The homicide rate dropped globally in the last decades, while in the same period the percentage of unsolved homicide cases increased. At the same time technological developments have drastically changed our lives and little is known about how this influenced the process of decision-making within homicide investigations. Therefore, the aim of this research is to develop a methodology that supports homicide investigators in the collection, prioritizing and elimination of persons of interest using pieces of evidence, in the new digital era, in such a way that the methodology is effective, reduces tunnel vision and obeys the laws for privacy.Currently used methodologies that use pieces of evidence to manage persons of interest were applied to three recent real-world homicide cases to evaluate whether these methodologies effectively collect, prioritize and eliminate persons of interest. The potential of a general approach, relying on big data and data science, to limit the number of persons of interest to be incorporated in homicide investigation, was explored. Subsequently, a methodology to incorporate and prioritize persons of interest was developed based on literature and the knowledge and expertise of experts in criminal investigation. This case specific elements library (C-SEL) consists of twenty-four elements and twelve underlying factors that can be used for the incorporation and prioritization of persons of interest. This new developed methodology was evaluated and compared to the currently used methodologies, not only on the performance measures of effectiveness, but also on tunnel vision reducing properties and the obedience to the laws of privacy.C-SEL effectively collects and prioritizes the perpetrator in all three real-world homicide cases and showed better results compared to the currently used methodologies on the tunnel vision reducing character and on the obedience to the laws of privacy. These results provide sufficient leads to further validate the new developed methodology.@en

With the advent of a smart society and an era of connectivity, there remain a numerous amount of challenges yet to be solved. One of the key IoT innovations analysed in this research is known as Unmanned Aerial Systems (UAS). UAS, or drone technologies, allow carrying out repetitive and dangerous tasks with almost no human intervention or supervision (Fernández-Caramés, Blanco-Novoa, Froiz-Míguez & Fraga-Lamas, 2019). The advent of drone utilisation in both public and civil domains has led to application areas such as realtimemonitoring and surveillance, parcel delivery, search and rescue missions (e.g. ER), remote sensing in agriculture and multiple other application domains (Valavanis & Vachtsevanos, 2015). However, the fast pacing development of interconnected devices and systems has outrun the human understanding and experience of usage, imposing challenges with regards to technical security, trust and of course privacy (Coetzee & Eksteen, 2011). Subsequently, most drones are limited to computing, storing and sharing data, making them vulnerable to adversarialattacks. A need for a more responsible adoption of drone-use exists and must be fulfilled with care. To this, a blockchain-based solution is proposed.The focus of this research is on a Dutch independent agency part of the Ministry of Agriculture, Nature and Food Quality, called the Netherlands Food and Consumer Product Safety Authority (NVWA). They have been actively using drones since 2017 and their main tasks consist of supervision, risk assessment and risk communication with the goal to protect the human and animal welfare (NVWA,n.d.). An explorative research approach was taken whereby the current bottlenecks experienced by the NVWA were identified and visualised, by means of desk research and semi-structured interviews. Subsequently, the sociotechnical effects of the use of drone detecting services by the NVWA were identified to obtain the key values for a responsible redesign. In this way, the role of the values, an understanding of the roles of institutions realizing these values, and the stakeholder engagement is understood. After identifying the open issues and conflicting values, a redesign phase began with the trade off between three mitigating security techniques.Blockchain technology is found to be the most suitable security technique in this research and fills in the ’lack of transparency’ gap which is crucial for a responsible redesign. Although it hasn’t reached full maturity and could induce undesired delays, it increases the security level of the organisation significantlyand takes into account the key values security and privacy. Moreover, the data managing method allows users to validate, maintain and synchronize the content of a transaction ledger which is replicated across the other users in the network (Tapscott & Tapscott, 2017).The solution has proven to work in existing literature and automotive contexts and is therefore generalisable in multiple on-going projects of the NVWA. Subsequently, the novel data managing method could be used as for revenue models such as digitised inspections. This indicates future growth options andpotential competitive advantages. All in all, the benefits outweigh the uncertainties of this technologyand it is believed that a blockchain based solution will be the next big step to achieve more responsible implementation of drones.

The livelihood of the Maasai pastoral communities in Longido District of Northern Tanzania are impacted by droughts regularly, with expectations of increasing variability in rainfall patterns the coming years due to climate change. The goal of this research is to explore if weather forecast and remote sensing data can be tailored to existing coping strategies and decision-making. Furthermore, it is assessed if this tailored information provides enough skill to effectively complement local knowledge and drought management strategies. The study generated important methodological and theoretical findings, both of which have practical implications for policy and technological development. An ethnographic and participatory approach, including four months of immersion with local families, was used to document local knowledge and strategies, and understand what specific, weather information may benefit pastoralists. The study focused on alamei periods, which refers to times of drought and scarcity in the Maasai language. It revealed that weather information around particular important ‘decision junctures’ is most relevant. On the one hand, decisions to move livestock during vulnerable times are based on current water and grass availability; on the other hand, families also consider expectations of rainfall in their decisions. The research determined that at very specific junctures throughout respective seasons, key, timely decisions must be made to maintain household resiliency. It is at these junctures that rainfall predictions become crucial. Using NDVI data and the ECMWF weather model, it was assessed if the onset of rains at such junctures can be predicted with enough skill to support livestock movement decisions. It revealed both optimism and scepticism about the role of current remote sensing and weather prediction technologies vis-à-vis variable, dryland ecologies and pastoral livelihoods.

