Megaprojects frequently face cost overruns and schedule delays, a pattern known as the iron law of megaprojects, which persists partly because traditional determinis tic or probabilistic planning methods are inadequate for managing deep uncertainty. This form of uncertainty arises when the probability, timing, or impact of key events cannot be reliably estimated, often leading to unrealistic schedules and ineffective risk responses. This study investigates the question: How can Exploratory Modelling and Analysis and Dynamic Adaptive Policy Pathways be applied to improve schedule robustness in infrastructure construction projects? The research focuses on the Schiphol bridge reconstruction, which is part of the Veenix A9 BaHo project and is conducted in collaboration with Count & Cooper. A Discrete Event Simulation (DES) model is built in SimPy and structured using a task dependency graph de rived from the project’s original schedule via NetworkX. The model is sampled 10,000 times under baseline conditions using Latin Hypercube Sampling. In scenario discovery, Patient Rule Induction Method (PRIM) is used in combination with a scaling function identifying six high-impact scenarios, which serve both as inputs for robust policy search and as Adaptation Tipping Points (ATP) for the Dynamic Adaptive Policy Pathways (DAPP) schedule. Robust mitigation strategies are derived using a Multi-Objective Evolutionary Algorithm under the Multi-Objective Robust Decision Making framework, with a second PRIM experiment selecting four final robust policies. These policies correspond to at least one of the high-impact scenarios and form the backbone of a conditional DAPP schedule. The DAPP schedule is evaluated against a static baseline using 5,000 DES simulations with identical uncertainty sampling. In 20 comparative runs, it reduced project duration by an average of 67 days and cost by approximately e97.5 million on the entire project schedule. All three robust policies include the measures new design, overtime labour, and electric machinery, suggesting that a focused subset of actions can improve resilience even when the future is highly uncertain. Unlike prior Decision Making under Deep Uncertainty (DMDU) applications, which often focus on long-term or high-level strategic planning, this study embeds adaptive logic within a highly granular, task-level construction schedule based on real project data. This approach raises methodological challenges in how adaptation tipping points are defined, triggered, and monitored within network-based simulation. The findings demonstrate not only the feasibility of combining Exploratory Modelling and Analysis (EMA) and DAPP in operational construction settings but also the need for further research into real-time scenario recognition and policy switching mechanisms under uncertainty.

This thesis studied which modeling approach is most suitable to simulate the human behavior of inhabitants in river deltas during environmental changes. The Vietnamese Mekong Delta (VMD) was chosen as the test case. The salinity levels are rising in river deltas, and it is studied how the impacts on local populations can be modeled. System Dynamics (SD), Discrete-Event Simulation (DES), and Agent-Based Modeling (ABM) were compared. There were too few advantages of DES compared to SD and ABM for this case, and therefore, only models were created in ABM and SD.

The ABM offers several advantages, among others, the ability to simulate individual human behavior and emergent behavior. However, there is a risk of overfitting with the current level of data, and the model is more complex to understand.

The SD model runs fast, and the stock-flow structure provides a clear overview of the system. However, the aggregation level of SD also has limitations: individual behavior cannot be modeled.

Due to the strengths and limitations of both techniques, the results of the two models differ considerably. Nevertheless, the ABM model provides a more realistic representation, and it is therefore recommended to the employees at Deltares to develop a model in ABM. This is one of the few studies that developed both an ABM and an SD model using the exact same variables, and it is the first one related to river deltas and farmers. Furthermore, it can be seen as a stepping stone for Deltares to continue its research towards developing a socioeconomic model to simulate the impacts of inhabitants in the VMD.

This thesis researches passenger preferences regarding the security time slot reservation system at Schiphol Airport. This study is motivated by the fact that time slots are currently used less than expected by Schiphol, while time slots aught to be one of the solutions for the rising number of passengers, which cause more pressure on several systems at the airport, including the security checkpoint.

The main goal of this thesis is: "to investigate passengers’ preferences of a security time slot reservation system at Schiphol Airport. Additionally, the study aims to identify the optimal balance between travellers with and without a time slot for the security check point to ensure system effectiveness." Through a survey that includes both a Stated Preference Analysis and a Factor Analysis, passenger preferences are researched. In addition, a discrete event simulation is done in order to explore possible scenarios for time slot usage.

The research shows that passengers who book a time slot tend to have a more favourable view of the system than those who do not have a reservation. While having time slots brings more certainty to catch your flight, it can also limit flexibility, which some travellers see as a downside. Stress levels vary between these two groups, with those using time slots reporting less stress because they know what to expect due to time slots. However, people who are not familiar with the time slot system often remain sceptical, underlining the need for better awareness to change these perceptions.

