Future demand for container transport is expected to increase, placing pressure on the port-hinterland system of the Rhine river. To meet European climate goals and relieve the congested road freight system, inland waterway transport (IWT) is promoted as a sustainable alternative. However, IWT is vulnerable to low water levels as a result of climate change, as evidenced by significant drought periods in 2018. This research investigates how the strategic establishment of transshipment hubs can enhance the climate resilience of the port-hinterland system of the Rhine river under varying river discharge scenarios. The focus is on three critical bottlenecks in terms of available draught: Druten, Duisburg, and Kaub. This study quantifies the impact of new hubs on the total cost and CO2-equivalent emissions by developing a mathematical location routing model, which is used for a scenario-based analysis with varying river discharge levels and rail capacity levels. Results show that hub establishment significantly reduces both total transport costs and CO2-equivalent emissions during low discharge scenarios, without requiring expansion of existing terminals. The establishment of hubs depends on location and multimodal accessibility, with rail connections only becoming economically viable when the remaining hub-destination distance exceeds certain distance thresholds. The findings provide actionable insights for policymakers and transport authorities on strategic decisions of infrastructure investments to improve the climate-resilience of freight transport along the Rhine corridor to minimize total costs, reduce CO2-equivalent emissions, and relieve congested road networks.

The increasing size of cargo vessels poses significant challenges for ports in ensuring safe mooring, as larger ships result in higher mooring forces.
Most existing port infrastructure and mooring equipment were designed for smaller ships, making accurate estimation of mooring line forces increasingly critical.
Metamodels, machine learning models trained on numerically simulated data, offer a promising alternative to traditional, computationally expensive simulation-based methods by enabling rapid predictions with a useful level of accuracy.
This study proposes a metamodeling approach for the numerical Dynamic Mooring Analysis (DMA) to predict mooring line forces from input parameters that describe environmental conditions, mooring systems, and ship characteristics.
The methodology is demonstrated in a case study involving a 333-meter container vessel moored at a berth in the Port of Rotterdam.
A total of 11,520 scenarios were simulated using the DMA model aNySIM and used to train and test two candidate metamodels: Linear Regression (LR) and Multilayer Perceptron (MLP).
After evaluating both models on predictive accuracy, efficiency in terms of prediction speed and development effort, and interpretability, the MLP was selected as the preferred DMA metamodel.
It achieved high predictive performance, with an RMSE of 10 kN and an R² of 0.996, while offering prediction times measured in microseconds. This is more than seven orders of magnitude faster than the numerical DMA, thereby enabling large-batch predictions.
The metamodel revealed that pretension is clearly the most dominant feature for predicting the mean mooring line force, followed by MBL. For the maximum mooring line force, the most influential features were identified as pretension, windvelocity, and wind direction.

Over the past decade, consumer shopping habits have increasingly shifted towards online grocery purchases, creating a growing demand for efficient and scalable warehouse operations. As order volumes and complexity rise, the optimization of warehouse processes has become essential to meet requirements while controlling operational costs. This study addresses the Joint Order Batching Picker Routing Problem (JOBPRP) by developing an exact optimization approach and examining the interdependencies among key warehouse processes, such as batching, routing, and product allocation. Using a case study of Crisp B.V., an online grocery retailer, the proposed algorithm was implemented to optimize configurations across multiple temperature-controlled zones with varying operational characteristics, such as differing pick densities and operational constraints. The research shows that all warehouse processes are interconnected and the best performing configuration is depending on the operational characteristics of the warehouse. The optimization approach achieved a 39.14% reduction in weekly travel distance compared to Crisp’s current benchmark, highlighting its potential to significantly enhance travel distances. These findings highlight the significant impact of integrating order batching and picker routing on warehouse efficiency. The study not only demonstrates the critical correlation between warehouse processes but also provides actionable insights for optimizing order picking in high-density, large-scale warehouses

Metro networks face operational challenges due to increasing ridership and system growth, particularly in managing delay propagation. Epidemiology models have recently been an interesting method in transportation research for studying delays. This study, therefore, aims to see if the Susceptible-infectious-susceptible (SIS) model is suitable to help model delay propagation in a metro network through its ability to reproduce the vulnerability of metro stations for specific instances. Using data from the Washington Metro Network, delay propagation instances were grouped, and the model was trained and tested using a differential evolution algorithm. The results indicate that the vulnerability values as calculated from the data do not follow the expected trend. Also, the model can predict the vulnerability values for the first group more accurately than the second group. However, limitations such as underestimation and overestimation of station vulnerabilities, and sensitivity to training data and parameters were observed. These challenges stemmed from the dynamics between specific parameters, the mismatch in the order of magnitude of model components, and the lack of additional factors.