Supply chains are to be found in almost every corporation around the world. When tryingto manage a supply chain often two types of disturbance can jeopardize the effectiveness of a supply chain. These are supply and demand uncertainty. This thesis tries to investigate the effects of those two types of disturbances in a supply chain and seeks to find a remedy. When taking a closer look at supply chains, robust supply chains are considered to withstand a higher level of supply and demand uncertainty. Robustness knows many definitions but is mostly characterized by the responsiveness and adaptability of a supply chain. Responsiveness implies a degree of quick reaction to sudden changes, while adaptability is defined as the capacity to deal with new circumstances. This thesis tries to relate robustness according to a level of resistance of supply and demand uncertainties. The supply chain of the royal dutch land forces is prone to such sudden changes. Therefore this research incorporates the context of a military supply chain to perform experiments on. The military supply chain in operational areas is selected and identified as a 3-echelon-multiple tier-supply chain, where the implementation of a networked supply chain can result in an increase in robustness. The research uses three key performance indicators to ”measure” a degree of robustness. With the use of a multi-agent discrete-event model that incorporates a highly dynamic environment including real-time transportation and order handling simulations. The model also contains an nteger supply optimization module using CPLEX and a distance-based swarming algorithm to emulate the lifelike situation of the supply chain concerning order handling and ”Notice to move”. Three experimental setus have been created that try to quantify robustness levels according to networked supply chain operations while coping with demand and supply uncertainty. First, a sensitivity analysis is performed, revealing the basic relationships between the indicator and emand/supply fluctuation. Second, an extensive model testing according to three different load cases in uncertainty is performed. Thirdly a case study specified for a military context is done. It is found that robustness can be increased by increasing the amount of networked forward supply nodes and interesting dependencies are revealed between the amount of forward supply centers and robustness. The findings of this research can be applied to other industries that have a similar supply chain design.

Purpose – The purpose of this study is to add to the existing body of knowledge and increase the understanding of the contribution of current smart emergency applications to the facility manager with the focus on required information during building fire emergency response operations. Design/methodology/approach – Semi-structured interviews with different safety professionals from Dutch universities are used to identify their role during a fire incident, an acceptable level of fire cause, necessary information items and, their viewpoint on the use of smart emergency applications. The results are translated to a multi-criteria analysis in which different smart emergency applications are evaluated according to a variety of information items. Main finding – This study reveals that not all required information is integrated into the current smart emergency apps. In fact, each smart emergency apps have a unique information provision which can be useful during a specific phase of a building fire emergency operation. In addition, the findings show that the use of smart emergency apps is partially supported by the participants of the interviews. Research limitations/implications – This study was confined to universities in The Netherlands and the semi-structured interviews have limited population size. A larger population with the focus on international universities would have allowed for more important data, but the findings do provide valuable and essential insight into the contribution of smart emergency apps to the facility managers. Practical implications – The empirical results provide guidance to the emergency and facility managers to opt for the smart emergency app(s) according to their required information and, give valuable insight to app developers and researchers for further improvement and development of the emergency apps. Originality/value – Most studies focus on the benefits and technical aspects of the smart emergency app. This research provides readers with more insight into the extent of the required information on current smart emergency apps in favor of the facility manager who needs to perform during a building fire emergency operation

The Sahel region has become a focal point of concern due to the alarming escalation of terrorism, culminating in devastating attacks that have claimed numerous lives and disrupted regional stability. Mali, in particular, has experienced a significant surge in terrorist activity. This thesis presents a novel approach of using criticality measures based on network science as pay-offs for a game theoretical model in order to protect connectivity of road networks in conflict areas. Through two comprehensive literature reviews the network scan approach, based on shortest paths, population sizes, travel distances, and travel times and the two-player zero-sum game structure were selected as the basis for the methodology. In order to enhance understanding of dynamics on the road network, semi-structured expert interviews were conducted. The model is applied to the region of Segou in Mali and optimal strategies were compared to empirical terrorist attack data of the ACLED database. It is concluded that road network protection can be strengthened to minimize the impact of terrorist attacks through the allocation of protective resources. Firstly, experts highlight the positive impact of combining military patrols and convoys with explosive ordnance disposal on road safety. Secondly, results revealed the network scan approach's potential as a valuable tool for criticality assessment. Thirdly, contexts lacking empirical traffic data, directing network protection efforts towards roads identified as optimal strategies by the model becomes prudent. Lastly, the comparison of optimal strategies against emperical data provides the means enhance insurgent behaviour's understanding. While the developed model can be considered rudimentary, it is believed to have laid down essential foundational principles. Further research is recommended the extend the model further, including validate the model's effectiveness and determine its applicability to different types of attacks and different insurgency groups. This thesis hopes to have made a contribution to the academic fields encompassing counter-terrorism, network science, and game theory within conflict settings.

