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.

The road freight transport market accounts for 40 \% of the carbon dioxide emissions of the transport sector globally. To reduce these emissions, battery electric trucks (BETs) have the greatest potential as they are zero-emission vehicles. However, compared to the private electric vehicle market, this market faces significant implementation barriers, as seen from the low BET sales. This is mainly due to the optimized market and low profit margins limiting freight carriers from investing in BETs. Furthermore, BETs face serious operational challenges due to long charging times and low operational flexibility. The way to charge the BET plays a big role in finding solutions to these challenges. In recent years, new technologies such as Electric Road Systems (ERS) and battery swapping have been developed next to depot charging and on-road charging. This study gains insight into the preferences of different freight carriers for charging alternatives, using a scenario-based multi-criteria decision analysis(MCDA). Four charging alternatives: depot charging, on-road charging, ERS, and battery swapping, are compared from a freight carrier's perspective on ten criteria identified through literature review, expert interviews, and a survey. The criteria are: Investment costs, operational costs, lifetime of the battery, charging time, operational flexibility, payload capacity, emission reduction, pioneering, complexity of implementation, and strategic policy alignment. To compare the charging alternatives, the criteria weights were obtained through the best-worst method and the analytic hierarchy process, and the performance of the alternatives on the criteria was calculated for daily distance use cases. These performances and weights were combined to arrive at a final ranking of alternatives. This was done for different freight carrier groups based on daily distance, sectors, company sizes, and countries of operation. The results show that financial and operational criteria were considered most important by all the segments of freight carriers. Furthermore, depot charging appeared to be the preferred alternative in most cases, and battery swapping scored best for larger daily distances. However, in almost all freight carrier groups, depot charging, ERS, and battery swapping had similar scores. This result advocates for stakeholders to consider investing in the charging alternatives to give the freight carrier the possibility to choose the charging alternative that is best for them. On-road charging had the lowest score for all the freight carrier groups. This result mismatches with the current investments in the charging alternative and showcases the need to reconsider these investments. Due to the number of criteria and the difficulties in obtaining many responses from freight carriers, the criteria performance and weights can be studied in more detail in future research. Also, using better weighing and evaluation methods and improved research on the four alternatives in a similar scenario can improve the quality of the results in future studies. Also, looking at the problem from different perspectives in future studies can provide great insight into which charging alternatives will shape the future of BETs.

Hyperconnectivity (HC) is an emerging concept in logistics that allows for large-scale collaboration of logistics services through shared assets, information exchange, standardised protocols, and flow alignment. A hyperconnected last mile delivery (LMD) system could lead to more optimised routing decisions through processes such as consolidation with fewer vehicle kilometres and more sustainable operations as a result. However, little is known about the combination of connected services that are deemed promising among stakeholders. The Q-methodology was applied to reveal the dominant perspectives among stakeholders on the implementation of HC characteristics in LMD services. Subsequently, the results were fed back to participants to validate whether the analysis was performed accurately. This paper concludes that there is a consensus among stakeholders that the current LMD system is too segmented and that services such as shared parcel lockers and white label concepts could help consolidate delivery operations. Important barriers identified were the fear of losing a competitive market position by sharing information, as well as economic concerns.

The European Union’s (EU’s) Critical Raw Materials Act (CRMA) aims to strengthen the EU’s resource resilience by increasing the autonomy of Critical Raw Material (CRM) supply through EU-based extraction, processing, and recycling, thereby reducing external dependencies and promoting a circular economy. The CRMA mandates, among others, that at least 25% of CRMs come from EU-based recycling. Copper, a key CRM, faces growing demand, and with mining alone, future supply will not fulfill demand. This research analyzed the copper supply chain before and after the CRMA. It was found that recycling is the most promising solution for adherence to the CRMA and a sustainable and resilient future. The study develops a Mixed Integer Linear Programming (MILP) model to optimize the European copper recycling network under the CRMA recycling requirement. The model minimizes total costs while determining the optimal number, locations, and capacities of recycling facilities. The model is tested through a case study of a natural resource company and is usable to similar firms. Results identify four optimal facility locations: Stuttgart, Geithain, Bologna, and Barcelona, which were selected based on, among others, geographic centrality and supply and demand quantities. Sensitivity and scenario analyses reveal the network is vulnerable to facility outages, supply shortages, and significant demand increases. Decentralizing the network by adding a fifth facility improves resilience with limited additional cost.

