Autonomous pod-based railway systems represent a promising innovation in freight transport, combining the flexibility of modular vehicle concepts with the efficiency of rail-based logistics. Their success, however, depends on effectively managing the assignment and relocation of carriers under dynamic and uncertain operating conditions. Static optimization approaches such as Mixed-Integer Linear Programming (MILP) can design efficient baseline schedules, but these often prove fragile when confronted with real-world uncertainties such as delays, carrier breakdowns, and structural disruptions. To address this gap, this thesis develops a hybrid decision-making framework that integrates MILP-based planning with a Discrete-Event Simulation (DES) environment, enabling dynamic re-optimization and real-time resilience analysis.

The framework operates through an event-driven feedback loop: initial assignments are optimised using MILP, executed within the DES, and re-optimised whenever disruptions occur. This coupling allows for continuous adjustment of plans to reflect the real-time state of the system. The methodology was validated through two case studies: a simplified Toy Case to verify the model logic and a large-scale Randstad network to evaluate system robustness against cascading disruptions. The analysis incorporated a range of scenarios, including probabilistic delays, deterministic breakdowns, arc removals, transport unit (TU) insertions at varying time brackets, and the impact of delivery-window flexibility.

Results demonstrate that re-optimisation is highly effective in recovering service levels after disruptions, significantly improving fulfillment rates and resource utilisation compared to static schedules. The system showed strong adaptability to sudden demand surges and carrier failures by reallocating idle resources, while also highlighting resilience thresholds in cases of severe network degradation. Time-window flexibility was found to play a dual role: it improved overall fulfillment but introduced delays, suggesting trade-offs that need to be balanced by operators and policymakers. Carrier utilisation and empty travel metrics further revealed how resilience is achieved at the cost of increased repositioning.

This study contributes both theoretically and practically. It establishes an integrated simulation–optimisation framework that advances research on disruption management in autonomous rail systems, and it provides operational insights on fleet pre-positioning, flexible service design, and the role of redundancy in network infrastructure. The findings emphasise that digital re-planning capabilities are necessary for future autonomous freight systems, while industrial and policy implications include incentivising early bookings, setting flexibility standards, and ensuring investment in redundant routing capacity. Overall, the proposed framework provides a powerful tool for designing, testing, and improving resilient autonomous pod-based rail networks, bridging the gap between strategic planning and operational execution under uncertainty.

Integrated Passenger–Freight Transport (IPFT) systems are increasingly proposed to improve efficiency, reduce emissions, and sustain service viability where conventional mobility options face pressure. Yet the conditions under which stakeholders are willing to participate remain underexplored. This study applies a mixed methods design to examine stakeholder evaluations of demand-responsive IPFT services. Two stated preference surveys were conducted in the Netherlands among passengers (N=166) and freight recipients (N=187), each combining rating and choice experiments. Comfort and delivery quality constructs derived from rating tasks were integrated into multinomial and panel mixed logit models. In parallel, five semi-structured interviews with public transport operators, logistics providers, and municipal actors were thematically analysed. Findings show convergence on spatial separation, predictable operations, and off-peak vehicle use, while divergences emerged over costs, staffing, service hours, and institutional responsibilities. The study shows that implementation requires stakeholder alignment and advances four scenario-based configurations to guide design and policy.

Global maritime trade carries over 80% of world cargo, yet vessel arrival time (VAT) prediction remains highly inaccurate. Hong Kong Port experiences average ETA-ATA deviations of 13.8 hours, causing massive congestion costs and supply chain disruptions.
Current methods rely exclusively on either static ETA reports or dynamic AIS data, missing the complete picture. This fragmented approach ignores how ships actually navigate—constantly responding to weather conditions, sea states, and their own physical capabilities.
This study develops a multi-source data fusion framework that integrates four key streams: ETA baselines, real-time AIS movements, marine weather data (wave height, wind speed, swell patterns), and vessel physical parameters (VPP). OpenFE automatic feature engineering handles complex data interactions, while six machine learning models (XGBoost, Random Forest, LightGBM, LSTM, Transformer, TabPFN) are systematically compared.
Testing on Hong Kong Port data shows TabPFN achieves optimal performance with 2.88–3.42 hour prediction errors, which means 43%–47% improvement over ETA baselines. Weather factors occupy 3 of the top 15 important features, contributing 20% of predictive power. Surprisingly, traditional machine learning consistently outperforms deep learning on this structured maritime data. These advances enable optimised berth allocation, reduced port congestion, and more reliable logistics planning, supporting the maritime industry’s digital transformation.