With the rise of big data, the velocity of information is increasing. Velocity will affect decisions that are based on information. The question raises whether this increase of information speed, continuity and veracity contributes to an increase in the decision quality of decision-makers in organisations. Such decision quality can be addressed by addressing the trade-off between speed and accuracy that is made by agents within the decision-making process. To create an understanding about the effects of information velocity on decision-quality, the effects of velocity on speed-accuracy trade-off are addressed by this research. The context of this trade-off is analysed within the context of the decision-making process.

In a rapidly evolving digital landscape, where information is the currency of progress, universities play a vital role in fostering innovation, research, and knowledge dissemination. However, this invaluable role also exposes universities to significant cybersecurity challenges. Cybersecurity is an increasingly important topic for organisations in all sectors, including universities. As repositories of valuable research data and other sensitive information, universities are attractive targets for cyber attacks. Addressing these challenges is crucial not only to protect intellectual property and sensitive data but also to maintain the trust and integrity of academic institutions. Despite the importance of cybersecurity for universities, there is a lack of research on how to effectively implement cybersecurity policies and practices in this context. The lack of a standardised approach to cybersecurity can leave universities vulnerable to cyber threats and hinder the sharing of best practices. This study is expected to identify key challenges and measures for cybersecurity policy in Dutch universities. It will provide insights into the implementation of effective cybersecurity policies and contribute to the development of an approach to cybersecurity in the higher education sector. The research question is: "How should the cybersecurity policies of Dutch universities be designed to mitigate cyber threats to ensure knowledge security?". To accomplish this research, a multidimensional approach was adopted. Extensive literature review provided a foundation for understanding cybersecurity standards and cyber ethics, while interviews were conducted with various Dutch universities to gain insights into their experiences and perspectives. Additionally, a comprehensive survey was administered to students and staff members of Dutch universities, enriching the study with diverse viewpoints. This work aligns with the objective of examining the quality of decision-making concerning grand societal challenges within the context of their socio-economic and political environments. It aspires to inform decision-makers in the public (policy) domain or at the intersection of the public and private spheres. By shedding light on the barriers faced by universities in implementing cybersecurity norms, this research aims to contribute to the ongoing discourse on securing knowledge assets in the face of emerging cyber threats. The thesis is structured as follows: it begins with an introduction that provides a comprehensive overview of the topic, delineating the problem at hand and outlining the proposed approach. Subsequently, the literature review section presents the findings of the extensive research conducted, exploring various subjects such as cybersecurity standards and cyber ethics, among others. The expected outcomes of this study are an overview of current cybersecurity challenges for Dutch universities, an understanding of the opinions and experiences of university staff and students, and recommendations for developing and implementing cybersecurity policies for Dutch universities.

Building failure incidents are occurring within the Netherlands and these incidents can be a valuable source of technical data regarding improvements in structural safety. This research has attempted to utilize this information gained to develop a tool to aid the building construction sector in identifying hazards during building construction projects. For that purpose, this research has investigated risk assessments and structural failure incidents. To be able to develop a tool, deeper knowledge of risk assessments is required and therefore risk assessments were studied to understand how they should be performed and what characteristics makes them effective. The research of Terwel (2012) on the Cobouw database formed the basis for the analysis of structural failure incidents. This thesis investigated how other research used structural failure databases to improve structural safety and studied the data available from the Cobouw database for developing a tool. The analysis of risk assessments has shown that identifying hazards is a crucial step in the assessment process. It was concluded that it would be beneficial to a project team if it had information on which hazards related to structural failure can occur, the probability of occurrence of hazards and the possible impact they can have. An investigation into structural failure databases showed that no other research could provide a tool that fulfilled the above requirements and in addition it was concluded that the database available to this research was only sufficient to provide information on which hazards can occur. There was not enough data available to make a justified estimate regarding the consequences of a hazard or probability of occurrence. Therefore it was chosen to use a different approach to analyze the database. The Cobouw database has been restructured into a fault tree format, to relate hazards to specific components of a building structure. Afterwards an attempt was made to discover why many hazards have occurred or which building components were prone to hazards. There was not enough data available to draw conclusions on that subject either. Finally this research concluded that the most efficient way of developing a tool, from the data available to this research, is to create a guide which can be used during the design phase to caution the project team about frequently occurring hazards. The method that was used to create the tool can also be used as an inspiration for future research, because this research was limited not only by the amount of available incidents, but also by the source of the incidents, which was from a news site that would most likely not focus on low-profile failure incidents and the reporting of precise technical details. Therefore the most important recommendation this research has, is to introduce a large scale collaboration in the construction industry focused on gathering accurate data on structural failure incidents.

