Airports are electrifying Ground Support Equipment (GSE) fleets to reduce local emissions, but electric GSE changes both fleet-capacity and operational-demand planning. This paper investigates how operational requirements, uncertainty, and charging affect the required fleet capacity and operational demand of electric Ground Support Equipment (eGSE) during airport turnaround operations. A rule-based discrete-event simulation represents individual vehicles, service time windows, airport travel, battery state, finite charging infrastructure, and service-specific resources. The focused case study considers two operationally distinct vehicle types at KLM Ground Services at Amsterdam Airport Schiphol: depot-based toilet vehicles and stand-side loaders. The model is extensively verified and validated for fitness for purpose using flight and task data, airport distances, track-and-trace movement data, measured energy consumption, state-of-charge and charging observations, waste and dumping logic, historical arrival deviations, and expert review. Water vehicles were also implemented and validated in the underlying study but are omitted from the detailed paper results to avoid repetition. Under combined task-timing and travel-time uncertainty, the strict fleet requirements are 15 toilet vehicles and 25 loaders. Pragmatic lower bounds of 9 toilet vehicles and 23 loaders achieve 99.90% and 99.93% mean on-time completion, respectively, but retain residual service-window risk. Operational-control experiments show that idle forward staging reduces the strict stochastic toilet requirement from 15 to 10 vehicles and substantially reduces deadheading. Feasibility-aware task triage reduces the number of late tasks under scarcity, but increases the lateness of tasks that are deprioritised. Charging is not the binding cause of lateness in the tested reference configurations, although it materially affects charging sessions, charger occupancy, peak use, and infrastructure demand. The scientific contribution is therefore a decision-support method that estimates fleet capacity and diagnoses how demand timing, positioning, dispatching, uncertainty, and charging resources shape operational performance. ... Read more

Airports are increasingly electrifying Ground Support Equipment (GSE) fleets to reduce local emissions and support decarbonisation targets. Although electric Ground Support Equipment (eGSE) are well suited to many airside operations, electrification changes the fleet-sizing problem. Vehicle availability is no longer determined only by task duration, location, and travel time, but also by battery state, charging duration, charger access, and operational charging rules. These constraints are especially relevant during aircraft turnaround operations, where multiple time-critical ground-handling tasks must be completed within narrow service windows.

This thesis investigates how operational requirements, uncertainty, and charging affect the required fleet capacity and operational demand of eGSE in airport turnaround processes. A structured review of the GSE and eGSE modelling literature shows that existing studies provide important building blocks for routing, scheduling, energy management, and charging analysis. However, the joint integration of task execution, individual vehicle availability, charging behaviour, infrastructure constraints, and operational uncertainty remains limited. In particular, many models either simplify charging and vehicle-level states, treat fleet size as a fixed input, or do not represent uncertainty. This motivates the development of a simulation-based decision-support approach in which eGSE fleet sizing is evaluated as a dynamic vehicle-availability problem.

A rule-based Discrete Event Simulation (DES) framework is developed to represent daily eGSE operations. The model includes individual service tasks, vehicle-specific states, airport location groups, travel times, battery state of charge, charging sessions, charger capacity, refilling, dumping, depot-return behaviour, and service-specific operating rules. Vehicles are assigned to tasks based on task urgency, time-window feasibility, travel time, battery state, and operational resource constraints. Task-timing uncertainty and travel-time uncertainty are included to evaluate how stochastic operational variability affects service-window performance and fleet-size requirements.

The framework is applied in a case study at KLM Ground Services (KLMGS) at Apron Services (AAS). The validated case-study scope includes three vehicle groups: water vehicles, toilet vehicles, and loaders. These groups represent both depot-based resource-constrained operations and stand-side service operations. The model is verified using synthetic test cases and validated through operational data checks, expert judgement, service-demand validation, energy and charging behaviour, water-demand consistency, and uncertainty-representation checks. Simulation experiments are then used to assess baseline performance, deterministic fleet sizing, task-timing and travel-time uncertainty, combined uncertainty, demand-case robustness, idle forward staging, feasibility-aware task selection, and sensitivity to operational and charging parameters.