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.

The Dutch Ministry of Defence (NLMoD) has stated the goal of reducing dependency on fossil fuels by at least 20% by the year 2030 and at least 70% by the year 2050 compared to the year 2010. Research in the NLMoD explores the possibility of changing diesel-fuelled vehicles and weapon platforms to alternative forms of energy such as electric or sustainable fuels. In these projects, the focus is on (part of) the vehicle or energy source itself, but the impact on the Military Supply Chain (MSC) is missing.

This research has developed a Mixed Integer Linear Programming model that can be used to gain insight into the impact of the energy type of tactical vehicles and weapon platforms on the MSC and therefore is able to see what energy type has the lowest impact on that MSC. The impact on the MSC is measured by minimizing the refuel time, number of supply trips, and CO2 equivalent emissions. The model can provide insight into what the minimal requirements of potential energy carriers and conversion devices should be in order to have a similar or better impact on the current diesel MSC. The model is based on the current supply chain of the NLMoD and is expanded with the use of APUs for vehicles, energy generation at Nodes, the use of small supply trucks as energy buffers, compatible supply material, and longer self-sufficient times. Combinations of these are looked at in different policies.

Results show the trend that energy types with lower CO2 equivalent emissions have higher refuel time and number of supply trips. An exception to this is HVO and HVO-electric series hybrid, which also have the least impact on the MSC. Energy types such as hydrogen and electric require huge improvements in energy density, fill speed, and FTW efficiency to come close to the results of current diesel.

Purpose - With the short term need to reduce direct CO2 emissions of trucks in distribution processes, this paper aims to provide an easy to implement solution approach for distribution processes of new cars from holistic perspective. Scarce emphasizes is provided on short-term alternatives in truck-based distribution processes and approaches lacks applicable for large scale problems including split delivery function.
Design/methodology/approach - The distribution process and model methods are analyzed using a literature study and interviews with experts, resulting in the development of a solution approach. Combined with an extensive field research, a solution approach enables the performance evaluation of the current state, and the policy implications. Future designs are used to validate the solution approach by calculating performance differences in multiple relevant evaluation domains.
Findings - The analysis of the current state has identified critical bottlenecks, leading to the development of two promising policies. The application of a new and validated prioritization strategy and permitting more stops per truck has successfully yielded a significant reduction in CO2 emissions. The performance of the solution approach demonstrates high precision on a small scale and yields results comparable to actual practices on a larger scale, suggesting the approach's effectiveness and potential for future application.
Research limitations/implications - This research provides a new solution approach for evaluating direct CO2 emissions of model different designs of distribution processes. Despite its narrow scope, the transportation sector has a significant environmental footprint, and offers the potential for substantial reductions in emissions. From modeling perspective, further research is suggested in integrating split delivery function without using dummy variables.
Originality/value - This paper contributes by identifying critical gaps in the understanding and implementation of system-wide efficient car distribution processes from distribution hubs to car dealers. It not only addresses potential improvements, but also proved efficiency gains of the system with a new solution approach, using a new combination of a state-of-the-art meta-heuristic and a proven split delivery method applicable for large-scale problems.

Despite being a long-cherished EU ambition and a crucial key to achieving climate goals, there is still no European High-Speed Rail (HSR) network, with the few completed projects often facing disappointing demand resulting in unprofitability. In order to gain insights to profitable network design, this study develops a new formulation to the “Transport Network Design & Frequency Setting Problem” (TNDFSP), as current literature lacks one that can optimally solve the problem for instances of this size while also accounting for demand elasticity. Our model works with elastic and dynamic HSR demand calculated from optimal fare settings and observed travel mode characteristics from competing alternatives. The optimal solution is largely insensitive to fare changes, and in some measures outperform current state-of-the-art. Our model considers the 111 most populous European cities, along with all their origin-destination (OD) pairs, and finds the most profitable network design within a reasonable solution time frame. The results show HSR can be very profitable in Europe, but only when concentrated around a selected group of the largest cities in the western part of the continent. Despite being profitable and contributing significantly to set Green Deal goals, the viability of our network heavily depends on the willingness of several countries to invest significantly in infrastructure construction.