The canals of Amsterdam represent a draw for tourists and add to the value of real estate along the canals. More than 40 percent of Amsterdam tourists take a trip through the canals, accounting for more than 3 million canal travelers annually. Therefore, the tour boat sector is crucial for tourism in Amsterdam. Many of the quay walls on Amsterdam’s canals have either reached the end of their lifespan or need replacing because of damage caused by overloading and the overuse of heavy supply trucks. Currently, a length of 200 km out of the total length of 300 km of quay walls have reached the end of their 100-year service life, and some walls have even passed that limit. In 2017, five incidents of quay-wall failure occurred. In 2018, three such incidents occurred. Hence, these quay walls are in critical need of replacement. Many are located along busy canals, which means that closing these canals will almost certainly directly lead to the congestion of traffic flow on the water. This is highly undesirable for the tourist sector. At the moment, there is no research being conducted into how to manage quay-wall renovations without disturbing transport over water. The main goal of this research project was to develop a first version of a model through which the impact of the quay wall renovations on transport over water (e.g., passenger transport and pleasure craft) can be assessed. Thus, the main research question for this project was as follows: "How can the impact on nautical traffic flow and congestion patterns of quay wall upgrade works in the canals of the City of Amsterdam be assessed?” A model was constructed based on the Amsterdam canal traffic analysis. An agent-based modeling framework was selected as a suitable framework, because it could give insight into not only if and when congestion occurs, but also why congestion occurs. The ABM framework is able to represent the distinct sailing speed and route selection of each vessel type, can reproduce the (routing) behavior of the three vessel types that interact, and had the additional option to incorporate the irrational behavior. Each vessel was modeled as an entity with its own logic (step-by-step instructions) and making its own decisions while moving on a graph. In order to reproduce observed traffic flow and congestion patterns, modeled vessel objects needed to move through this network, calculate their routes based on the network structure and properties, and queue at crossings. Therefore, this study applied network logic (graph theory) and queueing theory. This project used the open-source NetworkX package in Python to construct a network (graph) based on the spatial coordinates of the Amsterdam canal network, with nodes at crossings and bridges and edges connecting the nodes. Each vessel object in the model used algorithms from the NetworkX package to calculate its route on the network. The model also used the SimPy package to simulate queueing and crossing congestion at crossings with discrete time steps. To summarize, the model simulated on a microscopic level, used discrete time steps, and applied the agent-based modeling framework. As this model is developed within a community setting at the TU Delft, this model, and other transport network analysis models, can be viewed and found at https://github.com/TUDelft- CITG/Transport- Network- Analysis

Equalized involvement of stakeholders in decision-making processes (DMP) allows them to achieve a balanced outcome, when aiming to create value for each participant. However, as the number of stakeholders increases, traditional methods of interactive engagement become more complex, often leading to unbalanced solutions. Identifying such balanced outcomes also becomes more challenging and time-consuming, considering the wide range of potential outcomes. To address this complexity, this thesis introduces a new approach, Preference-based Goal Attainment (PBGA), and develops a tool for its application in DMPs. The PBGA tool facilitates transparent communication of stakeholders' needs, desires, and values within the DMP, helping to bridge satisfaction gaps among various stakeholders and yield an equitable and optimal outcome. For the development of this tool, a multi-objective optimization framework is proposed. This framework allows stakeholders to integrate their needs, desires, and values as objective functions, with decision parameters represented as variables, in the tool. Crucially, it enables stakeholders to specify preferred states within their objectives, known as preferences. The framework also considers the varying influence of stakeholders by incorporating a weighted goal attainment method. This method focuses on improving the objective with the largest weighted preference deficit in each iteration of the genetic algorithms used in the optimization process. For testing and validation, the approach is applied to a DMP in a selected case study. Data, including objectives, variables, and stakeholder preferences, are collected through semi-structured interviews, and transformed into a computational format for MATLAB® implementation. The outcomes are verified using the Kolmogorov-Smirnov and Chi-Square comparative methods. Additionally, the tool and its results are validated by stakeholders, ensuring the tool's practicality and acceptance of the new design. With the PBGA tool, stakeholders can transparently communicate their needs and desires in the DMP and achieve an outcome that involves equal participation from each stakeholder. The thesis concludes with a comprehensive discussion on the practical applications, limitations, and assumptions of the tool, highlighting its potential to improve stakeholder collaboration and decision-making in project management.