The results show that required fleet capacity depends strongly on the service-level interpretation. Under deterministic operating conditions, the smallest fleet sizes that achieve 100% on-time task completion are 12 water vehicles, 11 toilet vehicles, and 23 loaders. Under combined task-timing and travel-time uncertainty, the strict robust thresholds increase to 16 water vehicles, 15 toilet vehicles, and 25 loaders. These are the smallest tested configurations that achieve 100% on-time task completion across all 30 stochastic replications. However, if a very small number of short internal service-window violations is operationally acceptable, lower fleet sizes may also be defensible. Under this pragmatic interpretation, at least 11 water vehicles, 9 toilet vehicles, and 23 loaders achieve ≥99.90% on-time task completion. These results should be interpreted carefully, because the performance metric measures completion within internal service windows and does not directly measure aircraft departure delay.

The experiments also show that operational-control assumptions can materially affect fleet-size outcomes. Idle forward staging, where idle vehicles remain near the aircraft stand instead of returning immediately to the depot, reduces unnecessary deadheading and improves service performance for selected vehicle groups. For toilet vehicles, the deterministic strict requirement decreases from 11 to 9 vehicles. Under combined uncertainty, idle forward staging reduces the strict robust threshold from 16 to 14 vehicles for water vehicles and from 15 to 10 vehicles for toilet vehicles. For loaders, the fleet-size threshold remains unchanged, but driven distance and depot-return movements are substantially reduced. A feasibility-aware task-selection variant further shows that dispatching logic can reduce the number of late tasks under scarce-fleet conditions, although it may increase the lateness severity of tasks that are already infeasible.

The sensitivity analysis indicates that charging is not the main driver of late task completion within the tested configurations. Energy-related parameters mainly affect charging sessions, charger occupancy, and charging-infrastructure utilisation. Task punctuality is more strongly constrained by vehicle availability during demand peaks, travel-time assumptions, service-time assumptions, and operational positioning logic. Charging therefore remains important for infrastructure planning and vehicle availability, but it is not the dominant bottleneck in the tested case-study settings.

Overall, the study shows that explicitly simulating operational requirements, uncertainty, and charging changes the interpretation of eGSE fleet sizing from a static vehicle-count problem into a dynamic availability problem. Required fleet capacity depends not only on the number of tasks, but also on when and where vehicles are needed, how uncertainty clusters demand, how quickly vehicles can recover between tasks, and how charging and supporting infrastructure affect vehicle availability. A simulation-based approach therefore provides a useful decision-support method for assessing eGSE fleet capacity, operational robustness, and charging-related resource use in airport ground handling. The reported fleet sizes should be interpreted as operational fleet-capacity requirements under the tested service-level assumptions. Final implementation decisions should add a technical reserve for maintenance, failures, battery degradation, charger unavailability, and other sources of vehicle downtime. ... Read more

Inland and short-sea container shipping in Northwestern Europe relies on manual planning by experienced logistics operators. This process, while effective for routine operations, is time-consuming, difficult to scale, and limited in its ability to globally optimize fleet utilization. The underlying scheduling problem is classified as a Heterogeneous Vehicle Routing Problem with Pickup and Delivery and Time Windows (VRPPDTW), incorporating domain-specific constraints such as minimum call sizes, forbidden terminals, terminal opening hours, and mandatory vessel breaks.

This thesis presents Orion, a constraint-based optimization solver built on the Google OR-Tools Routing Library, designed to automate and improve this vessel scheduling process. The problem is formulated using Constraint Programming, which allows complex business rules to be expressed as logical predicates rather than linearized inequalities. The solver pipeline includes a data preprocessing stage that aggregates individual container orders into compound orders, reducing problem size by approximately 90\% while preserving solution quality.