The distribution chain of two-dose vaccines by an air carrier (KLM cargo) with its practical features is modelled to study the influence of supply uncertainty. First the goal is to find an efficient solution approach for the model which gives good quality solutions in reasonable computation time. Secondly with the developed solution method the supply uncertainty is taken into account using robust optimization. Solving the model using an exact solution method leads to a too large increase of computation time with increase of the model size for the purpose of robust analysis. Nevertheless, given the high reliability of the model the results of this commercial model are used as a reference. To circumvent the high computation time, two alternative solution methods are developed and implemented. Respectively the genetic algorithm and the rolling horizon method. A basic implementation of the genetic algorithm does not provide adequate results and gets trapped in a local optimum. An analysis shows that measures are needed to increase the flexibility and stimulate the algorithm to find so called “transaction-less” events. To achieve this, a toolbox is developed. The toolbox consists of analysis tools (measurement of convergence rate, sparsity and a diversity measurement) and tools intended to improve the convergence and accuracy of the solution. These latter are an adapted mutation operator that stimulates the number of transaction-less events (sparsity) and an approach for directed mutation. Each measure by itself has a positive effect on the initial convergence rate, but the algorithm still gets trapped at a somewhat improved local optimum at a level of approximately 10% above the optimum solution. A strong improvement is found by combining these measures of the toolbox resulting in solutions that approach the optimal solution within less than 5% for a single destination at a very high convergence rate. The best found combination of measures are implemented to solve a multi-destination problem. The results prove to be not as good as the result for the single destination: the gap to the optimal solution is roughly 10% and the convergence rate is somewhat slower. Probably this is due to the fact that the boundary conditions impose a reduction of flexibility for the multi-destination setting. On the other hand this finding might provide a base for worthwhile future work. Since this is solver is not (yet) suitable to be used in the robustness analysis. Three different implementations of the rolling horizon method were made: (i) the straight forward (myopic) approach, (ii) extending the time window with relaxed periods and (iii) a shifting rolling horizon. The straight forward approach is significantly improved by using the relaxed and shifting methods...

In recent years, shared mobility systems have had a growing presence in cities all over the world. This is understandable given its numerous advantages such as the reduced need for personal vehicle ownership, reduced traffic congestion and emissions, increased parking efficiency, and cost savings for users. Overall, shared mobility systems offer the potential to revolutionize transportation, providing individuals with more options and helping to create more sustainable, livable cities. For shared mobility systems to fully deliver their benefits, vehicle availability must be maintained at the right place and time. If the vehicle distribution is not optimal, it may lead to overcrowding and shortages which in turn will discourage usage and lead to reduced revenues for the operator. Therefore, ensuring proper balancing of supply and demand is crucial for the success of the shared mobility service. One way to balance supply and demand is through physically rebalancing vehicles within the service area. In this study, a simulation-based optimization model is created and used to determine the optimal rebalancing operations while quantifying system improvement. A case study is conducted using real data from the Dutch moped sharing provider Felyx to examine the impact of performing rebalancing operations in Eindhoven throughout May ’22. The results demonstrate a potential increase in profit of up to 2.06%. By performing the recommended rebalancing actions several times a week in each city where the operator is active, a significant amount of extra profit can be made. This additional profit will even rise as the usage of shared mobility rises in general.

The global market for personal mobility has transformed over the last decade. Traditional taxi services have to compete with the emergence of ride-hailing services such as Uber and Lyft. Rapid developments in available algorithms and real-time inter-connectivity of travellers and vehicles offer mobility-on-demand (MOD) services new possibilities to maximise the efficiency of the ride-hailing process. It is well established that rebalancing can enable the true potential of a ride-hailing fleet. In this context, rebalancing is defined as the redistribution of idle vehicles over the service area of a ride-hailing operator. In the search for the optimal rebalancing strategy, model predictive control (MPC) and approximate dynamic programming (ADP) methodologies are active fields of research. However, the computational burden of MPC limits the length of the predictive horizon in large MOD applications. This thesis proposes a novel algorithm that combines ADP and MPC. More specifically, we integrated a value function into the MPC framework as a terminal cost. We shorten the horizon of MPC and propose to use the value function as a long-term planner. For the terminal cost, we continue research into piece-wise linear value function approximation. We implement a novel multi-period approximation of the value function, where we use the maximum repositioning length as the horizon. In our case study, the value-based repositioning strategy offers comparable service quality to conventional MPC at 5.2% of the computational burden. The hybrid ADP-MPC algorithm offers flexible horizon partitions between the multi-period value function and MPC. It addresses the shortcomings of both ADP and MPC algorithms in repositioning problems. At peak performance, it offers an increase of 4.5% in service rate and a decrease of 5.2% in the average waiting time over conventional MPC, at roughly half the computational burden. Keywords: Ride-hailing, Mobility on demand, Model predictive control, Approximate Dynamic programming