Gaining insights into anticipated future expenses is essential for both the planning and construction phases of a project. A significant factor in future costs, particularly over extended periods, is the variability of material and labor prices. the change in costs over time, known as ’cost escalation,’ has been the focus of numerous research efforts. These studies primarily aim to forecast the Construction Cost Index (CCI), a composite index representing a standardized array of materials and labor typical in construction projects. This research presents a novel approach to forecast cost escalations, tailored to individual construction projects, addressing the shortcomings of predictions of a generic Construction Cost Index (CCI). Traditional CCI predictions, while providing some foresight, are limited by their generic nature and often overlook the specific material and labor variations within different projects. Additionally, current research generally fails to account for the uncertainty in the forecasts of the change in the material price and the estimate of the construction cost. In response to these challenges, this research pivots around the following research question: How can cost escalation for various types of construction projects be predicted, accounting for uncertainties in final construction costs and the forecast? Addressing this question involves selecting distinct indices for various project resources, such as steel, concrete, and labor, and combining these predicted indices in line with each resource’s cost. To achieve this, the study evaluates several time-series forecasting models, namely the vector error correction model (VECM), the vector autoregression (VAR) model, and the Holt-Winters model. For each model an automatic forecasting process is created, which automatically checks the suitability of the model, select the appropriate variables (in case of a multivariate model) and selects parameters. These models are evaluated for their accuracy across different time-frames and forecasting horizons. The Holt-Winters model, in particular, showed promise in providing reliable confidence intervals and point forecasts. The study’s testing phase revealed varying degrees of success, as volatile indices like copper and steel proved to be challenging, whereas forecasts for less volatile indices achieved higher accuracy. This outcome suggests room for improvement in refining these forecasting methods. The project-specific forecasts, including cost uncertainties, are developed by inputting cost estimates associated to the material price at current day value. The cost estimates input includes material and labor costs and their uncertainties. The uncertainties are represented through a three v point estimate (optimistic, pessimistic, and most likely values). This input is then transformed into a PERT distribution which is a transformation of the Beta distribution. The final stage involves combining the PDFs of each project activities cost (considering the cost of each specific resource) with the monthly forecast PDFs in a Monte Carlo simulation, providing a detailed cost distribution histogram of total cost and the individual resources cost. The tool’s functionality was demonstrated through a case study on a highway construction project. In this demonstration, specific project data, including material and labor cost estimates, were inputted into a hybrid web and Excel interface. This setup facilitates visualization and manipulation of project information. The tool processes these inputs via the AFP and Monte Carlo simulation, yielding comprehensive outputs such as histograms and statistical properties of the output. This is visible for total project costs and resource-specific escalations. This demonstration effectively showcased the tool’s capability to offer detailed insights into cost escalation, addressing the variability and uncertainty in construction projects. It underscored the tool’s alignment with the study’s objective of providing nuanced, project-specific cost escalation forecasts, moving beyond traditional CCI predictions. In conclusion, this study introduces a tool that caters to specific project resources, timelines, and uncertainties in cost escalations. While current limitations prevent its immediate practical application, this proof-of-concept lays the groundwork for future improvements. Focus of future research should be on refining accuracy and comparing the tool’s forecasts with escalation of historic projects to establish more robust insight into the actual escalation of projects.

Food safety inspections play an important role in the safety of consumers of food products. Even though food safety regulations have disciplinary power over food-related businesses, inspections are needed to ensure that regulations are followed for the well-being of consumers. Traditionally inspections did not have a goal of targeting higher risks. However, since 2004 EU regulations require all EU Member State inspectorates to follow a risk-based approach within their inspection plan. This is an effective way of inspecting and gives high results in catching violations. However, to be able to know what is happening in the overall industry representative results are needed, and performing inspections based on risks does not provide representativeness. Random inspections if performed in a representative way can provide this. Inspectorates are looking for ways to combine random and risk-based inspections to get representativeness from risk-based approaches. To do this the inspection strategy might need to consist of a combination of random and risk-based approaches. The reason for this is to avoid bias that may result from performing risk-based inspections. The main research question of this thesis therefore is: How can inspectorates adapt their inspection strategies to mitigate bias that results from risk-based inspections? The exploration of how risk-based and representative inspections can be combined is aimed to be supported with an investigation of methods that support this decision. This research aims to develop an assessment framework for evaluating such methods. In order to evaluate these methods, assessment criteria are needed that measure the methods appropriately. Since the reason to combine random and risk-based approaches is to reduce the assumed bias of risk-based inspections, there is a need to understand what bias is in risk-based inspections. Furthermore, to understand this, what risk means needs to be investigated. Therefore, the goal of the research is to find out how risk and bias affect the choice of inspection strategies, by investigating random and risk-based inspections and how these can be combined in order to leverage their advantages.

Recent years the Netherlands have been shocked by a series of earthquakes. Normally an earthquake would be considered as a natural hazard, however extracting gas induces these earthquakes. Whenever a hazard is induced, it is perceived as man-made and therefore controllable. This leads to differences in risk acceptance in relation to natural hazards. When ambiguity about risks exists, a participatory strategy should be used with involvement of the local community. In cases of induced hazards, low trust between local communities and authorities exists, leading to ineffective risk mitigation. The first step of improving mitigation measures is finding out whether differences in risk acceptance lead to differences in mitigation measure preferences. In this paper this research objective is carried out by gathering data in the earthquake area in Groningen with the Participatory Value Evaluation (PVE) method. Next the data is analysed using factor analysis and logistic regression. Based on the results is concluded that citizens with lower trust in authorities prefer measures that directly target the consequences of the induced hazard. While citizens with more trust in authorities and more belief in the benefits of the activity causing the hazard are more open for measures that target the indirect consequences of the hazard. During this research, assumptions were made to construct the context of the PVE method. To improve the validity of this research it is suggested to repeat the research with different variables.