When it comes to passenger preferences, the time between security checks and departure plays a crucial role in deciding whether to reserve a time slot. The study indicates that passengers find the most value when this time is less than 100 minutes, with the benefits decreasing as this time increases. Cost sensitivity is another important factor, as passengers react strongly when a previously free service becomes a paid one. On the other hand, their sensitivity is less strong for potential price increases. A similar trend is seen with saved waiting time, where the first few minutes saved are more valuable than larger time savings. Business travellers and passengers travelling with children tend to be less cost-sensitive, likely due to company support or a focus on convenience. Moreover, Schengen passengers are more likely to book time slots when the time between security and departure is short, while non-Schengen passengers have a less strong preferences towards short time slots.

The scenario analysis indicates that adding more security lanes can help reduce waiting times, but this effect is less strong when queues are already short. The study also finds that having dedicated security lanes for time slot passengers is not very efficient unless a large number of passengers are using them. Instead, a mixed strategy (with other types of lanes, such as priority class) is suggested to enhance efficiency. Additionally, peak shaving, which involves spreading out passengers across different time slots, is shown to be an effective method for managing queue lengths and improving overall flow.

Fugitive interception is a challenging task, requiring the police control room to decide in a matter of minutes on the optimal positions of police units to intercept a fleeing suspect. Models can support the police by suggesting interception positions. This dissertation addresses four barriers to using models for decision support in fugitive interception: timely simulation-optimization, effective representation of the search space, simulation of fugitive behavior, and adaptation to information updates.

“How can port resilience in archipelagos be evaluated—and what lessons emerge?” This dissertation develops a novel method to evaluate port resilience by integrating the existing theories with real-world challenges. Through a multiphase mixed-method approach, three case studies were carried out: Pantoloan Port (2018 Sulawesi Earthquake & Tsunami), Seba Port (2021 Cyclone Seroja), and Mamuju Port (2021 Majene Earthquake). The results provide actionable recommendations to enhance port infrastructure, operations, and recovery—demonstrating the method’s usefulness for policymakers and port operators in vulnerable archipelago regions.

Collective action is required to tackle urgent global challenges in energy and climate, biodiversity and food, and peace and security. The United Nations, therefore, adopted 17 global Sustainable Development Goals, to be met by 2030. Implementing these goals has proven to be no mean feat, but they are essential to establishing a sustainable society of ecological, social, and economic stability far into the future.

Psychological processes can provide insights into the dynamics of the complex problems that come with the current challenges. One key psychological component of many of these problems is the need for behavioral change on multiple levels. To stimulate such change, the public sector, societal actors, and the private sector can initiate behavioral interventions, ideally changing behavior simultaneously and consistently on population, community, and individual levels. Intervention frameworks like the Behaviour Change Wheel (BCW)1 can be helpful for a systematic approach to developing such behavioral interventions.

In addition to the BCW, using persuasive games2 as an intervention tool looks promising. Persuasive games seem able to influence players’ Capability (including knowledge and skills) and Motivation (all brain processes that energize and direct behavior), two components of the BCW that theoretically directly influence behavior. Moreover, these games can create personal experiences, making societal issues that may feel overwhelming and far away understandable and relevant, even for skeptics. Crucial to this is that the game’s message is interweaved within the game’s content without the risk of explicit approaches to persuasion that can backfire or be of limited use.

Still, despite the widespread application and promising impact of persuasive games, evaluation studies’ results often seem inconclusive. Game scholars, therefore, conclude that there is still a long way to go before the viability of games as a persuasive medium can be firmly determined. This dissertation attempts to make a positive contribution to this task.

Any research that aims to increase the impact of persuasive games, particularly how they can convey Meaning (ensuring that the game will serve its goal and support knowledge transfer)3, seems like a welcome contribution to the validation research field of persuasive games. Within the context of persuasive games that promote a sustainable society, a focus on reducing players’ attitude-behavior gap seems particularly relevant to strengthen this Meaning. The gap exists when individuals’ attitudes do not correlate with their actions. People can be conflicted by several factors that lead to inaction, although they often agree that action is required. For example, many people agree that humans cause climate change and that it needs to be addressed. However, they still do not make the necessary personal sacrifices to adjust their lifestyle. To reduce the attitude-behavior gap, it seems essential to strengthen people’s behavioral intentions. According to the influential Theory of Planned Behavior (TPB)4, behavioral intentions are the most direct determinants of human behavior. They are directly influenced by one’s attitudes, subjective norm, and perceived control. In addition to these three behavioral predictors, it also seems relevant to examine the factor of responsibility to improve the behavioral impact of games for a sustainable society, as the attitude-behavior gap may be partly caused by people’s lack of responsibility to put their intentions into action...

