Freight transport is vital for urban economic growth, yet its increasing demand exacerbates congestion and emissions. The integration of freight delivery within public transport networks presents a sustainable alternative, leveraging underutilised transit capacity. While prior studies focus on operational aspects, limited research addresses strategic hub locations within public transit networks. This study introduces a novel framework for selecting public transport stations as freight hubs using a Multi-Criteria Decision Analysis (MCDA) approach with the Best-Worst Method (BWM). The framework evaluates key factors, such as accessibility, capacity, and costs, to determine optimal hub locations. A simulation is subsequently conducted to assess real-world viability, demonstrating the trade-offs between network reach, handling efficiency, and financial constraints. The results indicate that strategic hub placement significantly impacts network scalability and efficiency. Future research can expand
upon this framework to refine urban freight delivery models tailored to specific implementation areas.

This study presents two novel strategies for identifying outlier parcels in urban deliveries, focusing on cost and environmental impact. With the growth of Business-to-Customer (B2C) e-commerce, the logistics industry faces increased pressure to improve last-mile delivery (LMD) efficiency. Outlier parcels, characterized by higher costs and emissions, pose significant challenges to logistics service providers (LSPs). Using the Marginal Cost Method and the COFRET method, this study develops cost-based and emission-based identification strategies to help LSPs address inefficiencies. The elbow point method is introduced to set objective thresholds for identifying outliers. Results from simulations in the South Holland region reveal significant differences in outlier parcel distributions across various carriers. Additionally, a sensitivity analysis assesses the impact of carbon pricing on parcel identification. The findings provide actionable insights for LSPs to optimize delivery operations and achieve sustainability goals. Future work will explore the application of these methods in different regions and consider alternative delivery methods for handling outlier parcels.

Seaports are critical to the global supply chain, and as maritime trade grows with globalization, inefficiencies in port call processes and ship waiting times are increasing. This research develops a framework for Port Call Optimization (PCO) models to support collaboration and decision-making between stakeholders within the Nautical Chain (NC) and terminals. The Design Science Research (DSR) approach provides a systematic way to design a PCO tool for a port that is backed by a strong knowledge base. Integrating this approach with the co-design process involving user input ensures that the tool aligns with client needs and trends. This study uncovers a framework to assess the prerequisites for PCO tool implementation and develops steps to build a roadmap for PCO tools that incorporate the intricacies of different port governance structures. The findings from this research lays the groundwork for future research regarding the implementation of a PCO tool by bridging the gap between the fragmented research in this domain.

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.

In this research, a step by step approach is followed to find the root cause of the lagging adoption of an automated planning system by equipment planners. This is done by exploring the Technology Acceptance Model by Davis, from which a conceptual framework is established. This framework is central to a survey conducted under equipment planners globally. The results of this survey, and follow-up interviews, allow to identify the root cause of the lagging adoption of the new system. Furthermore, these results are used to draft a road map to improve the adoption rate.

This thesis investigates a container drayage problem involving terminals, depots, and shippers. Each terminal operates a homogeneous fleet of trucks that start and end at their respective terminals, carrying either one 40ft or two 20ft containers, and can make multiple trips within a single planning horizon. Terminals handle full containers and maintain a limited stock of empty containers, while depots provide additional empty containers. Shippers have specific time windows for service, and requests include sending and receiving empty or full containers to designated terminals, with street turns of empty containers possible. An Adaptive Large Neighborhood Search (ALNS) algorithm is implemented to address this problem, outperforming traditional methods like CPLEX. Extensive computational experiments validate the ALNS algorithm's efficacy, highlighting the logistical benefits of optimal depot placement and the impact of street turns, emphasizing the practical implications for container logistics.