This study focuses on optimizing last-mile delivery in e-commerce by balancing cost efficiency and customer preferences, particularly for mixed perishable and non-perishable goods networks. As online grocery shopping grows, ensuring the timely and efficient delivery of perishable products while maintaining quality remains a critical challenge. The model evaluates home delivery, attended pickup points, and lockers, revealing that cost-driven strategies shift deliveries toward self-pickup, with perishable items primarily assigned to attended pickup points due to temperature control. The findings provide insights for improving delivery network design, enhancing service quality, and optimizing the distribution of both perishable and non-perishable products.

The aviation industry is poised for substantial expansion, with forecasts predicting that passenger demand will double by the mid-2030s. This increase in demand presents both opportunities and challenges for various stakeholders within the industry. One sector anticipated to encounter difficulties is Maintenance, Repair, and Overhaul (MRO), particularly regarding data sharing within the supply chain for part tracking. Effective part tracking is essential as it entails crucial data that confirms the 'airworthiness' of parts, demonstrating their safety for operation. Currently, the absence of comprehensive inter-organizational maintenance record software impedes accurate tracking of aircraft parts. This lack leads to increased maintenance and communication costs for all stakeholders involved, as each party must independently verify parts history, often resulting in redundant work and additional pressure on profit margins.

With profit margins already under strain, aviation companies must innovate. One promising solution is blockchain technology, which has the potential to serve as an inter-organizational maintenance record system, facilitating the tracking and tracing of part histories and offering significant cost reductions. However, effective lifecycle tracking of parts requires a collective effort since multiple entities are involved at different stages of the lifecycle, making the formation of a data-sharing consortium necessary. The creation of such a consortium is further incentivized by the implementation of the Corporate Sustainability Reporting Directive (CSRD), which requires companies to disclose all activities related to both downstream and upstream value chains and assess their sustainability impacts. Despite the potential for cost reduction, increased efficiency in inventory management, and legal mandates, companies remain hesitant to join a data-sharing blockchain consortium. Research into similar industry data-sharing consortia indicates that one persistent barrier to success is the design of governance mechanisms within the consortium. Although extensive research on governance mechanisms exists, stakeholder participation in these data-sharing blockchain consortia remains low.

The objective of this research is to develop governance mechanisms that enhance stakeholder participation by taking into account stakeholder values. To achieve this, a value-focused thinking methodology is utilized, ensuring that stakeholder values are integral to the governance design process rather than relying on arbitrary arrangements. In addition to conducting a literature review to discover values, contact was established with the Independent Data Consortium for Aviation (IDCA). IDCA is a global consortium of leading aviation companies at all levels of the industry coming together to develop the foundation for allowing data to be shared in a non-competitive manner. Its objective is to create a more efficient marketplace where both waste and the time required to get to a common solution are minimized. A combination of participatory observations within the part-tracking work group of IDCA and interviews with high-level executives from the aviation industry is conducted to discover stakeholder values. Three contexts, 21 values and 11 sub-values, were identified for joining a data-sharing blockchain consortium. The three contexts are the data that will be shared and received, the stakeholders that will join the consortium, and the blockchain platform that will be used.

Adopting such a broad perspective on values has necessitated the prioritization of these values to create a clear roadmap for the governance design process. This approach ensures that the final governance designs are not only comprehensive but also practical, enabling effective and sustainable blockchain implementation for part tracking in the aviation industry. To obtain this ranking, a survey employing the Bayesian Best-Worst Method (BWM) was used to systematically analyse stakeholder values where 12 aviation stakeholders pertaining to the eight different stakeholder groups encompassing the full life-cycle of a part were received.