The evaluation follows a three-phase methodology. First, a feasibility analysis filters 14 construction heuristics down to two viable candidates: Local Cheapest Insertion and Parallel Cheapest Insertion. Second, a parameter tuning phase identifies Local Cheapest Insertion combined with Tabu Search as the best-performing configuration, achieving an Average Relative Percentage Deviation of 2.72\% across all scenarios. Third, a benchmarking phase compares Orion against human planners and the company's existing Simulated Annealing solver (Baseline SA) on six real-world scenarios from three distinct logistics operators.

The results show that Orion achieves travel cost reductions of 6.7\% to 32.8\% compared to human planners on five of six scenarios and outperforms Baseline SA's average result on five of six scenarios. A key structural advantage is determinism: Orion produces identical results across repeated runs, whereas Baseline SA exhibits variance of up to 37.5\% between its best and worst runs. Convergence analysis indicates that 96--99.97\% of objective improvement occurs within the first 30 seconds, making a 5-minute time budget sufficient for operational use.

The thesis concludes with recommendations for production deployment and identifies future research directions, including dynamic water level constraints, improved rolling-horizon replanning, custom fleet reduction operators, and hub-based consolidation strategies.
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Freight mode choice models are traditionally estimated using discrete choice models such as the Multinomial Logit (MNL), valued for their interpretability but often limited in their predictive accuracy and ability to capture complex, nonlinear relationships. Recent studies have applied machine learning to freight mode choice using disaggregate shipment-level data. A gap remains in assessing how machine learning models trained on aggregate data perform and how they compare with MNL models for policy applications. This study addresses this gap by training three machine learning models (logistic regression, Random Forest, and XGBoost) on EU aggregate freight flow data and evaluating them with reference to the NEAC MNL model. The models are assessed using seven criteria identified as relevant for freight policy analysis: predictive performance, interpretability, practicality, computation time, robustness, generalizability, and data efficiency. The results show that XGBoost achieves the highest predictive performance, while logistic regression demonstrates advantages in generalizability, robustness, and data efficiency. The findings highlight the trade-off between predictive performance and interpretability/practicality, indicating that machine learning models can complement but not replace Logit-based models in freight policy analysis. ... Read more

This thesis examines the use of a potential field model for simulating pedestrian dynamics in complex environments. The study first reviewed the different types of pedestrian dynamics models, highlighting their strengths and weaknesses. From this study, a research gap emerged regarding hybrid pedestrian movement models. As a base for such a model, a microscopic pedestrian dynamics model has been developed combining potential fields and gradient descent optimization as the drivers for trajectory selection. In this formulation, agents follow trajectories along the gradient of the potential field, naturally balancing goal seeking behavior with obstacle and inter-agent avoidance. The potential field approach was also discussed as a foundation for hybrid models, in which microscopic and macroscopic modeling strategies are combined to exploit the advantages of both. To evaluate and calibrate the model, real-world trajectory data from a bidirectional corridor experiment and a bottleneck experiment were used. A surrogate model was constructed to accelerate the optimization process, given the high computational cost of the original simulation model. The surrogate model enabled systematic parameter calibration and sensitivity analysis, focusing on three key parameters: the goal potential function weight (KG), the wall potential function weight (KW), and the obstacle potential function weight (KO). The results demonstrated that the optimized model is capable of reproducing key crowd phenomena observed in the empirical datasets. A sensitivity analysis further showed the relative importance of the potential function weights across different key performance indicators. Moreover, predictive uncertainty analysis confirmed that the model exhibited relatively high confidence around the optimum and avoided regions of overfitting. Despite these contributions, the research was constrained by the computational cost of the simulation model. The reliance on a surrogate model limited the optimization to a small subset of parameters, assuming that other model parameters were already sufficiently calibrated. This assumption likely introduced some biases, such as underestimation of obstacle repulsion, leading to overly frequent close inter-agent encounters. In conclusion, this thesis has demonstrated that potential field models, when combined with real-world data and surrogate-based optimization, provide a valid and powerful framework for simulating pedestrian dynamics in complex environments. Their ability to model pedestrian trajectories through potential functions and gradient descent makes them conceptually simple yet effective, while their extensibility offers a pathway toward hybrid models. Nevertheless, computational burden and limited parameter coverage remain key challenges, highlighting the need for more efficient implementations and broader parameter optimization in future research. ... Read more