In their Distribution Centre (DC) in Maasdam, FrieslandCampina (FC) uses a four-crew shift schedule to prepare all necessary orders for their clients, 24 hours of each Monday to Saturday. Their large automated warehouse is home to 10 000 pallet places, containing fresh dairy products. From here, orders are either prepared as full pallets, machine-picked layers or hand-picked "colli". In the last department especially, personnel cost is high relative to the throughput. Definitive picking deadlines are often ambiguous, posing challenges in job and personnel scheduling. The study goal is twofold. Firstly, to find out whether full knowledge of picking deadlines can contribute to a more efficient job, and so, shift schedule. Secondly, to offer insight for a trade-off between shift types to absorb workload. To reach this study goal, a Shift Minimisation Personnel Task Scheduling Problem (Krishnamoorthy et. al., 2012) and a Bin Packing Problem (Paquay et. al., 2014) were combined and tailored to fit the scheduling problem at FC's DC. In three weekly scenarios, the MILP model scheduled picking jobs in the least expensive shifts through a cost minimisation function. Two model configurations were used, one to prefer the shift between 09:00 and 17:00 (flex), and one to prefer either one of the 06:00-14:00 (morning) or the 14:00-22:00 (afternoon) shifts. Both model configurations inherently avoided the most expensive 22:00-06:00 (night) shift. Main findings include the possibility to absorb workload using the morning and afternoon shift and to avoid the night shift. Additionally, it was confirmed that insight in picking deadlines can contribute to an efficient personnel schedule a great deal.

This research aims to enhance the understanding and quantification of logistics in the Netherlands by identifying factors influencing freight activities in urban areas. Using a linear regression model developed from BasGoed freight tour data, CBS location data, and Dutch zonal plan data, the study integrates 26 variables to predict freight trip generation and freight generation. The main explaining factors include the surface area of distribution centers, employment in various sectors, the presence of rail or inland waterway terminals, urban density, and the surface area of business spaces. The model, validated through statistical tests and literature comparisons, provides reliable predictions, with employment influencing freight production and surface area affecting freight attraction.

A case study in the Utrecht A12 zone demonstrated the practical relevance of the model, offering insights for policymakers to design logistics-friendly urban environments. Limitations include simplified statistical procedures and reduced accuracy in seaport and transhipment areas. Despite these constraints, the model delivers valuable predictions for various spatial scenarios.

Future research should explore model performance using CBS XML-microdata, consider non-linear relationships, and examine variables for shipment size and vehicle type. Addressing causality and endogeneity with instrumental variables is also recommended. This study contributes significantly to both scientific and societal domains, providing a robust model for freight trip generation and freight generation, and guiding municipalities in effective spatial planning and decision-making for sustainable urban development.

The installation rate of offshore wind energy has to be quadrupled by 2030 to meet the green climate ambitions of European countries, but this growth is hindered by logistical challenges. Therefore, this study explores how two transportation and installation strategies, shuttling and feedering, affect the installation rate with the inclusion of manufacturing ports. Shuttling is where the installation vessel collects components itself at a port, and feedering is where the installation vessel remains offshore and gets supplied directly via feeder vessels. This is a novel approach as feedering and manufacturing ports are often not considered and production rates at manufacturing ports have not been considered at all. A rolling horizon simulation model is developed, which uses a Markov simulation model for weather forecasting, with a 72.92% forecast accuracy over two weeks, and a greedy algorithm for transportation and installation optimization. Results indicate that accurate initial buffer calculations, depending on the production rate at the manufacturing ports and project-dependent characteristics, can increase the installation rate significantly for either strategy. Shuttling becomes more efficient than feedering if the distance between the manufacturing ports and the offshore wind farm is too large or the size of the feeder vessels is too small. Feedering is more efficient in all other circumstances and, on average, results in a 9.2% higher installation rate and reduces project duration by 29 days compared to shuttling.

This research explores the integration of electric vehicles (EVs) into the logistics of bulk-liquid transportation, specifically within Heineken Netherlands’ operations, using a two-echelon network. This study is pivotal as it aligns with global moves towards zero-emission regulations and sustainability in logistics. The primary question it addresses is optimizing a logistic network and truck operations for bulk liquid delivery by transitioning to EVs within a two-echelon framework, ensuring efficiency while adhering to sustainability and regulatory demands.
The investigation employs a sequential exploratory strategy, beginning with qualitative analysis to identify core challenges and opportunities, followed by quantitative methods to refine network design and decision-making. Advanced clustering techniques such as the center of gravity, p-median, and k-means are utilized to determine optimal depot locations, essential for overcoming the operational range and charging limits of EVs. This approach aids in developing a logistics network that is both operationally efficient and environmentally sustainable.
A significant portion of the study focuses on vehicle routing within the two-echelon location-routing model. It considers critical factors like the limited range of EVs, multi-compartment transport requirements for bulk liquids, and specific customer delivery windows. The model integrates these elements to optimize vehicle routes for efficiency and regulatory compliance, illustrating its practical use through the two-echelon multi-compartment electric vehicle routing problem with time windows (2E-MCEVRPTW).
The practical application of this research is demonstrated in a case study of Heineken Netherlands, highlighting the logistical complexities of transitioning to an EV fleet for beer distribution. The study examines operational challenges such as vehicle range and product diversity management, proving the viability and effectiveness of the proposed models.
Results discussion reveals that strategic network design using the center of gravity method significantly enhances kilometer savings and operational efficiencies. However, the benefits diminish with additional hubs, indicating an optimal hub number exists. While transshipment costs pose a significant challenge, outweighing the kilometer savings, potential cost reductions through increased reefer capacity and reduced transshipment times are identified, pointing to possible areas for improvement.
The study concludes that the two-step optimization process, integrating network design and vehicle routing, effectively addresses the research question. It not only shows the potential of EVs in transforming logistics but also underscores the economic and operational challenges of adopting a two-echelon network. The findings lay a groundwork for future innovations in sustainable logistics, though they caution the need for tailored solutions across different operational contexts and suggest further research into computational strategies and customer clustering for enhanced route optimization.