Survey takers ranked the data context as the most important, underscoring the value companies place on data as their primary resource, which was followed by stakeholder context. On the other hand, the blockchain platform was ranked the lowest in importance, which was also justified by the credal ranking calculations indicating it is seen merely as a tool for data sharing. Within the data context, the values of integrity, access control, confidentiality, and ownership emerged not only as the highest priorities but also as the most important values overall. This underscores the critical importance stakeholders place on data. These were followed by the values of trust, legitimacy, neutrality, compliance, and benefit equality within the stakeholder context. Completing the top ten, security within the blockchain context was followed by adaptability and tangibility.

Once these stakeholder values are identified, governance designs can be proposed to ensure their fulfilment. A benchmarking study was conducted on existing governance research to understand its definition, scope, and blockchain data-sharing initiatives in other industries. The literature review revealed that no unified definition or established mechanisms for blockchain governance currently exist. Therefore, the researcher established the following definition: "Consortium blockchain governance refers to the process and mechanisms that ensure the direction, control, and coordination of a blockchain platform to which stakeholders jointly contribute."

Given that consortium blockchain governance is a broad term, it is essential to keep its definition straightforward while supplementing it with specific mechanisms. To supplement this definition, a comprehensive table was developed that combines all previously identified mechanisms.Finally, five real-world blockchain consortia were investigated to understand how these mechanisms are designed and implemented in projects that have undergone piloting or advanced further.

The research was finalized by linking the eight most important stakeholders' values to governance mechanisms and, subsequently, governance designs. One example is Executive neutrality, which concerns the impartiality of the entity controlling a platform's direction. Stakeholders also, during interviews, have stated from past experiences that the executive body might not remain unbiased, leading to decisions that favour specific parties. Several governance mechanisms can ensure executive neutrality, including legal compliance, where decisions are based on industry laws and standards. However, due to the underdevelopment of data-sharing policies, industry standards, policies cannot yet serve as reliable benchmarks. Another approach is relying on ethical responsibilities, assuming all participants will adhere to principles of the greater good, though this might be overly optimistic in a profit-driven ecosystem. Since this value primarily revolves around the decisions made by the governing body, decision rights are identified as the most suitable governance mechanism and governance designs can be established.

Based on similar projects like VeChainThor and PharmaLedger, four governance designs were considered: Ecosystem members-led, Service Provider-led, Foundation-led, Independently Governed, and Foundation-led Ecosystem Governed. It was decided, based on the stakeholder value, that the foundation-led ecosystem-governed model was the most suitable, where a non-profit foundation directs the network's operations while being accountable to a board comprising ecosystem stakeholders. This model ensures stakeholder involvement and collective decision-making, maintaining executive neutrality. Finally, it is acknowledged that design inherently involves trade-offs. Therefore, selecting a particular design to address one stakeholder’s value can have ramifications on other values. Because of that, the design decision on other values is investigated. One potential downside is its impact on growth, as involving more stakeholders in decision-making can be challenging and may lead to resistance to adding new entities, potentially hindering growth. This method was employed for the other seven most important values that were identified through the BWM. However, it is important to note that this recommendation is based on the values identified through stakeholder interviews. If stakeholders prefer not to participate actively in the execution of governance duties, then a foundation-led, independently governed model would be more suitable. In this model, an independent non-profit entity would take on the responsibility of directing and managing the network, creating technology roadmaps, and providing necessary services, with limited direct involvement from the stakeholders.


In conclusion, to address the primary research objective of designing governance mechanisms that enhance participation in blockchain-based aviation part-tracking consortia, it can be stated that \textbf{there is no universally optimal governance mechanism design that enhances stakeholder participation; rather, this varies according to the values held by stakeholders within their specific context. As these values change depending on the context, it is recommended that decision-makers begin by investigating stakeholder values.} A value-focused thinking approach constitutes the primary contribution of this research, as it introduces a novel method of blockchain governance that prioritizes stakeholder values. By integrating these values into governance designs, a mutual understanding among consortium members is established, promoting more effective collaboration and decision-making. Furthermore, this research addresses a literature gap within the aviation industry, where blockchain consortiums have not been extensively examined.