Due to automation and robotization of material handling systems (MHS) in the transportation industry, maintaining equipment and systems becomes more important, and needs to be integrated in the company’s business strategy (Tsang, 2002). Implementing and investing in the right maintenance approach to keep these systems in optimum state is therefore essential for the performance of a company and more important, for the customer’s satisfaction. When a company has to deal with an increase in failures and decreasing performance, it needs to anticipate to change this negative trend. In literature, most of these problems are analysed by using electronic data and condition-based monitoring (Bouvard, Artus, Bérenguer, & Cocquempot, 2011). Analysing the condition of equipment requires data monitoring which is not always possible. Besides, these analyses are often based on a single system which conditions do not apply on systems with different specifications. TNT Express (initiator of this research) is dealing with this problem within the Benelux. They are dealing with decreasing on-time delivery (OTD) performance and increasing costs of smaller sorting-sites without technicians. Having high OTD performance is an essential element of TNT’s business strategy, and is a competitive advantage in their industry. Their time-critical processes combined with the interrelated network structure of their sites makes it hard to develop a single strategy or solution that positively affects the performance of all sites. The differences in size, work methods, level of technology, different size of freight and fluctuating demands makes it a dynamic and complex environment with a lot of variables that influence the performance of on-time delivery. That makes it hard to identify the causes and effects of these failures over time. Besides, TNT doesn’t have the useful data to base their maintenance on. Therefore, finding the bottlenecks and the right variables that influence this performance most is essential for decreasing the costs of TNT. Therefore, the objective of this research is: “Identify the constraints of TNT’s Material Handling Systems in a dynamic environment, to be able to apply the right maintenance strategy that preserves TNT’s delivery performance at lower cost.” Before a start could be made on finding the bottlenecks of the sorting sites, TNT’s maintenance structure, operational structure, financial structure and operational structure needed to be defined. TNT’s sorting operation, where freight is unloaded, sorted, measures and loaded, runs around 19 hours a day, 6 days a week all year long. That limits the time for maintenance and check-ups to 6 hours a day. Still, a fast response is needed when the sorter does fail during operation, to prevent the process getting delayed which generates extra costs. TNT’s maintenance of those sites turned out to be depending on mostly corrective maintenance, and less on preventive and predictive maintenance. Mechanics are traveling from site to site to solve current incidents that cause delays, and less for preventive maintenance and check-ups. Original equipment manufacturers (OEMs) are also responsible for some maintenance activities, although these are mostly time-based and less used for trouble shooting. When specifying TNT’s on-time delivery performance, the performance of the sorting sites turned out to be the most important factor of the OTD. This sorting site performance is measured by the capacity of freight it theoretically can handle, and the amount of freight it is transporting in reality. This site performance is also influenced by several variables, where the Material Handling Systems availability turned out to be the largest influential variable. The failing devices and equipment are the cause that IV operators on the floor have to sort manually, which takes a lot more time. Besides, these delays are generating extra costs, only how much and which costs is not quite clear. TNT’s limited registration of essential elements like downtime, maintenance costs and OEM activities made it difficult to link these elements, and to find possible bottlenecks. So, to be able to link the MHS- downtime with the related costs on a daily basis, three Critical-to-Quality factors (CTQs) are analysed:  Breakdown costs  Maintenance costs  MHS availability Because the financial impact of a breakdown was not yet specified, the most important factors are determined by interviewing business improvement experts. Some costs turned out to be different for the import or export process during a working day. For TNT, the next financial factors turned out to be important: 1. Missed Check-Weight-Cube (CWC) revenue 2. Hiring extra vans and trucks 3. Personnel costs (overtime) 4. Financial consequences of lower service level (loss of customers) From these factors, the missed CWC revenue turned out to be largest costs factor in the import process, where hiring extra trucks and vans turned out to be the largest costs factor of the export process. These two factors are further specified and calculated, to link them to specific breakdowns. Depending on the volume in different locations, the missed CWC revenue is variating between €000,- and €000,- for a single shift per day. These costs are calculated by determining the number of packages that are affected by a breakdown and do not get a second weighing check on a next location, multiplied by the average revenue missed per parcel. These calculations showed that without a working sorter or CWC on a single location comes with large financial consequences. These specific insights in CWC revenue loss show the need for fewer failures to improve the financial performance of TNT by changing the maintenance activities. The extra costs of hiring extra vans to transport all freight towards the customers in the export process turned out to be €000,- for a single shift (1day). These costs are in the same order as the CWC revenue losses, and from financial perspective just as bad. The cost analysis regarding this data showed how disperse the information is within TNT, and that it is hard to develop a strategy without this kind of crucial information. Maintenance costs are referred as costs that are made to solve the incidents and include, service contracts, mechanics salaries etc. However, costs that are made for decreasing the number of incidents are more important. Think of hiring extra employees, changing service contracts, training current employees. Quantifying these costs is only possible when the bottlenecks of the breakdowns have been derived. V The lacking quality of the data regarding downtime and availability of the MHS made it impossible to use mathematical solutions to find the bottlenecks, so “Soft Operations Research” methods have been used to find the causes of the failures (Heyer, 2004; Masys, 2015). Using Pareto charts and Ishikawa diagrams on the incident file controlled by TNT’s mechanics, the data in this file is structured and enriched to find failures with recurring root causes that are a structural problem. First, three devices that have the most impact on the MHS availability are determined. These are the Sorter, Roller track/belts and Check- Weigh-Cube (CWC) and are responsible for 130 of the 210 incidents for the first 5 months in 2017, which represents more than 60% of all incidents of the MHS. The registered incident data of these three devices is enriched by going a step further in the cause, structuring all data, and visualising them in an Ishikawa diagram. By doing so, it became clear that a high number of incidents (35%) have an operation cause, which means that they are caused by human error. These incidents are not specifically location bounded, and solving the most frequent occurring incidents will prevent a lot of future incidents. The three selected root causes that are responsible for most incidents are: 1. Lack of system knowledge Operators, especially Team Leaders (TL) and Leading Hands (LH), have too little knowledge of the process, the effects of failures and simple technical solutions. There skills haven’t been developed with the increasing mechanisation. Not knowing the impact of failures, and how simple incidents can be prevented leads to unnecessary failures. 2. Sensor related incidents A large part of those operational incidents are sensor related. Because these incidents are still frequently occurring, they need to be handled separately. These incidents can be solved quite easy most of the times, and a support system to help operators to solve them is very helpful. 3. Lack of working according instructions A large part of the incidents are caused by wrong choices of operators, by putting wrong parcels on the sorter, putting them on the sorter at the wrong place or working not according the given instructions. The lack of knowing the consequences results in an uninterested work attitude which results in a performance decrease of the sorting site. For these three root causes, several improvements have been developed. For the first root cause, a training program is recommended which start with a well-communicated plan to create the urge for change. The current attitude of personnel is asking for a plan that provides support from the whole organisation. TNT needs to know the value of knowledge amongst their employees, and this needs support from top management. Combining this with low-technical training sessions and useful supporting tools will increase the employees’ knowledge, and also creates ownership with the employees. To do so, TNT needs to review its distribution between part-time employees from employment agencies, and employees contracted by TNT. Increasing system knowledge amongst employees is only effective if that knowledge stays within the company. Rewarding well performing VI employees with a contract by TNT will have positive effect on the knowledge on the work floor and not only prevents, but also decreases the amount of downtime. For sensor related incidents, a supporting manual has been developed that functions as a simple flow chart that guides TL through the process of solving these failures. Following these steps in the included manual gives simple but clear instructions how to act and what to do. Before using this supporting manual, the TL need sufficient training before they can execute the process. Although this solution is more focussed on corrective maintenance, this manual helps to solve simple incidents that mechanics do not have to solve by themselves. Not only are these incidents solved much quicker, due to direct handling of the TL, but the mechanics do not have to drive towards the location of the incident. That also gives the mechanics more time to do preventive maintenance tasks, and therefore further decrease the number of incidents of the MHS. This manual strengthens the need for more direct or autonomous maintenance within the sorting sites of TNT (Chen, 2013). Currently, these sites are too depended on the knowledge of external maintenance providers to repair the failures which is not desirable. To improve that employees work more according instructions, better supervision is advised and recommendations are made to reward well-performing operators. Rewarding operators also means that they will be more responsible for their colleagues and have to make sure that they understand the instructions. They become responsible for preventing wrong parcels being put on the sorter, and have to correct their employees if they ignore the instructions because a lot of incidents are caused by wrong packages being put on the sorter. However, TNT should also improve the visibility and clarity of these instructions by using dummy parcels to indicate the allowed parcels sizes. Improving this product flow on the sorter will improve the sorters performance and lowers the number of incidents. The effect of these improvement could only be expressed by a decrease on the number of incidents, and not on downtime due to lacking data registration. If, as a starting point, already half of these structural incidents is solved by these improvements, the total amount of these 210 incidents over 2017 would already decrease with 13%. Due to the unknown length of the breakdowns, they cannot be compared to financial results although these results definitely improve. All these improvements are based on autonomous maintenance which is focussed on letting operators do more maintenance and technical tasks. Finally, all these improvements will help retaining TNT’s on-time delivery and decrease their breakdown costs. This process of finding these bottlenecks led to an even more valuable advice for gathering data. This research showed that TNT needs to improve their data collection system, and document important factors with a computerized maintenance management system (CMMS) like time registration, downtime during operations, and weight of breakdown. This data also needs to be accessible for OEM’s, so they also have insight in the failures, and can help improve the MHS performance. With this data, TNT can measure the downtime and better monitor its MHS availability to see which devices or incidents need further improvement. The importance of such data collection system is once emphasized by the fact that it was hard to determine the bottlenecks, especially with multiple locations at the sorting locations of TNT. VII The goal was to identify the constraints in a dynamic environment that has a lot of influential factors that make it hard to determine the effects of these variables. Using multiple techniques based on the number of incidents and the failure history, the first bottlenecks could be derived that needed improvement. Together with a costs analysis on the most important financial factors, solutions and recommendations are developed that will retain TNT’s on-time delivery at lower costs. However, the recommendations for a more centralized data-based maintenance system could even be more valuable in the future.

