate the risks, especially in the semiconductor industry challenged with long cycle times and short product life-cycles. Transferring production from a primary site to an alternative site after a disaster is one of the strategies to ensure resilience of the supply network. In this study, different types of alternative sites with various levels of preparedness are investigated. A discrete-event simulation is used to evaluate their operational and financial impacts under four different disruption scenarios. The simulation outcomes demonstrate unexpected positive benefits of various alternative sites in terms of fast recovery and resilience building.

Worldwide, machine learning is increasingly used to achieve optimal analytics and outcomes in business and governmental organisations. However, it has become evident that structural guidance for creating machine learning projects is needed. Machine learning projects are dependent on multiple factors. This thesis focuses on three factors influencing machine learning projects: the technical aspects of machine learning, the organisational aspects of successful implementation of machine learning projects and last the ethical aspects, since difficulties coming with meeting ethical standards contribute to the suboptimal use of machine learning projects. The research was done in accordance with Design Science Research Methodology (DSRM). Ten steps to setup machine learning projects were identified and a method has been designed, demonstrated and evaluated. The research included an extensive literature search which provided the main contributing theories of Knowledge Discovery in Databases and Ethical Impact Assessment. Furthermore, multiple experiments in Standard Business Reporting (SBR) context were done. Expert interviews provided input for optimisation and evaluation. In this thesis a method for setting up creating machine learning projects, including ten steps with accompanying sub steps, was created. A single method combining and integrating technical, ethical and organisational aspects was not yet available. At this moment, it is unique in comparison to other methods, as it combines interdisciplinary aspects into one method and provides guidance for management and engineers. Apart from the developed method, this thesis also provides structured insights in the use of machine learning in SBR context.

The frequency of occurrence and the size of natural disaster is increasing, with more people to beaffected in the coming years. The impact is huge, with major infrastructures being damaged andaccess to basic needs cut off for the disaster struck regions. The affected regions and communitiesare dependent on the delivery of disaster relief aid, by governments and non-governmental orga-nizations. The delivery can be difficult, while airports, harbours and other infrastructure is oftendamaged from the disaster.The timely delivery of relief aid to the affected communities is a major challenge of today. Theairports used for the distribution of relief items are often unable to handle the shear size of theinflux of these items. The resources are damaged and personnel might not be able to show up forwork. In order to reduce the overexertion of the local airport, an airbridge can be made with aregional airport. The regional airport can then be used as a buffer, for bundling and storing reliefaid, until it can be delivered to the local airport. The system in which the regional airport receivesthe international contribution of relief items and sends them in a moderated flow to the local airportis called the airport relief chain.The relief chain is subject to several bottlenecks, but due to scarcity in research, the influenceon airport relief chains had not been identified previously. Reducing these bottlenecks and im-proving the relief chain can be achieved by improving the processes on individual airports, as wellas by altering small features in the structure of the relief chain itself. The research question isconsequently:How can the impact of bottlenecks on an airport relief chain system be decreased,considering the airport operations in a fixed network design?To be able to confirm the bottlenecks and investigate possible solutions, a simulation model wasbuilt for the airport relief chain system. To obtain relevant outcomes, data of a real world casestudy was applied to the simulation model. The case study of choice is the earthquake followed bya tsunami in September 2018, that struck the island of Palu, Indonesia. In this disaster, an airportrelief chain system was created, in which the delivery to Mutiara on Palu, serving as the local air-port, was provided through Sepinggan airport on Kalimantan, serving as the regional buffer airport.To this end three bottlenecks were identified for the airport relief chain. namely: (1) The air-craft arrival process at the regional airport, (2) The cargo handling after unloading of the aircraftat the regional airport, and (3) the airbridge connection between the local and regional airport. Thebottleneck at the airbridge is the most stressing, absorbing most of the cargo. Even when the otherbottlenecks are resolved, without improvement in the airbridge the performance will eventually belimited.With the airbridge as a major constraint in the airport relief chain system, the delivery of re-lief items can not be larger than the throughput of the airbridge. The bottlenecks identified at theaircraft arrival and the regional airport processes reduce the flow of relief items even further. Thepolicies investigated are meant to maximize the performance up to the airbridge, creating maximalperformance within the constraints of the system.The performance was measured through the performance indicator, namely the timely delivery.ii The timely delivery consists oftotal deliveyand theshare of priority demand delivered. The perfor-mance of the system as a whole can as such be evaluated, yet it lacks detail for the implementation ofpolicies. In order to improve the system, the bottlenecks had to be identified, for which the bottle-neck identification measures were introduced. These consist ofidle cargoandthroughput time. Theidle cargo is generally a notification for the occurrence of a bottleneck, with high average idle cargomeaning a bottleneck occurs. The throughput time here indicates whether it was very impacting,as high numbers for throughput time generally mean less impact on the overall component over time.The individual policies were ineffective to truly improve the airport relief chain as a whole. Severalpolicies proved to be quite effective at reducing bottlenecks, thus improving the timely delivery.However, only small improvements were seen in the total delivery, due to the downstream occur-rence of other bottlenecks. Theshare of priority demandalone could be raised effectively throughprioritization policy. Those policies would not provide further system improvements, making theiruse even more case specific.In order to improve on the timely delivery indicators, which includes the total delivery, combina-tions of inventory management policies and airport policies were examined. Based on the individualperformancesdestination prioritizationthrough use of off-airport storage anditem prioritizationon the most stressing relief type were chosen as the inventory management policies. These, if pairedwithadditional workerspolicies, were able to absolve the bottlenecks in the system up to the air-bridge. The combinations as such performed at the optimal range, considering the final constrainton the system and are preferable for implementation.The prioritization policies were the most useful, but require staff and their equipment to fullyperform. As such the processes in an airbridge system for the provision of relief items should con-sider the capacity at the regional airport and optimize the processes accordingly up to the airbridgeitself.