For the initial phase of implementation, it is advisable to engage regulators to establish credibility, thereby fostering increased participation. Once a stable level of participation is achieved, the focus can then shift to examining the values of the stakeholders involved. Future research could assess the applicability of identified values and governance mechanisms in contexts beyond the aviation industry. Similar studies across various industries are important to ensure external validity. Additionally, further research could be conducted into how different mechanisms besides the one suggested in this research could fulfil stakeholder values and investigate the interaction among those mechanisms.

The increasing volume of global freight trade, coupled with economic growth, necessitates ongoing innovation in optimizing freight operations. Over the past decade, the concept of synchromodality has been explored to encourage a modal shift from unimodal to multimodal transport. Synchromodality, with its flexibility feature, can create more resilient freight transport systems. Various models employing different techniques have been proposed to establish a resilient synchromodal framework capable of reacting to disruptions. However, there are only few studies addressing the unknown duration of disruptions. This research proposes a learning-based modular framework comprising to capture the dynamics of disruptions in multimodal transport and learn to make more effective decisions, thus addressing the challenge of limited prior knowledge about disruptions and enabling fast responses to disruptions.

Dry ports have emerged as a promising solution to hinterland transport challenges. Existing literature implies an area of study on the topic of dry port that considers dry port stakeholders’ perspectives to construct a maturity model that could help dry port develop. This study has tried to contribute to this subject, starting with considering arguably two of the most relevant dry port stakeholders: the customer and the dry port operator. A series of performance logistic attributes has been identified to represent dry port customers’ perspectives, while a series of innovation factors is used to represent the dry port operator’s perspective. Utilizing the creative approach of MAMCA-Swing to analyze the services offered by a dry port using each of the stakeholders’ perspectives as criteria, the study has established a link between this analysis result and the creation of a dry port maturity model. A case study based on Indonesian dry ports has also been conducted to illustrate the use of such a framework, resulting in a dry port maturity model for Indonesia that is arguably relevant and useful in helping Indonesian dry ports develop.

This research presents a mathematical model for routing that incorporates multiple depots, occasional drivers, and multiple depot visits, a problem faced by many companies in reality. An Adaptive Large Neighborhood Search (ALNS) algorithm was employed, using Random and Worst removal operators, Basic Greedy and Regret-2 insertion strategies, a roulette wheel for operator selection, and simulated annealing for acceptance criteria. Computational experiments validated the effectiveness of the ALNS algorithm and model.Sensitivity analysis revealed that adding depots (ODs) can reduce routing costs by up to 15.21%, with various OD capacities offering additional savings (7.86% for 60% capacity and 9.97% for 70% capacity). A case study with the Dutch e-commerce company Ochama, scaled to 150 requests, confirmed practical applicabil- ity, achieving cost reductions of 11.37% for small vehicles and 6.85% for medium-sized vehicles. The results highlight that integrating ODs into routing strategies can significantly lower costs, with optimal outcomes dependent on market research to balance savings, service quality, and OD capacity.

The transport sector is the Netherlands’ second highest emitting sector, with 19% of the total energy demand. Where other sectors are becoming more sustainable, the transport sector is one of the most difficult to decarbonise despite the many studies in this field. This research aims to gather a list of potential measures and combine them to reach an 80% reduction of emissions in 2040 for the hinterland freight corridor of Rotterdam - Venlo with a high rail freight intensity. A list of potential measures was established with literature studies and expert interviews. With a normative scenario design approach, an iterative process resulted in a list of 8 potential measures with a set of targets for these measures. After computation with the DeCaMod model a reduction of 80% is achieved on the corridor. The results showed that for this corridor, the reduction depends on the availability and adaptability of biodiesels and the electrification of vehicles and vessels. In contrast, measures aiming to improve logistics efficiency do reduce the total energy required but are less effective in reducing carbon dioxide emissions.