The research effort on autonomous ships has increased over the last years. The realisation of these ships will have as a consequence that the crew can be reduced significantly or even be removed entirely, resulting in unmanned ships. Although there is a strong belief that unmanned ships would lead to more economic efficiency, only limited research has been performed in order to demonstrate what the overall effect of the change to unmanned shipping would have on transport costs. Nonetheless, more reductions in cost or improvement of transport performance for unmanned ships would make them more attractive and economically viable. The design of a ship is subjected to regulations and requirements that limit the design freedom, but it increases safety. Removing the crew from the ship reduces the risk of shipping, under the assumption that the probability that an incident occurs does not change, since the lives of the crew are no longer at risk. If the risk is lower, the requirements to the design of unmanned ships might become less strict, while maintaining equivalent safety. In this way more design freedom can be realised for unmanned ships, as well as more economic efficiency. Within this report the required subdivision index will be evaluated, for it is expected that reducing this requirement can create more design freedom. Therefore, this research will focus on what reduction in the requirement concerning damage stability for unmanned ships can be allowed, if they are subjected to an equivalent level of risk as manned ships of the same size and type. It has been found that the required subdivision index can be lowered for unmanned ships, based on equivalent safety. The contribution of the risk of losing lives to the overall level of risk is larger for smaller ships. This is reasonable, since for larger ships, the size of the crew increases with a lower rate compared to the amount of cargo, installed power or capital costs. Therefore, the allowable reduction in the required subdivision index is largest for smaller ships. However, the size of the reduction depends strongly on missing accident statistics concerning the loss of life. The missing data results in the following uncertainties, for which further research is recommended. Firstly, for different ship types differences in the accident statistics are present. There are significantly less fatalities related to bulk carriers and container ships compared to general cargo ships. It is expected that these differences are related to the individual ship size and crew size, for which both the correlation with the probability of losing life is unknown. Secondly, there is a discrepancy between the theoretical probability of survival of a ship, namely the attained subdivision index, and the probability of survival that the accident data suggests. The attained subdivision index suggests that at least 30% of all accidents should lead to a total ship loss. The accident data reveals that for general cargo ships around 10% leads to a total ship loss.