This thesis has provided insight into how machine learning can be beneficial to path planning in container terminals. Path planning algorithms can be used in environments with automated vehicles. A well known algorithm is the A* path planning algorithm, which is the fastest optimal path planning algorithm under satisfied conditions. However, the behaviour of a container terminal is unknown beforehand, costs can change over iterations. Therefore, Liu et al. [Liu et al., 2019] and Keselman et al. [Keselman et al., 2018] show the advantage of combining A* with Machine Learning. This way, the exploring part of the ML algorithm is combined with the fast andmore precise properties of the A* PP algorithm. This thesis has proposed the machine learning algorithm Vehicle Aware Reinforcement Learning Path Planning Algorithm VARLPPA. This algorithm uses Monte Carlo Control method. This is a model free approach, which has been shown in both experiments to find more efficient solutions in exceptional situations.

Around the world, authorities try to increase the attractiveness of multimodal public transport (PT)-related trips to reduce car usage. To achieve this, a seamless combination between the different modes is necessary. The Dutch train station operator NS tries to enhance the combination of the bike and train by providing a train station-based round-trip bikesharing (SBRT) scheme located at train stations throughout the country. This scheme allows users to rent a bike to connect the train station and their destination. The round-trip characteristic SBRT makes it unique in comparison to widely applied one-way bikesharing schemes. While on the latter a wide range of research exists, little research has been conducted on round-trip bikesharing, especially when being integrated into an existing public transport scheme. This paper aims to fill this gap by identifying potential temporal and weather-related determinants for SBRT-rentals of the Dutch SBRT-system OV-fiets using multiple linear regression (MLR). The results are compared with findings on one-way bikesharing schemes. The results are then used as an input to forecast short-term demand. To identify a best performing forecasting method, the statistical methods MLR and Prophet are compared with the neural-network based method Long Short-Term Memory (LSTM).
It is found that for hourly rentals in an SBRT-system, the highest explanatory power achieved with the number of train travelers leaving the corresponding train station, followed by temporal and weather-related determinants. Further, the magnitude of the correlation between the determinants and the hourly demand differs across the stations in the system. For forecasting, the performance of the methods differs across the stations and forecasted periods due to the stations' distinct characteristics. But, especially in times of uncertainty, LSTM is likely to outperform the others due to it's capability of adapting to short-term changes in the demand.

When traffic demand is high and the intersection lacks throughput, oversaturated traffic phenomena occur, such as the oversaturation of a turning bay or intersection link. When such unsafe situations emerge, control strategies can be applied to handle or avoid these spillback effects. In this paper, a spillback component is designed that detects and controls spillback in a real-time controlled traffic system (RTCS) that optimizes multiple vehicle types in its optimization, with a high vehicle priority for the light-rail vehicle (LRV). A spillback control strategy was developed that assigns additional priority to the congested link when spillback is detected. For assessment, a simulation study is conducted to gain insight into the effects of applying spillback control strategies on the traffic and network performance at an isolated intersection and in a corridor. The results showed that the intersection performance decreased when a spillback control strategy was applied in the optimization, but the network performance increased during the peak period. Furthermore, a spillback penalty factor effectively decreases spillback durations while it works well in combination with higher prioritized public transit. A too-high priority negatively impacts the intersection and network performance. However, in some cases, a (high) spillback priority is necessary to provide enough priority so that a growing unsafe situation is avoided. Therefore, it is crucial to determine the intended use and applicability when providing spillback priority. The spillback priority is one of the few priorities that can be configured in the utilized RTCS; it is recommended to research the impacts when varying priorities are used.

This paper describes a real-time energy management system developed for a solar park in the Netherlands using an alkaline electrolyser. The optimization problem is split into a two-step optimization, taking into account the specifications of the electrolyser and allowing the electrolyser to respond to changes in the imbalance market. The first optimization step determines the state of the electrolyser one day in advance. The second optimization step determines the electrolyser power, using the state of the electrolyser as an input. Simulations using data from 2020, 2021 and 2022 show that the use of the electrolyser is limited to a number of days in the year with a lot of solar generation, causing the day-ahead prices to be low. Different scenarios have been tested to get insight into how the use of the electrolyser is influenced by these changes. The type of electrolyser, being able to put the electrolyser on standby and allowing the grid to be used for the electrolyser hardly affected the results. At last, different hydrogen prices are compared. The higher hydrogen prices lead to more use of the electrolyser. This real-time EMS contributes to the ability of an alkaline electrolyser to respond to the sudden changes in the grid and by doing this making it possible to use alkaline electrolysers for balancing the grid and contributes to the use of green hydrogen in the industry for a competitive price.