Port systems face numerous challenges, including limited storage capacity at terminal yards, congestion in road port access due to overwhelmed infrastructure, and inefficient inter-terminal transportation. Close dry ports, situated between 10-40 km from the port, offer a potential solution, but lack flexible and reliable transport connections that efficiently handle transportation without adding extra handling moves. Recent technological advances in rail systems, particularly fully automated magnetic levitation (maglev) cargo shuttles, provide promising solutions for connecting dry ports with seaport terminals.

This research explores the integration of maglev technology into port logistics, focusing on connecting a dry port terminal to seaport terminals, with a direct connection to the berth. Using the Transport System Bögl (TSB) Cargo system as a reference, five different designs were developed for integration. These designs required a redesign of the dry port and terminal yard, as well as the design of the system’s berth connection and connections between all terminals.

Simulation modeling using Siemens Tecnomatix Plant Simulation software evaluated the performance of these designs under various scenarios based on Port of Hamburg demand input data. Results showed that all designs improved median berth times compared to the German benchmark of 18.96 hours, with Design 5 demonstrating the best performance together with Design 2.

Recommendations include continued collaboration with port authorities through simulation case studies, serving as proof of performance for potential integration projects, and exploration of collaboration opportunities with smaller scale ports facing space availability and road access issues. Further research should expand the model to accommodate new export container loading requirements and include all terminals, while studying new algorithms to balance the volumes and requests of all three terminals. This research demonstrates the feasibility of integrating maglev systems into port logistics, challenging the ’status quo’ and opening up new possibilities for improving port operations. Through a systematic approach, this study offers valuable insights for the integration of maglev technology into port logistics, paving the way for future advancements in the field.

Local governments hardly have a clear overview of their current progress in reducing emissions. This could be explained by the inadequacy of available tools. To address this issue, this research used a design cycle to explore the development of a feasible and useful method for allocating emissions in the road freight transport subsector. Four methods, two existing and two proposed, were evaluated against criteria derived from a literature review. A synthetic case study of Delft was conducted to evaluate these four methods and an indirect approach for relative accuracy, adaptability and feasibility. One of the proposed methods showed promise, but further research is needed to evaluate its absolute uncertainty. This proposed method is recommended to pursue, up until the moment the international standard only includes one method per subsector. Additionally, the proposed indirect allocation approach may offer greater flexibility when standards change and data is not fully accessible.

Long truck queues and congestion around terminals is a common sight, however they come with many negative externalities for all the stakeholders involved. Truck Appointment System (TAS) is the most commonly used system to face these problems, but it still has some drawbacks and limitations. Consequently, in this research an extension of the typical TAS is proposed in order to improve its performance. The main components of this system are the use of truck dependent time-windows, the utilization of real-time information and the adaptive trucks rescheduling model. The duration of the arrival time-windows is longer than the actual service times, allowing overlap between time-windows. Thus, the actual service sequence might be different from the reserved one. To determine the actual loading sequence an Optimization model is developed, which is run periodically while utilizing real-time truck information. A chemical plant is used as a case study in this research. The performance of the proposed TAS is assessed with the use of a Simulation model. The outcomes of this research suggest that the a less strict TAS can significantly improve the system’s performance, especially trucks’ waiting times. Also, the system’s resilience against disruptions and the plant’s environmental footprint are improved, while queues are reduced.