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 .

As societies worldwide grapple with the urgent need to mitigate carbon emissions, one domain where substantial strides can be made is heavy-duty road freight transport. Electrification has emerged as a practical and technologically mature solution to address this challenge. However, the widespread adoption of electrification in this sector is met with formidable obstacles. These obstacles encompass the inadequacy of charging infrastructure, restrictions in driving range on a single charge, the demand for robust and high-capacity onboard batteries, and the looming specter of potential battery shortages.

Within the European context, discussions revolve around sustainable solutions that can pave the way for a cleaner and greener future. Among the contenders in this realm, the electric road system (ERS) has risen to prominence. ERS introduces a groundbreaking concept where trucks can recharge their batteries while in motion on highways, promising an array of ecological and economic benefits. However, the journey toward the implementation of ERS infrastructure is not without its intricacies. It necessitates the installation of specialized charging infrastructure, which can take the form of overhead catenaries accessed by a pantograph or embedded road equipment. Moreover, there is a substantial financial commitment required to equip entire truck fleets with the necessary batteries, adding to the complexity of the endeavor.

The central challenge in this landscape revolves around the meticulous design of an optimal ERS network that adeptly balances infrastructure costs with battery expenses. This research aims to address this multifaceted challenge by posing a fundamental question: How to determine the optimal ERS network, given the trade-off between infrastructure and battery costs?

To tackle this question head-on, this paper introduces a sophisticated multi-objective optimization model. This model is a computational framework that concurrently minimizes the costs associated with infrastructure investment, encompassing the installation and maintenance of ERS components, and the total transport expenses. These total transport costs encompass a range of factors, including the procurement of batteries, energy consumption, and toll charges. This comprehensive approach takes into account the diverse perspectives and interests of both investors and logistics companies, providing a holistic view of the intricate challenges associated with ERS adoption.

One pivotal advantage of ERS becomes evident in its capacity to extend the lifespan of batteries by reducing wear and tear during typical driving conditions. The model thoughtfully incorporates this aspect, factoring in battery purchase costs that hinge on projected lifespans. These projected lifespans, in turn, are influenced by the chosen route's electrification rate (ERS implementation).

To validate the model's effectiveness and practicality, it is subjected to a rigorous real-world case study. This case study delves into the intricacies of road freight transport in Germany, the Netherlands, Belgium, and Luxembourg. Additionally, this research introduces an enhanced Genetic algorithm, complemented by an Elitism strategy. These enhancements are designed to optimize solutions effectively within the confines of this practical context.

The findings derived from this rigorous analysis reveal a diverse Pareto set. This set showcases the delicate equilibrium between infrastructure investment and total annual transport costs. Notably, when budget constraints are absent, investing in ERS consistently proves advantageous. The total reductions in transport costs demonstrably surpass the initial ERS investment. For instance, the comprehensive electrification of 27,114 kilometers of highway results in a remarkable 30% reduction in total transport costs...

This master thesis explores how ports, such as Hamburg, are adapting their logistics to enhance supply chain efficiency, with a specific focus on the evolving global first-mile fresh fruit reefer (GFFR) cold chain. It discusses the use of value stream mapping (VSM) from an inside-out perspective to analyze operations, challenges, and opportunities for improvement. Through VSM and opportunity analysis, it identifies key issues and opportunities, stressing the need to improve customs scanning and establish a food cluster network at the Port of Hamburg. In summary, this study underscores the significance of effectively managing GFFR cold chain relationships, with proposed improvements addressing weaknesses and capitalizing on opportunities for competitiveness.