Even in this digital era, the data for improving supply chain visibility is often sparse due to the actors’ reluctance to share information or because of illicit activities. This dissertation demonstrates a simulation approach for reconstructing illicit supply chains with sparse data, that could help robust decision-making on effective interventions. We use a simulation calibration approach in combination with a ground truth simulation model of a stylized counterfeit Personal Protective Equipment (PPE) supply chain as case study.

Humanity has always faced extreme events that pose significant challenges due to their unpredictability and substantial impact. While, in the past, these events were relatively rare, recently, the frequency, intensity, and geographic spread of extreme events have increased dramatically, driven by factors like climate change and globalisation. The European heatwaves of 2022, 2023, and 2024, for instance, demonstrated how climate-driven extremes can severely impact modern societies, with tens of thousands of heat-related deaths. Likewise, the COVID-19 pandemic revealed the vulnerabilities in global preparedness for health crises, causing severe economic impacts and widespread social disruption. Both phenomena underscore the urgent need for enhanced resilience and preparedness in the face of increasingly frequent and intense extreme events....

In the Netherlands, fewer than half of violent crimes lead to convictions. A key method for increasing convictions is red-handed arrests, which can be enhanced by developing a decision-support system for optimally positioning police units. Existing models often assume an empty city, using maximum road speeds for both fugitives and police, which ignores real-world traffic conditions.
This thesis aims to improve the realism of these models by incorporating traffic effects, such as congestion and delays from traffic lights, into the optimisation of police strategies. The research examines how these traffic conditions impact the escape and interception processes, with the goal of increasing the accuracy of interception strategies and reducing the number of unpunished violent crimes.
The research question addressed is: 'What is the added value of considering realistic traffic conditions in the optimal positioning of police units for fugitive interception?' To answer this, a discrete event simulation model was developed using insights from a literature review and interviews. Simulation modelling was chosen to test complex scenarios without the biases and costs of real-world experiments.
The literature review identified that traffic includes static, semi-static, and dynamic components, such as traffic lights, open bridges, and congestion. It also revealed factors affecting criminal behaviour and route choices under stress, supplemented by interviews with stressed parcel delivery drivers. This knowledge, combined with understanding police behaviour during interceptions, informed the development of the simulation model.
The results showed that incorporating traffic conditions into the simulation model increases the probability of interception. Specifically, when fugitives face delays before reaching highways, the likelihood of interception improves because police units, moving faster due to their priority status, benefit more from traffic delays.
Optimisation of police positions was tested with and without traffic delays, and it was found that positions optimised with realistic traffic conditions were more robust. This was particularly evident in city centre scenarios but not in port dock areas, likely due to the high interception rates in the docks which made traffic impacts harder to assess.
In conclusion, models excluding traffic conditions are less effective in intercepting escape routes compared to those incorporating traffic. Therefore, integrating traffic into optimisation models is crucial for maximising interception probabilities. To mitigate the negative effects of omitting traffic, deploying additional police units is recommended.
The study also found that the impact of traffic on interception timing is more significant than accounting for the suspect's mental state. Traffic affects the timing of interceptions: accurate traffic estimates provide more time for police, while incorrect ones reduce it, affecting interception success. Future research should explore the effects of dynamic traffic conditions, such as varying green times and more differentiated fugitive and police behaviours, to optimise model accuracy while managing computational demands. Recommendations for police include integrating realistic traffic conditions and prioritising traffic lights over congestion, as well as experimenting with different escape speeds to maintain effectiveness in varying scenarios.

This study examines the challenges faced by the Customs Administration of the Netherlands in supervising cross-border e-commerce imports under the current European customs legislation, the Union Customs Code (UCC). Through a mixed-method approach, including qualitative interviews, document analysis, and simulation modelling, the study identifies potential supervisory actions to strengthen e-commerce import supervision, particularly in anticipation of the proposed UCC reform. The findings indicate the complexities of customs operations and emphasise the need for efficient, fair, and effective enforcement while fostering trust and accountability. The uncertainties surrounding the UCC reform, trade markets, and technological advancements are addressed, with recommendations that focus on strategic automation, stimulating curiosity among customs officers, clear communication, and further research to develop adaptive supervision strategies. These recommendations aim to address core challenges and exploit opportunities for improvement while considering feasibility constraints and potential uncertainties.