In order to fulfill the climate commitments outlined in the 2015 Paris Climate Agreement, there is a pressing need to substantially reduce Greenhouse Gas (GHG) emissions worldwide. Among these GHG emissions, CO2 contributes for 75% of the total Greenhouse Gas emissions. The transport sector accounts for 22% of these CO2 emissions. Despite the only 2% of the overall vehicle fleet in Europe, trucks and buses contribute a substantial 28% to the annual CO2 emissions on roads. Decarbonizing the transportation sector can be approached with various solution, with electrification emerging as one viable solution. Electrification involves converting diesel trucks with internal combustion engines into battery trucks. Furthermore, extending this shift to heavy duty trucks can be achieved by implementing an Electric Road System (ERS), utilizing either inductive or conductive charging. Completing an European network for ERS would be optimal, considering the daily cross-border transportation undertaken by heavy-duty trucks. Given the challenges in constructing a full European network at once, it is recommended to start an ERS infrastructure between two major freight handling points to guarantee high initial utilization. Numerous studies and pilot projects have investigated ERS technologies, with the Overhead Catenary Line system currently standing out as the most mature technology. However, existing studies primarily focus on the technical aspects or cost-effectiveness of ERS technology, neglecting a crucial aspect—the Social Cost-Benefit Analysis (SCBA) that evaluates the socio-economic impact of implementing an Overhead Catenary Line system. Moreover, these studies mostly looked into the implementation of a larger ERS network. Consequently, the main research question is formulated: What is the socio-economic feasibility of the implementation of an Electric Road System (ERS), the overhead catenary line system, between two freight handling points?
The shift towards electrification has already begun, with the introduction of battery trucks alongside diesel trucks. Hence, a transitional phase involving a mix of diesel and battery trucks is anticipated in the coming years (the zero alternative). The study has focused on testing one policy measure, namely the implementation of an ERS infrastructure between the ports of Rotterdam and Antwerp. The implementation of an ERS network, extend the truck fleet (consisting of diesel trucks and battery trucks), with a new type: the catenary trucks. This is the so called policy scenario (anticipated completion by 2030), which has been compared with the zero alternative. Three distinct route alternatives between the ports were assessed through SCBA, revealing that implementing an ERS infrastructure proves welfare enhancing for all three route alternatives, thus demonstrating the socio-economic feasibility of the policy measure. Furthermore, among the route alternatives (the Western route, the Middle route and the Eastern route), the Eastern route presents the most favorable Net Present Value outcomes for ERS infrastructure. However, conducting further studies encompassing research variables and non-monetized factors arising from ERS infrastructure implementation is necessary. This comprehensive analysis is crucial for informed decision making regarding the successful implementation of an Electric Road System.

Modern manufacturing operations rely on accurate, real time data for efficient operation and control of the production process. Many small and medium sized enterprises (SMEs) still operate with legacy assets, limiting their access to a clear data picture. This thesis research and its design solutions help SMEs gain real time insights into their manufacturing process, resulting in production efficiency gains and helping identify process improvements. These factors help manufacturing SMEs stay competitive in an ever-increasing competitive global business climate.

The primary objective was to identify the requirements for enabling real-time insights into SME manufacturing operations. Findings suggest that in-house infrastructure can facilitate the required transparency, provided it is lightweight and minimalistic. The essential factor is to transform machine-generated data into insightful metrics for relevant stakeholders. A significant aspect to consider is the human element— the adoption and commitment of employees towards the technology.

Upon analyzing the benefits, the research underscores that real-time insights lead to enhanced production efficiency. By offering an objective view into production data, SMEs can quickly identify and rectify bottlenecks. Moreover, such transparent insights improve communication within teams, empowering employees by showing the direct impact of their performance on overall production, thus boosting their morale and productivity.

The study also evaluates the key features needed to make these real-time insights actionable. Contrary to initial assumptions that a single KPI would suffice, it became evident that a more comprehensive view—incorporating metrics such as Overall Equipment Effectiveness (OEE)—was necessary. For SMEs with diverse product portfolios, additional parameters like average batch size, cycle time, and machine power-on time provide valuable context.

Lastly, the implemented design framework showcases the positive impacts of real-time insights on production. Access to objective metrics aids in performance evaluation, problem identification, and the formulation of improvement strategies. An essential feature highlighted is the predictive capability that guides resource planning, thus minimizing errors.