Demand forecasting plays a critical role in organizational planning, encompassing inventory management, capacity allocation, and financial decision-making. However, achieving accurate forecasts can be challenging, particularly in industries characterized by high demand volatility, such as semiconductor assembly equipment manufacturing, exemplified by Besi. Leveraging machine learning (ML) techniques presents a promising solution for effectively forecasting the seasonal and cyclical demand fluctuations experienced by Besi.

Besi, full name BE Semiconductor Industries N.V., is an multinational semiconductor equipment manufacturer which originates from The Netherlands. The company was founded in 1995 by Richard Blickman and now has operations in, among other countries, China, Switzerland and Malaysia. Besi develops leading edge assembly processes and equipment for leadframe, substrate and wafer level packaging applications in a wide range of end-user markets including electronics, mobile internet, cloud server, computing, automotive, industrial, LED and solar energy.
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This paper investigates the challenges organizations face when implementing ML models into their demand forecasting processes, aiming to design a framework and implementation plan to guide organizations in adopting ML techniques. The research methodology employed is design science research (DSR), which focuses on developing and validating new designs within existing systems. The paper follows the iterative steps of DSR, including problem identification and motivation, objective definition, design and development, demonstration, evaluation, and communication. This iterative process facilitates collaboration with literature and industry experts to design a practical solution.

The study draws on literature research and exploratory discussions with Besi employees, emphasizing five key areas for investigation: the current forecasting process, existing forecasting techniques, organizational requirements, limitations, and input-output considerations. The findings highlight that Besi, similar to other organizations, employs a multi-layered forecasting process, with the most effective layer for implementing improvements and ML models being the initial forecast. Additionally, Besi predominantly relies on judgment-based forecasting techniques, making the implementation of a neutral ML tool necessary to create a hybrid forecasting system that mitigates human bias. Besi possesses the necessary prerequisites for effective ML techniques, such as clean and abundant data, but lacks the expertise required to construct and implement accurate models. Furthermore, Besi desires a neutral model that counters human bias and inputs historical monthly sales data, with the output expressed as total monthly sales.

To facilitate successful implementation within Besi's forecasting chain, several aspects are explored: the process framework (including integration, monitoring, updating, forecasting, and communication), peripheral considerations (e.g., legal and end-user trust), and the dashboard. The process framework is designed based on the existing forecasting process at Besi, incorporating the ML model, a dashboard, and revised information flows. The steps align with literature recommendations for ML-based forecasting, indicating that initial implementation is expected to face minimal resistance, monitoring is an ongoing task for the forecaster, updating involves improving the model and frequent training with new data, forecasting remains with the same personnel but incorporates additional information sources, and communication remains unchanged.

Peripheral matters, such as regulations and end-user trust, are limited in their impact, with research indicating that no laws impede ML model implementation, while a dashboard can enhance trust among direct users. Gradual implementation in phases, where the model does not hold authoritative power, facilitates organizational acceptance.

Based on insights from all sources, a step-wise plan for ML model implementation is proposed. The initial phase involves assembling the necessary infrastructure, data, and stakeholders, followed by creating and implementing a minimum viable product as a confirmation tool alongside the existing forecasting process. The minimal viable product is a functional model that provides usable accuracy and lists expected monthly sales based solely on historical data and observed fluctuations.

The second and final step focuses on refining the model and presenting its findings through a dashboard, incorporating information from other relevant sources to support the forecaster in making informed forecasts. This phase also enables improved communication with other relevant departments. Ultimately, the forecast incorporates real-time sales data for increased accuracy.

Feedback from Besi representatives indicates that this implementation approach is suitable for their business. The framework and steps presented in this study have been generalized to benefit other organizations, making an academic contribution in the field of ML-based demand forecasting. This contribution stems from the building upon the theory by Caniato in implementing a quantitative forecasting method. The new findings show that there are multiple ways to implement a quantitative method and that, according to this paper, the implementation is best cut up into two phases for smooth transition and maximum acceptance.