The electrification of the heavy-duty freight sector requires a robust charging infrastructure that balances operational needs with grid and cost constraints. This thesis develops an integrated modeling framework to simulate charging demand and optimize the placement and configuration of high power charging stations for HDEVs. The first phase involves modeling charging demand by simulating energy depletion across real world truck trips. Using detailed vehicle specifications and regulatory driving limits, State of Charge (SoC) calculations identify when and where trucks are likely to require charging. These simulated charging events reflect realistic operational behavior across Dutch and cross border freight routes. The second phase applies a Mixed-Integer Linear Programming (MILP) model to determine optimal station locations and configurations. The optimization selects candi- date stations, either generated trough regulatory rules (AFIR) or traffic clustering, and minimizes system wide costs while accounting for wait times, charger availability, and grid connection limits. Events are assigned to specific stations and time slots to ensure feasible, cost effective infrastructure deployment. Two station placements strategies are evaluated for the year 2025 and 2030: (1) AFIR based regulatory , and (2) demand driven locations based on clustering from simulated charging events. The simulated electrification rates are 0.75% for 2025 and 7.5% for 2030. The optimization model provides the number of chargers, where to place them, and how charging events are assigned. From these results, demand curves at the station are created. The findings show that demand based placement can reduce wait times and overall installation cost, while better handling growing charging needs compared to AFIR compliant layouts. Note that the AFIR layout station locations are fixed to the regulatory minimum requirements, and only the number of chargers is adjusted to meet future demand. This thesis presents a practical method for planning charging networks using real truck data. It shows how simulated charging demand can help design better infrastructure, taking into account operational limits and policy goals to support the shift to electric freight transport in Europe.

Globalization, rapid technological progress, and growing product variety expose manufacturing systems to increasingly volatile and uncertain production requirements. To remain competitive, manufacturing systems must be able to adapt to these changes. A critical step towards achieving manufacturing adaptability is the identification of the required levels and types of adaptability. However, the literature lacks structured methodologies to support this process, limiting effective implementation in practice. This paper addresses this methodological gap by developing an integrated framework that unifies flexibility- and reconfigurability-oriented research streams. First, a new theoretical perspective on manufacturing adaptability is proposed, encompassing newly defined concepts and terminology. Building on this foundation, the study develops a methodological framework to support manufacturers in systematically identifying adaptability requirements. The proposed method employs a scenario-based approach to account for both expected volatility and uncertainty of predictions. The framework is applied in a case study in the food processing industry and subsequently evaluated through a series of expert interviews. Results indicate that the framework has the potential to facilitate structured requirement formulation and provides practical guidance for decision-making at both strategic and operational levels. This work contributes both theoretically, by advancing a unified perspective on manufacturing adaptability, and practically, by offering a tool to operationalize the identification of adaptability requirements.

In response to the inefficiencies in road freight transport, this research proposes an innovative collaboration index for carriers. The framework involves a collaboration decision tree that determines four collaboration modes at the trip level, which are then aggregated into a carrier-level evaluation using heuristic local optimization. This results in a collaboration index that quantitatively assesses the potential for collaboration among carriers based on operational data, filling a gap in partner selection processes. The study reveals that participants in horizontal collaboration achieved an average cost-saving rate of 14.4%, with the top 25% reaching 22.12%. While geographical similarity moderately correlates with the index, vehicle type does not show a linear relationship.
The significance of this work lies in its unique collaboration index formulated from operational data, which not only facilitates the identification of collaboration potentials but also provides insights for collaborative partner selection strategies.