This master thesis explores to what degree information about a company's commitment to privacy can be extracted from a privacy statement. To do this, firstly a new database of more than 1500 privacy statements is created using Amazon Mechanical Turk. Next, 72 different aspects related to privacy of the statements are enumerated by using natural language processing. Multiple analyses are run on this data to look for signals which can indicate commitment to privacy. Ultimately, the analyses result in a number of ways which the quality and completeness can be assessed, and a method to find out the degree to which a privacy statement has likely copied other statements. This can be useful for privacy regulators in identifying companies which are less likely to comply with existing privacy regulations.

Organizations benefit from improved cybersecurity threat detection capabilities if they share information in a community of their peers. However, organizations are unlikely to share the sensitive information that is most valuable as this poses individual risks. Information sharing in cybersecurity communities therefore forms a collective action problem. Currently, cybersecurity information sharing is being studied primarily as a technological challenge. Drawing on theory from economics and the social sciences, this study proposes governance requirements to overcome individual interests and improve information sharing. These are used to design a governance structure for the case of the National Detection Network, a cybersecurity community initiated by the government of the Netherlands. The proposed governance meets interests of parties through a process of interactive decision-making in four phases, while incentivizing sharing of cybersecurity information. Lessons are drawn from the case for cybersecurity communities in general.

Situation: Information management enables humanitarian organizations to make adequate interventions based on timely, appropriate and trustworthy information. A crucial type of information are identities, because they can be used to assess vulnerability and efficiently manage aid distribution. Vulnerability determines who receives aid first because resources are always limited. This information is increasingly being stored and processed in identity systems. Complication: Most identity systems are centralized and produce analogue proofs of identity such as passports or ID cards. These systems are susceptible to privacy and data breaches. Centralization leads to single-points-of-failure and could lead to fraudulent behavior resulting in people lacking formal proofs of identity. In general there is limited interoperability between identity systems and limited collaboration between the owners of these systems. Approach: To create an interoperable and shared digital identity system using a Design Science Research strategy and systems engineering approach. This system must be distributed, protect privacy and put the identity owner in control of his or her data. The foundation of the system consists of Humanitarian Information Management principles, Privacy-by-Design principles and Self-Sovereign Identity principles. This research creates a functional blockchain based system, that enables identities for the use-case of Cash Transfer Programs. Results: We present a validated set of ten design decisions that represent the trade-offs that have been made and prescribe a blueprint for a technical design. Next steps:Future research should be done on how such a system could be implemented and used. This would require a process design approach that has to be developed, Also, elaborate research into user experience and user interfaces should be conducted.