The transport sector is one of the most significant contributors to European GHG emissions and is expected to grow even further in the coming years. The target of the European Union to reduce transport-related GHG emissions by 55% by 2030 seems to be a challenging task, keeping in mind the projected growth rates of the sector. In order to achieve the set-out targets, transparent and standardised GHG emissions quantification methodologies and reporting schemes must exist to monitor the GHG emissions of vehicles and reduce them where possible. Vehicle emissions used to be approximated with standard emission factors per vehicle class. However, the accuracy of these traditional emission factors is limited, especially on a larger scale, such as the European Union, leading to inaccurate vehicle emissions. Thus, primary fuel consumption data could be used to calculate the emissions of vehicles accurately by collecting the primary data via onboard sensors. The combination of data management structures for primary data in road logistics with the possibility of improving the accuracy of emissions calculations and reporting from a socio-technical perspective is currently not sufficiently addressed in scientific literature. The study is also highly relevant from a private interest, as new legislative decisions force private companies to declare their emissions on only validated and correct emissions factors.

The research aimed to investigate the current practices of using vehicles’ primary data to improve GHG emissions’ accuracy by creating a system architecture for the data flow from the vehicle to the final visualisation of the GHG emissions. For that, the design science research methodology (DSRM) approach was chosen to answer the main research question of:

”What onboard sensor systems architecture can enable road logistics operators to gather primary data from their fleet to accurately determine their vehicle emissions?”

Four sub-questions were formulated in line with the used DSRM design cycle to answer the main research question and to contribute to the existing body of knowledge via a socio-technical analysis, system requirements, system architecture, and an evaluation.

The research utilised interviews, scientific literature, and informal conversations with industry players as the main knowledge source. The expert interviews were first used during the design process to understand the environment, derive system requirements for the later design, and to create a stakeholder overview. In the second phase of the research, the experts were utilised to evaluate the created design. Interview partners were selected based on their role in the system and potential expertise to help steer the design and evaluation. The feedback received was directly implemented in the designs.
The first step of the analysis was the socio-technical analysis. It revealed the first requirements and design principles for the later design phase, based on the institutional setting and stakeholder demands derived from interviews and the available literature. It also showcased the active and influencing role of the EU in the system, which underlined the need for a socio-technical analysis. Lastly, the stakeholder overview visualised the transport sector’s highly fragmented and multi-stakeholder domain, which industry experts evaluated and approved.

The complete system requirements were established and finalised in the second phase of the research. They were separated into three clusters: institutional, stakeholder, and technical-related requirements and were further categorised into functional and non-functional system requirements. Design principles were also created to steer the design process. Six functional and ten non-function system requirements were derived and four design principles. The requirements were evaluated and approved by the expert interviews and were used as the main input for the design phase. The main conclusion was the stakeholder-specific characteristic of some of the requirements due to the different needs of logistical actors and related IT companies that calculated GHG emissions.

The third phase was the designing of the system architecture. It was separated into two parts. First, the creation of a list of possible design options to address the derived system requirements with another evaluation round with the expert. Second, the creation of the system architecture by creating system architecture components, which incorporate the most fundamental design options from the design phase. The experts again evaluated these system architecture components before they were incorporated into stakeholder-specific system architecture, which captured the overarching processes and data flows of an IT company that specialised in the quantification of GHG emissions of vehicles in road logistics. The main conclusion was that, due to the diversity of system stakeholders, a general system architecture that adresses all stakeholder needs is less feasible than the creation of stakeholder-specific system architectures.

The fourth phase was the evaluation, which happened throughout all stages of this research, and concluded the general correctness of the derived stakeholder-specific system architecture by the experts. It also pointed out potential limitations of the designs. The specific knowledge needed to validate such designs of the technical domain (data management structures), the policy and institutional knowledge, and the specific details of state-of-the-art GHG quantification methodologies makes evaluating the entire system more challenging. Thus, a broad sample of interview partners was needed. Moreover, selecting a design option, especially in the perception and physical layer of the system architecture, can create path dependencies and narrow down the design space. The evaluation phase was concluded by addressing the general success factors of the proposed design. Here the willingness of the logistical operators to adopt the GHG emissions reporting, the importance of methodology alignments and the need for truly value-adding services were especially highlighted as success factors of the system architecture.

The research concluded by recognising the great potential of primary data to improve the accuracy of emission factors in road logistics. Seven main conclusions and contributions to the field of logistics were made:
1. The inclusion of a multi-domain designer perspective when designing an abstract system architecture in the logistical sector - should be mandatory in the scoping of any project.
2. The identification of relevant stakeholder clusters and an abstract stakeholder analysis to be considered when designing in the socio-technical environment.
3. The categorization of systems requirements into institutional, stakeholder and technical requirements to represent the multi-domain character of the system.
4. The need for financial quantification tools of the CO2 reduction for logistical operators to validate their investment decisions.
5. Compliance with leading European GHG emissions quantification methodologies and data regulations - should also be implemented, e.g. in the EU taxonomy.
6. A stakeholder cluster-specific system architecture, which incorporated the derived requirements and outlined business relationships and data flows in the system, evaluated and approved by industry experts.
7. The issue of the stakeholder-specific requirements for the system leading to multiple co-existing system architecture specifications.