With increasing urbanization and a need to reduce greenhouse gas emissions, new forms of urban mobility are studied worldwide. An interesting transportation method is ridesharing, where people can share parts of their rides with other passengers travelling in a similar direction in the same car. To im- prove the attraction of ridesharing systems, research into extensions to the existing ridesharing model formulation is required. The goal of this research is to make ridesharing more attractive to potential users by including the option to make reservations. In this work, the inclusion of reservations (pre-bookings) into the on-demand ridesharing problem is studied. The inclusion of ridesharing has to potential to make better assignments by having knowledge on part of the future demand. The main challenge from reservations is the increased computational load generated by having more available information. This thesis proposes multiple adjustments and different heuristics for a state-of-the-art on-demand ridesharing system to deal with the requirements and challenges brought by the incorporation of reservations. An additional consideration is a trade-off between providing benefits for reservations and serving on-demand requests. Numeric experiments are performed on the Manhattan street network with NYC taxicab request data. The results indicate that reservations can be included in on-demand ridesharing at a tolerable compu- tational cost with the proposed framework. Passengers that make reservations shortly in advance can be guaranteed service once initially accepted by the system. Having passengers make reservations in advance does increase the overall service the system can provide.

The significant growth in B2C e-commerce in the last decade increased the traffic and volume of parcels in last-mile delivery significantly. To mitigate the impact of the last-mile delivery service such as the increased traffic and carbon dioxide (CO2) emission, crowdshipping emerged as an innovation that utilise the travelling crowd as occasional courier to deliver parcels. The goal of this study is to analyse the potential impact of crowdshipping performed by private cars to the local vehicle mileage and its result to the CO2 emission. This is done by simulating the passenger and parcel delivery transportation activities in agent-based modelling platform using MASS-GT and MATSim. An integration framework to bridge MASS-GT and MATSim was formulated in this study to model the interaction between the passenger and urban freight transport. Several scenarios are formulated to analyse the impact of crowdshipping. The results show that the CO2 and passenger-kilometres savings in parcel delivery vans transportation is exceeded heavily by the increase in passenger transportation performed by cars. This results in a slight increase in passenger-kilometres travelled and CO2 emission caused by car-based crowdshipping, although the value is very small that could be considered insignificant. It was found that car-based crowdshipping won’t either improve or worsen the impact of the current last-mile delivery system. It could be concluded that crowdshipping will be better performed using a more sustainable transport mode instead.

To decrease Europe’s harmful emissions, the European Union aims to substantially increase its offshore wind energy capacity. To further develop offshore wind energy, investment in ever-larger construction vessels is necessary. Designing these vessels is difficult however, as this market is characterised by a large but seemingly unpredictable growth of market demand, turbine size and distance from shore. Currently no way of dealing with such market uncertainty within the ship design process was found to exist. This research aims to develop a method that is able to deal with market uncertainty in early ship design by increasing knowledge when design freedom is still high. The method uses uncertainty modelling prior to the requirement definition stage by performing global market research, and during the concept design stage by iteratively co-evolving the vessel design and business case in parallel. The method consists of three parts; simulating an expected market from data, modelling multiple vessel designs, and an uncertainty model that evaluates the performance of the vessels in the market. The case study into offshore wind foundation installation vessels showed that the method can provide valuable insight in the effect of ship design parameters like main dimensions, crane size and ship speed on the performance in an uncertain market. These results were used to create an initial design that is expected to perform well in the uncertain market. The developed method thus provides a way to deal with market uncertainty in the early ship design process.

Convention centers are facing logistic challenges due to poor performance, lack of space and governmental regulations. This research is focused on finding strategies that can improve performance while maintaining or reducing space and complying to these governmental regulations. In this research three main strategies are introduced that can improve logistic performance at convention
centers. These main strategies are: a truck appointment system, extended operational times and cross-docking. Besides these strategies specific designs are made for the case of the RAI Amsterdam. These designs differ in infrastructure and material handling equipment such as automated vehicles. It was found that implementation of a truck appointment system, extended operational times and cross-docking can improve the performance of convention center logistics. Further improvements can be made by adjusting the infrastructure and by the use of automated vehicles.