This research emphasizes that manufacturing SMEs with legacy assets can achieve real-time insights into their processes through cost-effective, lightweight solutions. The proposed methods and guidelines, as discussed in detail in the design section, are foundational for SMEs aiming to modernize their operations and bridge the knowledge gap in implementing real-time monitoring. By offering universal access to transparent production metrics, SMEs can optimize their processes, ensuring better efficiency and communication at all levels of operation.

To achieve the goals set by the "European Green Deal" of reducing greenhouse gas emissions, it is imperative for heavy goods vehicles to transition towards becoming climate neutral. To address this challenge, Electric Road Systems (ERS) with overhead conductive and in-road inductive technology present a promising sustainable mobility solution. However, little is known about the real-life feasibility of these technologies, particularly as in-road inductive systems are partly still under development. ERS is a system of systems, it contains several linked subsystems with complex interaction effects, which makes it difficult to predict its performance across different dimensions that are important for the many stakeholders involved.
Therefore, this study aims to evaluate, from a systemic perspective, the different impacts that the two ERS technologies will have on all stakeholder groups, thereby providing input to the decision-making process on the adoption of either technology. The methodology is an adaptation of Multi-Actor Multi-Criteria Analysis (MAMCA) and Design for Value (DfV), based on a literature review, stakeholder interviews and conceptual modelling. With the study area of the corridor between Rotterdam and Antwerp, related stakeholders are interviewed to understand their main values and criteria for evaluation. The data on ERS technologies is collected to carry out the evaluation and comparison. The results of the impacts of two ERS technologies are shared with stakeholders for validation purposes.

This study aims to investigate and develop the efficiency of a decentralised dynamic system for last-mile parcel distribution. A better understanding of the efficiency, and thus the feasibility of this decentralised logistics method, contributes to a better understanding of the potential of these systems as alternatives to the current centrally organised system. The main research question is: To what extent can the efficiency of a decentralised dynamic parcel distribution method be improved by applying different heuristics and adapting the simulation environment with congestion and a mixed fleet? To answer this question, a review is made of existing literature on last-mile logistics, self-organisation in logistics and auction methods in logistics. The current model is evaluated, benchmarked and validated. Model improvements are defined and applied in an experimental setting using a small dataset, after which the extended model is applied to a real-scale case dataset based on 729 delivery addresses in Delft. The applied modification strategies are random insertion, parcel swapping and k-means clustering. To account for the behaviour of the model under different conditions, the method was tested for a heterogeneous fleet of vans and cargo bikes and different levels of congestion. In general, the heuristics were able to improve the performance of the model, but the improvements in travel distance were minimal. When the combination of the best performing heuristics is applied to the case data, a 4.6% improvement in vehicle travel distance can be achieved compared to the original method, but still with high computation times. Comparison with an external centrally organised OR solver shows that although the decentralised method can be improved, it does not perform well, as the OR solver can find a better solution in a fraction of the time needed for the improved decentralised auction method. This study provides a proof of concept of the method and a more efficient model has been constructed. However, given the limitations of the method, further research should carefully consider whether it makes sense to continue with this particular method.

Testing Sidewalk Autonomous Delivery Robot (SADR) performance in real world conditions is important to prove whether the innovation is ready for large-scale adoption. Currently, testing SADRs in public is not allowed in the Netherlands, because little is known about the actual risks associated with delivery robots, and the risks are currently difficult to assess because the robots are continuously evolving. Since the adoption of SADRs can reduce the negative effects of last mile logistics, in this article a theoretical dynamic assessment framework for the safe and sustainable performance of delivery robots is proposed. It is explored that there exist different degrees of relative difficulty within the Operational
Design Domain of SADRs and a set of adapted metrics to objectively and completely quantify and qualify factors that cause this relative difficulty is drawn up. On this basis, the dynamic assessment framework has been developed. Both the theory of relative Operational Design Domain Difficulty and the proposed dynamic assessment framework have been validated with experts. Delivery robots can be safely tested in public, because based on the dynamic assessment framework and a living lab approach, associated risks can be constantly minimised.