This study develops a synchromodal transport model with real-time replanning using a Double-Dueling Deep Q-Network (D3QN) agent. The hinterland port-hinterland leg dominates supply-chain costs and remains vulnerable to disruptions. We simulate realistic disruptions using varying occurrence and severity levels and train a D3QN agent to decide whether an affected shipment should wait or reassign to another mode. Model performance is evaluated in a Rhine-Alpine corridor case study against two baselines strategies (Always Wait, Always Reassign) and a tabular Q-learning benchmark. The results show that the D3QN policy achieves the lowest costs on a combined set of disruptions, outperforming alternatives overall. In particular, D3QN excels under high-occurrence, low-severity disruptions but appears less effective under rare, high-severity events. The integration of the D3QN with parallel simulation greatly sped up training (roughly 4 times faster convergence) without degrading policy quality. It is discussed how classifying disruptions by occurrence and severity helps tractability, and show that the D3QN learns to reassign shipments more effectively. The findings indicate that deep reinforcement learning can improve synchromodal resilience, particularly in large-scale settings where conventional RL or heuristics fail. ... Read more

This thesis explores the use of Machine Learning (ML) techniques to model and predict transportation mode choice behavior, a critical component of urban mobility planning. Traditional mode choice modeling relies on Random Utility Maximization (RUM) theory, with models such as Multinomial Logit model and Mixed Logit model (MXL). While these offer interpretability, they often struggle with complex feature relationships and heterogeneity in large datasets. ML methods, by contrast, offer greater predictive power and flexibility, albeit with interpretability challenges. This study evaluates the performance of various ML models like Gradient Boosting and Random Forest Decision Tree on two datasets, with a primary focus on a detailed case study using Swiss travel data. The models are assessed under various configurations, including feature limitation, latent variable extraction, and SMOTE-based resampling. Comparative results demonstrate that ML models consistently outperform traditional Logistic Regression models in terms of F1 and Balanced Accuracy metrics. Additionally, tools like SHapely Additive exPlanation (SHAP) are employed to enhance the interpretability of ML outcomes. The findings highlight the potential of ML to improve Mode Choice modeling, particularly when combined with theory-informed structures and advanced data balancing techniques. ... Read more

The rapid adoption of Commercial Electric Vehicles (CEVs), such as electric trucks and buses, is driven by advancements in battery technology, supportive government policies, and increasing environmental awareness. This growth imposes substantial demand on power grids, necessitating efficient charging strategies to ensure cost-effective fleet operations, grid stability, and environmental sustainability. This thesis addresses the challenge of scalable charging scheduling for a heterogeneous fleet of CEVs at depots with heterogeneous chargers, where the number of chargers is less than the number of vehicles. The optimisation objectives include minimising electricity cost, minimising battery degradation, and maximising robustness to variations in arrival and departure times. A Mixed Integer Non Linear Programming (MINLP)-based centralised framework was used as a baseline, and a Multi-Agent Systems (MAS)-based hybrid framework was proposed, in which vehicle, charge request, charger, and depot agents coordinate via direct communication and a shared blackboard. The hybrid method generates charging schedules through four sequential steps, combining centralised decision-making by the depot agent with distributed data processing by other agents. Case studies with fleets of 10, 25, 50, and 100 vehicles showed that the hybrid method produces feasible solutions with minimal deviations in objective values, reduces computation time by up to 99\%, and exhibits approximately linear scalability with fleet size across all objectives. Its flexibility also allows experimentation with agent rules and objective function definitions to evaluate their impact on performance. The results demonstrate that the proposed MAS-based hybrid framework enables scalable and adaptable charging scheduling under grid constraints. ... Read more