The motivation for this research is rooted in the expected increase in demand for international train trips within the EU railway network, driven by policies promoting sustainable transportation solutions. Notable initiatives include the EU’s Green Deal objectives and the commitment to achieve climate neutrality by 2050. Additionally, significant investments in rail infrastructure, such as the Trans-European Transport Network, the introduction of high-speed rail for international travel, and the reduction of barriers to promote international train travel, are contributing factors. This surge in demand requires a reliable railway network and improved management of temporary capacity reductions, particularly during maintenance operations at cross-border sections. Cross-border rail transport is essential for this strategy but faces significant challenges, including technical barriers, regulatory discrepancies, and coordination inefficiencies among infrastructure managers from different countries.

Despite its importance, academic literature on cross-border maintenance strategies is notably limited or non-existent. This thesis begins by describing the current situation of railway cross-border sections to identify the main challenges and existing coordination practices. It then proceeds to analyse these challenges through expert opinions gathered via a questionnaire. By highlighting the key issues and potential solutions, this research aims to fill the gap in the literature and provide a comprehensive framework for improving cross-border railway maintenance coordination.

This thesis also develops a digital and cooperative framework (DCF) to enhance cross-border maintenance decision support within the railway system of the European Union (EU), addressing the cross-border challenges holistically. It emphasizes the introduction of digital twin technology to meet the urgent need for infrastructure digitalization and coordination between the authorities and infrastructure managers in different countries, enabling the cooperative optimisation of the capacity of railway networks. The DCF consists of three components: the establishment of a European Railway Entity, the creation of a European Railway Forum, and the implementation of digital twin (DT) technology. The European Railway Entity aims to centralize coordination, streamline decision-making, and enforce standardized regulations across member states. The European Railway Forum focuses on fostering collaboration and knowledge sharing among stakeholders, facilitating continuous improvement in maintenance practices. The DT technology offers real-time data visualization, predictive maintenance, and advanced analytics to optimize maintenance operations and enhance infrastructure reliability, enabling an evidence-based decision-making process.

Each of the three components of the DCF is evaluated for its potential benefits and implementation challenges. The European Railway Entity can enhance the coordination and standardization of maintenance operations but may face resistance due to its hierarchical structure and potential conflicts with infrastructure managers whose national interests might be compromised for the sake of overall system performance. The European Railway Forum is cost-effective and practical, promoting voluntary data sharing and continuous improvement; however, suggestions and implementation involve complex procedures that require trust and the management of key confidential information. DT offers the greatest potential for innovation and future-readiness, but it demands a significant initial investment, robust data security measures, and the design of complex mechanisms to enable coordination between different DT platforms and protocols across countries.

The proposed DCF is discussed in two case studies. The first case study consists of a generic case of a bridge for railways between two countries focusing on the implementation and assessing potential benefits and limitations of DT as a tool of the DCF. The second case study addresses the cross-border situation in the Netherlands, proposing an implementation plan and evaluating potential advantages and disadvantages. Additionally, the Emmerich–Oberhausen maintenance project on the cross-border section between the Netherlands and Germany is analysed. This case study highlights the benefits, and challenges of implementing the proposed DCF.

In conclusion, the proposed digital and cooperative framework, integrating the European Railway Entity, the European Railway Forum, and DT technology, aims to support maintenance decisions and address the current challenges in cross-border railway maintenance. This integrated framework seeks to improve operational efficiency, increase network reliability, and support the sustainability goals of the EU, ultimately making the railway system more attractive to users.