When a crime is committed, catching the offender in the act has several benefits. Among others, it provides evidence of the offender's involvement in the crime, which simplifies the conviction process. However, to capture an offender in the act, the collaboration of multiple police officers is required. When a crime is being reported through the emergency line, it is the responsibility of the police officers in the control room to answer the call. They gather information on the type of crime, the location, a description of the offender, etc. Subsequently, they dispatch police units to the crime scene. The first unit prioritizes reaching the crime scene to ensure the safety of citizens, while the other units are tasked with locating and apprehending the offender.

To adequately respond to reported crimes, police officers in the control room and units on the street rely on intuition, experience, and habit. This leads to the development of individual-specific strategies for handling interception scenarios. Reducing the dependency on these individualized approaches by identifying proven and robust strategies could increase the likelihood of successfully capturing a suspect. However, how the police currently save data on fugitive interception scenarios does not allow for such identification. Hence, alternative approaches to overcome this limitation must be found. Therefore, this paper explores whether simulation and game theoretic analysis are suitable methods for determining robust interception strategies for the police, aiming to increase the catch rate in fugitive interception scenarios.

Classical game theory is the mathematical theory of interactions among rational decision-makers with opposing interests. It offers valuable insights into the decision-making processes, compromises, and strategies the police and offenders may employ in real-world situations. To analyze the fugitive interception scenario with game theory, it is first simulated with an agent-based model. In this simulation model, the police and offender are individual agents with opposing interests and individual decision-making processes. Before modeling, research is conducted to find the current strategies that the police and offenders can potentially adopt.

Given the limited availability of data on fugitive interception scenarios, literature and expert interviews serve as sources for data collection. They provide insights into the behavior and strategies of both agents. Both sources emphasize the nature of the crime as a primary indicator of the offender's escape behavior. Large crimes, such as assassinations or armed robberies, are typically well-planned and characterized by predefined escape routes and rational behavior. During their escape, offenders of large crimes are found to be less susceptible to external factors such as crowd flows or police sightings. On the other hand, smaller crimes are more frequently committed spontaneously and associated with bounded rational behavior. This is depicted by their chaotic and unpredictable escape routes while taking many turns.

For the game theoretic analysis, the results of the simulation model are analyzed. The fugitive interception project is regarded as a non-cooperative zero-sum game. The results are presented in a payoff table, in which Nash equilibria are calculated. Nash equilibria are the points at which no player can single-handedly improve their outcome when the other player does not change strategy.

The pure-strategy Nash equilibrium resulted from the offender strategy where they started at a central metro station and aimed to transfer to a train network. These routes were frequently identified as the shortest compared to other end goals. Conversely, strategies that focused on getting as far away as possible, as quickly as possible, were found to be the least successful.

In determining the success of the police strategy, two factors were found to be crucial. Firstly, strategies where the police conducted surveillance on the metro platforms, as opposed to the station exits, proved significantly more effective. This highlights the importance for the police to strategically position themselves where the offender is most likely to pass, irrespective of assuming it to be the offender's final destination. Secondly, the police's response time served as an indicator for capture success. The quicker the crime is reported, the faster the police can take action to capture the offender, which increases capture chances.

Additionally to the game theoretic analysis, the relationship between the model’s output and its sensitivity to changes in input variables is tested. Results showed that variations in input did not lead to significant changes in output. This can be attributed to the deep uncertainty of this model. To address this challenge, the model must be refined, and done with more iterations.

In conclusion, by combining simulation and game theory new insights can be found beyond what either method can provide individually. By modeling the dynamic nature of a fugitive interception scenario, the success of the offender and police behaviour can be found. This can help the police during decision-making to adopt more robust strategies while considering the dynamic nature of the environment and strategic interactions with the offender.

The study addresses the knowledge gap by simulating offender and police behavior, and analyzing the result with classical game theory. This study has created a simulation model with an intuitively driven agent in a complex dynamic problem. Potential improvements in offender capture chances, with findings informing effective and unbiased police interception strategies. The study aims to contribute to crime reduction and foster increased trust in the Dutch national police. However, before generalizing the results future research must be done to overcome limitations resulting from the simplifications of this simulation model.