This thesis focuses on the combination of agent based modelling and Bayesian network. These techniques are used to see if this is a viable way of modelling green markets, like the heat pump market. These markets can change quickly. The combination of the techniques gives the investors in the model the ability to learn from new information during the model and change their strategy or decision upon this. The outcomes show that this is a promising combination and future research should be done to further explore the possibilities.

Machine learning applications are increasingly being implemented in socio-technical safety-critical systems, but the safety of these applications is not well understood. This thesis used a mixed-method design and applied four methods to explore the field of machine learning and safety. With the results of these four methods, a framework was constructed for the risk analysis of machine learning applications. All findings were synthesised in two ways: by drawing conclusions about machine learning capabilities, and by drawing conclusions about organisational capabilities. This approach provided a way to think conceptually about how far we can take machine learning to increase safety in socio-technical safety-critical systems.

In order to increase safety and efficiency at sea the International Maritime Organization decided in 2000 that all sea going vessels were to implement Automatic Identification Systems (AIS). AIS is an automatic tracking system which broadcasts static and dynamic information about the vessel. Even though AIS was not initially designed for research purposes, large amounts of AIS data are available from which valuable insights can be gained. In this research an AIS Port processes tool is developed, which transforms (raw) AIS data into data containing information about the entry and exit times of certain port locations. With this tool and based on data analysis of multiple terminals, the inter arrival time distribution, the service time distribution and the berth occupancy can be obtained, these are key parameters for using Queuing Theory in terminal design. Following, these parameters and distributions can be compared to what current nautical infrastructure guidelines advise and expect. Insights gained from these AIS data analyses lead to innovative ideas and recommendations for current theoretical frameworks used in port planning.

The increased decentralisation and heterogeneity of critical infrastructure systems pose a threat to the safe and secure operation of critical infrastructures by complicating cybersecurity procedures. The increased frequency and impact of cyberthreats have led to the desire to develop coherent security policies. In order to explore the effects of such policies, an ecosystem-level framework for cyberincidents in critical infrastructure systems has been developed. This framework can be used to explore the effects of conceptual defensive strategy designs. The aggregation of these concepts was operationalised around a central degree of critical infrastructure operability. A practical application of the framework is provided in the form of an agent-based modelling study that is capable of simulating the effects of coherent defensive strategies. The framework by itself is limited to exploratory modelling for the sake of generalisability and requires further specification of concepts based on representative infrastructure scenarios for more advanced analysis and design of tangible security policies.