Railways are increasingly investing in their infrastructure to achieve sustainability objectives and meet expected growth in demand for rail transport services. Since building new physical infrastructure requires large-scale investments and long lead times, many infrastructure managers (IMs) are prioritizing upgrades to their signalling systems to optimize use of their existing networks. By migrating from legacy multi-aspect to distance-to-go (DTG) signalling, IMs gain the ability to safely run trains closer together, better manage disruptions occurring in normal operations, and implement other compatible technologies that can increase capacity and enhance service.
The key advantages of DTG systems are derived from their ability to provide more precise brake and speed supervision relative to multi-aspect signalling. Existing multi-aspect signalling systems rely on lineside signals to display braking indications to trains and do not consider train braking capabilities when determining the minimum separation between trains. As a result, minimum train separation is based on the worst-case braking distance of any train that could operate on the line. In contrast, brake supervision in DTG signalling systems is performed by the train’s onboard computer which calculates a train’s braking distance based on that specific train’s current speed and braking capability. The ability to determine braking distance based on a train’s own characteristics allows more delayed braking relative to multi-aspect signalling. Moreover, migrating the brake supervision from track to train eliminates the need for lineside signalling and related restrictions on block lengths, thereby permitting layouts with shorter blocks than would otherwise be possible with multi-aspect signalling.
To further increase network capacity, IMs are also looking to implement Connected Driver Advisory Systems (C-DAS) and Automatic Train Operation (ATO) systems. These systems permit greater coordination of train paths at the operational level through the provision of timing windows to trains (referred to as a Train Path Envelope or TPE) in real time. This timing information, in turn, helps trains avoid conflicts at the operational level when recovering from small departure delays at stations.
In practice, the benefits of DTG signalling and C-DAS/ATO systems can best be realized if timetabling algorithms consider the actual capabilities of those digital technologies to operate trains closer together in regular operations. It is therefore necessary to align tactical and operational scheduling rules, and to update train planning rules to reflect the capabilities of DTG signalling and C-DAS/ATO systems. However, state-of-the-art-methods for tactical scheduling could result in suboptimal network use as they do not accurately represent operational processes occurring on the railway network, and rely on different levels of detail than those used for real-time traffic management. Furthermore, the abstraction of signalling constraints at the tactical level necessitates additional and more detailed assessments using microscopic methods to achieve a target service plan and timetable feasibility.

With the rapid development of cross-border e-commerce logistics, how to efficiently load goods into Unit Loaders (ULDs) and ensure their on-time delivery has become a key issue in logistics systems. Based on Cainiao's actual logistics challenges, this paper proposes a comprehensive loading planning scheme in two phases: the first phase solves how each item can be efficiently packed into ULDs in 3D space, and the second phase solves when each ULD can be loaded on multiple parallel workstations. This design-oriented approach fine-tunes and integrates existing optimization techniques into a cohesive pipeline to tackle these interconnected problems systematically.

To tackle the 3D Bin Packing Problem (3DBPP), two approaches, Mixed Integer Programming (MIP) and Extreme Point Heuristic (EPH), are used in this paper. The MIP model maximizes space utilization through accurate optimization and is suitable for small-scale packing scenarios, while the EPH algorithm performs well in large-scale scenarios and generates high-quality approximate solutions in a short period. Although its space utilization is slightly lower than that of MIP, its solution efficiency is well suited to real logistics operations that require fast response time.

For the Build-up Scheduling Problem (BSP), a parallel machine scheduling model is used to optimize the assembly sequence and timing of ULDs to ensure that all ULDs can be loaded within a strict time window. Experimental results show that the model performs well in optimizing workstation load balancing and avoiding delays, which can significantly improve the scheduling efficiency of the whole system.

The research results in this paper are validated to show that the proposed two-stage framework has significant application value in improving space utilization, reducing cargo delays, and optimizing workstation scheduling in real logistics scenarios of Cainiao. Future research can introduce a feedback loop between the two phases, and combine real-time data with dynamic adjustment strategies, hybrid algorithms, and other methods to further improve the adaptability and efficiency of the model.

The study aims to develop an optimization model that determines the optimal configuration of dynamic Electric Road Systems (ERS) and static charging infrastructure for heavy-duty electric trucks. By considering varying levels of ERS adoption, the model seeks to minimize total infrastructure and operational costs while maximizing demand coverage along key transport routes. The research uses a bi-level optimization model: the upper level addresses government decisions on infrastructure placement to minimize infrastructure costs, while the lower level focuses on user routing to minimize transportation expenses. The model was applied to the Netherlands as a case study, optimizing the placement of ERS and static chargers based on traffic patterns and user behavior. Key findings indicate that ERS and static chargers are complementary, with ERS proving more cost-effective on high-traffic routes, reducing battery size and eliminating charging downtime. In low-traffic areas, static chargers provide essential infrastructure support. The model demonstrated that an integrated charging network could lead to cost savings of up to 25%. The study concludes that a combined ERS-static charging infrastructure is the a cost-efficient approach for electrifying freight transport, offering both economic and environmental benefits .

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.