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

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

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

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

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

This research explores the application of geospatial intelligence and machine learning to map and analyse routes used in illicit supply chains, specifically targeting the networks that transport illegal goods from South America to Europe. With drug trafficking routes becoming more adaptive and complex, particularly in the corridors connecting South America to Europe, traditional methods of monitoring and interception have proven insufficient. Enforcement strategies often rely on seizure data, which is delayed and offers an incomplete view of these evolving networks. To address this, the study proposes a novel methodology that combines satellite data with machine learning to identify and map elements of the supply chain, such as cultivation fields, clandestine laboratories, and transportation routes. This approach aims to create a scalable solution for tracking these illicit networks and supporting law enforcement.

Through the use of supervised machine learning models, potential cultivation fields and clandestine laboratories are detected in satellite imagery. The methodology includes sourcing data, configuring machine learning models based on literature, and evaluating model performance. The challenge in training models is the limited labelled data for illegal activities.

Once these nodes are identified, a connected graph is constructed that integrates both licit and illicit transport routes. This graph serves as the foundation for estimating smuggling routes. The process involves combining graphs of waterways and roads with airstrips, ports, potential cultivation fields and laboratories. To estimate paths between illicit sites and the main infrastructure network, an algorithm is applied that estimates the least steep path based on a slope raster.

Route estimation is the next step, where Dijkstra's algorithm is employed to calculate the most probable paths. Each edge is weighted based on travel time, cost, and risk, with factors adjusted to account for various modes of transport, including trucks, boats, planes, and donkeys, depending on terrain. There is also a factor added to account for transloading. These weighted paths offer a realistic picture of choices traffickers might make to minimize travel time, cost or risk.

To test the model, a case study is performed focusing on the Colombia-Venezuela border region, a region with high trafficking activity. The case study applies the graph-based methodology to identify plausible trafficking routes, connecting cultivation sites to ports. By calculating optimal paths, the study reveals which routes are likely to be used most frequently, providing insights that could potentially aid in targeted enforcement.

There are some limitations and areas for future improvement in this proof of concept, such as challenges with data quality and model accuracy, as well as potential refinements to the methodology to enhance real-world applicability. However, there is potential in combining machine learning with geospatial intelligence to create a tool for tracking and disrupting illicit supply chains. This research offers a proof of concept for enabling scalable monitoring of illegal activities, supporting efforts to disrupt global trafficking networks.

Advances in supply chains together with more turbulence in today's business environments, increase the vulnerability of supply chains to disruption. Various cases of supply chain disruptions in recent decades have revealed that besides focusing on efficiency, improving resilience is also crucial for a supply chain to be competitive in the marketplace. The first step in improving supply chain resilience, or in general, improving supply chain risk management, is to identify and assess risks. The approaches toward assessing risk mostly depend on the availability of information about the disruptive risks. These approaches assume a known probability of occurrence of a disruptive risk and its magnitude, or the methods provide ways to estimate these risk parameters. Although probability‐based approaches toward risk modeling work fine for common supply chain disturbances or operational risks, they cannot deal with less frequent or unknown disruptions. In this research, we suggest a consequence-based approach toward the supply chain risk analysis. Instead of focusing on identifying the root cause of risk and determining its characteristics, such as the probability of occurrence and intensity, one concentrates on analyzing the negative impact of the disruption on supply chain performance. A big challenge for performing an efficient consequence-based risk analysis of a supply chain is analyzing a comprehensive set of disruption scenarios that the supply chain may encounter in the future. Also, resilience practices for overcoming supply chain disruptions must be effective under as many disruption scenarios in the future as possible. To address these challenges, we draw on the paradigm of decision making under deep uncertainty (DMDU). This paradigm emerged in the context of climate change adaptation in response to limits to predictability. A foundational idea in this paradigm is to apply models for exploring rather than for predicting the future. Robust decision making (RDM) is one of the approaches in DMDU that focuses on identifying planning strategies that result in satisfactory outcomes across a large set of scenarios regarding the future. We investigate the potential of using RDM for supporting consequence-based risk analysis of a supply chain.
Network-level stress testing of a supply chain plays an important role in resilience practices. However, the common graphical perspective on the structure of a supply chain network, where different network structures are represented by a configuration of nodes (facilities) and links (transport), may ignore key operational aspects of the supply chain that can affect resilience. We proposed an approach in which we compared the resilience of several supply chain structures that differ in three operational aspects including product structure, sourcing strategy, and production strategy.
A successful supply chain resilience practice depends on reliable stress-testing of the supply chain. A reliable stress test of a supply chain depends on the availability of information about the supply chain structure, where relations among different actors of the supply chain are clear. However, the complexity and globalization of today's supply chains make it difficult for decision makers to access data and information, especially from actors more than a few tiers upstream or downstream. Also, as a dynamic system, a supply chain is confronted with changes in structure and configuration over time. Here, there is a need for an approach for stress testing a supply chain that can deal with a lack of information about the structure of the supply chain. We proposed ensemble modeling as a solution. Rather than focusing on a single supply chain structure, this approach involves generating and analyzing an ensemble of structures to identify vulnerability sources requiring resilience practices.