Finally, the research concluded with a short and long-term outlook of how the sector might develop and presented potential future research topics, such as the possibility of using other forms of data sharing, such as data spaces or blockchain applications, for secure and trusted data sharing.

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.

In the upcoming years the Municipality of Amsterdam has to cope with renovation works to approximately 200 kilometers of quay walls and 800 bridges. These projects need to be executed in the least burdensome manner, which is why executing parties are obliged to perform their construction logistics via waterways instead of the road. Stakeholders want to have more insight into the consequences of using waterborne construction logistics and what the possible choices and decisions are within the system. In this research a waterborne construction logistics system is presented from which waterborne scenarios can both be compared with each other and with the conventional road modality. A decision support tool is developed and used to quantify these consequences and it can be concluded that water is always the preferred alternative when looking at movements and vessel/vehicle kilometers through the city, which is for both indicators a decrease of approximately 80%. From this analysis can be concluded that in the most optimistic scenario it is possible for waterborne logistics to become less expensive than road transport, but under the conditions that the average transported load and vessel speed are rather high. It is therefore recommended to further investigate the optimum between vessel capacity, loading rate and vessel speed and for the Municipality of Amsterdam to involve executing stakeholders to contribute to reaching this optimum.

Heavy-duty road transport needs to make a transition to become climate neutral in 2050. Not considering the negative impact on the environment, road transport remains a competitive way of transportation in the hinterland of maritime ports. Policymakers and strategists of the Port Authority have a lack of knowledge to decarbonize heavy-duty transportation in the hinterland, without deteriorating the competitive position of a maritime port as a transit node. Various studies are made about the transition in the heavy-duty road transport segment. However, a social cost-benefit analysis that assesses social-economic welfare effects of energy carriers to decarbonize heavy-duty road transport in the hinterland of maritime ports remains unaddressed in the literature. This resulted in the following main research question: What is the socio-economic feasibility of strategies for decarbonization of heavy-duty road freight transport in the hinterland of maritime ports towards 2050? In the literature review promising energy carriers and technologies to decarbonize heavy-duty freight were: battery electric vehicles that drive on renewable electricity, a catenary electric road system that supplies more direct electricity, fuel cell electric vehicles that drive on green hydrogen and conventional trucks that use e-diesel. Considering the availability and the total cost of ownership of these energy carriers and technologies, strategies are developed in which the usage is mandated or stimulated by subsidies. The strategies are compared with the continuation of diesel (the zero-alternative) in a social cost-benefit analysis. The costs and the benefits included are the direct, indirect effects and the societal costs from a tank-to-wheel scope and external effects from a well-to-wheel scope in order to ensure that the emissions do not shift to another part of the chain. The results show that at the current CO2-valuation, all proposed strategies are not welfare enhancing and have severe losses of tax on fossil fuels. However, at an increased CO2-valuation of 500 euro per ton emitted CO2, the strategy in which battery electric vehicles are stimulated by subsidy would be feasible from socio-economic perspective. The social cost-benefit analysis provides insights for infrastructure investments in port areas.

This paper investigates the uncertainties underlying carbon footprint estimations of freight transportation, with a particular focus on trip-level shipment data. The research aimed to identify and classify possible causes of these uncertainties using Walker’s uncertainty matrix, drawing from literature reviews, protocol analyses, field research, and interviews. Findings suggest that the accuracy and transparency of assumptions are crucial when communicating carbon footprint estimations, as emphasized in current literature. This study contributes to the existing knowledge by providing deeper insights into the influence of uncertainty causes on the final estimation. The primary source of these uncertainties can largely be attributed to data accuracy in the calculation of carbon footprints. A structured approach is proposed to examine potential causes of uncertainties and their potential impacts on the final carbon footprint estimation. To demonstrate the effectiveness of this approach, a case study analyzing three freight movements to a building site was conducted. This research offers valuable insights and a systematic method for addressing uncertainties in carbon footprint estimations, ultimately supporting more informed decision-making in the freight transportation sector.