This research focuses on optimizing the performance of a hybrid warehouse system that combines automation with manual picking processes. The study utilizes a case study of an online grocer, Picnic, and investigates the allocation of orders within a buffer and the configuration of the manual picking process.

The research identifies the key performance metrics for the system, including the number of late totes, average sojourn time, average stack throughput time, and picker productivity. Several factors impacting the system's performance are examined, such as buffer lane selection and group formation strategies, minimum and maximum group sizes, number of workers, pick times, and input data.

Through comprehensive experimentation and simulation modelling, the study reveals that prioritizing the deadline of totes for buffer lane selection and the IPB time for group formation results in the best system performance. The research also suggests that the configuration of the manual process should consider the group size should be adjusted based on the expected workload.

The study identifies the consolidation stations as a significant bottleneck in the system, and repairing them promptly on busy days is essential to maintain performance. The research provides practical implications for Picnic, emphasizing efficient buffer allocation and group formation strategies.

In conclusion, this research offers valuable insights into enhancing the performance of hybrid warehouse systems. Companies like Picnic can improve operational efficiency and customer satisfaction by optimizing buffer allocation, buffer lane selection, and group formation. The findings from this research can serve as a basis for further optimization and decision-making in similar warehouse setups.

When a crime is committed, the task of the regional control rooms is to use the available situational information to identify the possible movements of a fugitive suspect to use in positioning police units. Currently, the methods to do this rely heavily on the intuition and experience of the control room employees and the speed of technology and communication. To reduce this reliance, there is an increasing demand for methods to objectively determine the tasks to undertake in a fugitive escape situation. Two methods to do this are under development which help in determining the location of a fugitive suspect and the optimal positioning of police units to these locations. However both of these methods
still require a demarcation of the possible routes that a criminal fugitive will take to be used effectively. Therefore, this study explored the possibility of making likelihood estimations of possible escape routes.

Because of a lack of reliable data, alternative methods to determine likelihood of escape routes are needed. A method that could be used is simulation. Simulation of human behaviour is however complex and careful consideration of the
assumptions in such a model is needed to be able to have a high level of confidence in the resulting outcome. To do this, it is important that the theoretical background on which behavioural factors influence the criminal fugitive route-choice behaviour is complete and it is known how these factors affect the resulting routes. This is the knowledge gap addressed in this study.

To address this knowledge gap, the question of what effect behavioural factors from criminal route choice behaviour have on escape routes will be answered. This is done by determining which main factors influence criminal fugitive route-choice behaviour and how these factors influence the resulting escape routes. The method used to answer these questions is a combination the development of
a theoretical background based on a literature review of existing research and expert opinion and a quantitative sensitivity analysis on a simulation model.

Because of a lack of research on criminal fugitive route-choice behaviour, it was necessary to use literature from the following research fields to find relevant topics: criminal decision-making, rationality in decision making and route-choice decision-making. From the literature in these fields, it was found that many different personal and crime characteristics exist, but it is unknown how these affect route choice behaviour. Next to this, it was found that rational decision-making cannot be assumed for the criminal situation and that bounded rationality needs to be considered. Lastly, from the route-choice decision-making literature, it was found that many different route-choice factors are relevant. The following list of route-choice behavioural factors was found: obstacle avoidance, risky behaviour, traffic avoidance, route distance and maximum speed, and preference for main or residential roads. For the route choice decision-making modelling methods, the following relevant topics were found: cost benefit
calculations, short or long-term goals, emotional state, choice prioritisation and timing. These two lists of factors should be considered when conceptualising criminal fugitive route-choice behaviour.

In the conceptualisation phase of this study, it was found that while many different suspect and crime characteristics might affect suspect behaviour, no specific behavioural profiles could be used to conceptualise route-choice behaviour. Therefore it was chosen to conceptualise the behaviour by creating
dynamic strategy profiles based on behavioural route-choice factors. From the list of behavioural route choice factors to include in these strategy profiles, it was found that they can be described as either a preference or avoidance of road characteristics. The road characteristics seen to be avoided are cameras, obstacles, one-way roads and high traffic. The preferred road characteristics are a high number of lanes, residential roads, a high maximum speed and short roads. Next, it was found that there is a distinction in decisions based on long or short-term goals, which require either low or full network familiarity. For general route-choice behaviour, the conceptualisation of a route choice as a whole route between an origin and destination location was found to be most appropriate. When considering the rationality of the decisions made for the route choices, it was found that there is too much uncertainty and ambiguity in the considered bounded rationality conceptualisation to use them for aconcrete route-choice conceptualisation. Therefore, alterations to the assumptions of rationality are
used to concentualise this. Finally, the emotional state of a fugitive is included in the conceptualisation through the possibility of changing route-choice strategies. This conceptualisation is further used to describe the general criminal fugitive route-choice behaviour in this study.

To measure the influence of the behavioural route-choice factors in the conceptualisation, a route cost model was developed. In this model, the cost of a route is calculated using the characteristics of the edges in a road network. Based on this model, an experimental design is defined including a case study and sensitivity analysis to find the quantitative influence of route-choice behaviour on route metrics describing differences in escape routes through route length and overlap.

When evaluating the results of the case studies and sensitivity analysis, it was found that the influence of behavioural route-choice factors on routes depends on the origin and destination locations and the distribution of edge characteristics over a road network. Next to this, it was found that there were no
behavioural profiles leading to routes with specific characteristics and that in practical application, a broad set of strategies should be included when finding important locations is a road network to use for positioning police units. To do this, a method of using heat maps to find these locations was proposed.
This method combined with the route cost model described in this study was found to have high applicability but more research needs to be done on the usability of this method.

From the findings of this study, it can be concluded that criminal fugitive route-choice behaviour is complex and that different possible conceptualisations exist to be used for different purposes of studying general route-choice behaviour or specific behavioural factors. This affects the ability to measure the influence of behavioural factors on the resulting routes. Limitations were found on the
measurement techniques used in the quantitative method to measure differences in routes which reduced the ability to interpret the resulting influence of behavioural factors on the routes. This showed that to find the influence of behavioural factors on the routes, the results of this study can show that the route-choice factors defined in the conceptualisation affect the routes but that more qualitative research is needed to find how these factors influence the resulting routes.

To conclude, the findings of this study add to current research by showcasing the complexity of modelling route-choice decision-making and human behaviour in general and the many considerations that need to be taken when doing so. Next, it shows the difficulty of using quantitative and qualitative methods on the data type of routes to determine relations between factors influencing route-choice behaviour and resulting routes. And lastly, it adds to the current literature by developing an overview of the factors influencing criminal fugitive route-choice behaviour that need to be considered in the simulation of fugitive escape routes.

Inland Waterway Transport (IWT) is an untapped resource that can be mobilised to achieve a more sustainable transport system without compromising competitiveness. It outperforms rail and road alternatives in terms of low emissions, costs, high capacity, energy efficiency, freight safety, and security. Waterway locks are ageing assets in IWT systems and are infamous for creating bottlenecks. The effectiveness and performance of these locks can be measured and integrated into decision making to establish well-informed operational, maintenance, and renewal policies. This study addresses the following research question:How can the effectiveness of waterway locks be assessed to support lock maintenance and operation? Simulation modelling, which offers an efficient and low-risk evaluation of policy options while incorporating the intrinsic variability of the system, is selected as the core methodology. The simulation model incorporates operational aspects of the system, malfunctions, corrective maintenance activities, and the calculation of various performance indicators. An extensive list of performance indicators is complied through literature research. These indicators include infrastructure occupancy, vessel waiting times, costs, and emissions. In addition to existing indicators, three formulations for Overall Equipment Effectiveness (OEE) are proposed in lock complexes. The applicability of the selected methodology is demonstrated by employing a case study of the Volkerak complex, one of the largest and busiest lock complexes in Europe. Quantitative and qualitative data, collected through operational logs, maintenance reports, and interviews with experts, support that as the lock complex gets older, malfunctions become more frequent. SIVAK, a software package utilised by the Ministry of Infrastructure and Water Management of the Netherlands ("Rijkswaterstaat", RWS), is used as the basis of the simulation model. Extensions are made to calculate additional performance indicators and to simulate fluttering doors and slowdowns, two types of malfunctions that are diagnosed to be frequent and impactful based on maintenance reports and interviews. Experiments are designed to explore the performance of different maintenance policies, such as mean time to repair (MTTR) and inspection frequency, and different operational policies such as locking regimes under various fleet mix and lock condition scenarios. Stress tests and univariate analyses are also conducted. The study findings highlight the following:
- With rising demand, the significance of lock condition in maintaining acceptable service levels and minimising CO2 emissions becomes more evident. The findings indicate a trade-off between preventive and corrective maintenance efforts. In challenging lock conditions, faster repairs and more frequent inspections are needed to prevent capacity problems, leading to longer waiting times. Notable differences in handling capacity are observed in the three lock conditions studied.
- The concept of baseline OEE proves to be valuable as a maintenance-oriented metric. It emphasises that proficient maintenance strategies can counteract deficiencies in the lock system, resulting in improved capacity, reduced transit times, and reduced CO2 emissions. A general rule of thumb suggests that improving baseline OEE by one point corresponds to about a 1.2%-1.5% improvement in waiting times and emissions.
- Changing MTTR and inspection policies influences baseline OEE scores, but these adjustments must be aligned with the lock condition. Frequent inspections might yield unnecessary availability losses when the lock is well maintained. Similarly, the extent of benefits of shorter MTTRs depends on the frequency of breakdowns.
- A prominent dilemma in lock systems involves balancing transit times and the number of levellings. Locking regimes capture this trade-off, where reducing waiting time thresholds increases levellings and operational costs. However, some strategies can achieve an improvement in both aspects. These include expanding traffic range and considering the current state of the system when assigning lock chambers to incoming vessels.
- Service-based OEE, integrating operational and maintenance policies, aligns better with waiting times and CO2 emissions compared to the service level alone. This composite index can serve the purpose of monitoring waterway network lock systems, helping to identify losses due to unavailability and reduced speed.

A monopile installation project consists of the whole process of loading monopiles to vessels, transporting monopiles to the wind farm and drilling monopiles in the seabed. Each installation project has different characteristics as the weather at the installation location, the distances to the suppliers and the vessels that are available differ between projects. Different project characteristics require different choices to be made when planning a typical installation project in order to optimally plan or reduce the expected cost of a project. One of these choices is determining which supply chain configuration will be used to get the monopiles from the supplier to the offshore wind farm. The choice of configuration is important as it determines the type of vessels that are required and if intermediate storage ports need to be rented, which requires initial investments.

The purpose of the research study is to assess the performance of different supply chain configurations. The performance of these different configurations is assessed on the key performance indicators project cost, project duration, utilization of the vessels and the waiting on weather duration. In order to assess the effect of using different supply chain configurations on the key performance indicators, 6 different experiments were performed within a developed simulation model.

Planners can use the qualitative insights of this report as a starting point for the choice of the preferred supply chain configuration when planning an offshore wind farm installation project. This will enable planners to make educated choices in the planning phase and give these planners insight into the different factors that are important in the supply chain of offshore wind farm installation projects. From a theoretical point of view, this report contributes to the sparse literature on the supply chain and logistical decisions in the field of offshore wind farm installation. Next to these insights, the weather module used to generate synthetic weather data improves Markov models discussed in literature and provides a solution to the problem of persistence that is present in conventional Markov models.

The city of Amsterdam is facing various challenges in managing urban freight transport within its historic and densely populated inner city. With a rapidly growing population and increasing number of trips to, from, and within the city, Amsterdam is facing pressure on accessibility, road safety and quality of life. Furthermore, Amsterdam needs to renovate a significant portion of its historical quays and bridges, which will have a major impact on urban freight transport. The city’s commitment to maintaining accessibility and liveability, combined with its ambitious goals, has led to advancements in urban freight transport, with one notable initiative being waterborne transport. While many initiatives of waterborne transport showed success in pilot projects and demonstrations, large-scale or long-term implementation could give rise to unexpected side effects. Challenges also persist due to government policies and the cooperation of key stakeholders. In this research a design methodology is presented for sustainable urban waterborne freight distribution. The design methodology offers a stepwise approach for designing, evaluating, and generating implementation guidance for waterborne freight transport, enabling a thorough understanding and assessment of impacts. The design methodology is applied to the case study of het Wallengebied of Amsterdam. During these steps, several alternatives were prepared and scored for all stakeholders involved based on the criteria important to them. Possibilities to turn certain alternatives with a poor score on a criterion into a more positive score were considered to improve its potential in designing sustainable urban waterborne freight distribution.