This study proposes an integrated mode choice and assignment model that combines the final steps in the classical four-step transport modelling approach. Using multi-objective optimisation, the model minimises service costs while simultaneously maximising the probability of mode selection. Compared to traditional sequential models, the integrated model prevents over-assignment to modes exceeding capacity. The model was then used to asses three key transport mode attributes—cost, capacity, and frequency—to evaluate their effect on the modal split, with a focus on increasing Inland Waterway Transport (IWT). Results showed that increasing road costs and IWT service frequency (with associated capacity growth) had the most impact on shifting modal share towards IWT. These findings demonstrate the potential of this integrated model to better evaluate policy impacts and inform transport planning. ... Read more

Shared Automated Vehicles (SAVs) hold significant potential to redefine urban mobility services. However, the applicability and optimization of their operational strategies in diverse urban contexts remain unclear, particularly concerning the complex interactions with existing public transport systems. This study systematically investigates how urban heterogeneity, including city scale, network topology, and demand patterns, modulates SAV fleet management strategies to balance operator costs and user utility within a multi-modal transportation context.

To this end, this study develops a comprehensive simulation framework that combines an advanced ride-pooling candidate generation algorithm (ExMAS) with an innovative two-stage vehicle assignment optimization process, and integrates a nested Logit model to quantify the competition structure with public transport. This framework is applied to 37 Dutch cities of varying scales to derive generalizable findings.

The study finds that population scale is a fundamental determinant of the required fleet size, exhibiting a strong linear relationship, especially in large cities. The key to enhancing operational efficiency, however, lies in more nuanced urban structure metrics. In large cities, longer commuting distances combined with more complex networks (higher node degrees) foster ride-pooling potential. In contrast, for small cities, the local compactness of the network (higher clustering coefficient) and demand density are critical for improving vehicle turnover efficiency. Furthermore, the research confirms that a uniform pricing strategies are unlikely to achieve best performance on pooling efficiency, highlighting the necessity of implementing differentiated pricing based on user preferences and city scale. In competition with public transport, the study's Public Transport Competitiveness Index reveals a non-monotonic relationship with travel distance. A state of competitive balance is observed for short-distance trips, while PT holds a distinct advantage in the medium-distance range. For long-distance trips, the inherent speed advantage of SAVs allows them to become the more competitive option. This external competition also structurally increases the internal pooling rate of the SAV system.

The findings of this study provide critical strategic insights for SAV operators and policymakers, emphasizing the critical importance of adjusting fleet management, service design, and pricing strategies according to specific urban characteristics and the competitive environment, thereby providing decision support for achieving an efficient and sustainable urban transportation system. ... Read more

In vendor-to-retailer systems where retailers with limited inventory capacity place orders and vendors dispatch trucks within soft time windows, trucks may arrive before sufficient space is available, requiring them to wait on site resulting in inventory-driven delays. Since no existing formulation incorporates inventory-dependent service times, this study models the dispatch problem as a discrete-time Markov Decision Process (MDP) with inventory-dependent service times, solved using Approximate Dynamic Programming (ADP) with a Partial Value Function Approximation (PVFA) that ranks outstanding orders based on urgency. The model is implemented at an Ethiopian brewery and benchmarked against a First-In First-Out (FIFO) policy across three replenishment strategies: Retailer-Managed Inventory (RMI) with Poisson-generated orders, threshold-based replenishment with a fixed lead time, and Vendor-Managed Inventory (VMI) where order timing is optimized. Results show that, depending on the replenishment strategy, the PVFA reduces unmet demand by 53–98%, lowers extended service times by 65–93%, and frees 8–16% of fleet capacity to be repurposed, with the total number of orders served ranging from a 5% decrease to a 22% increase. These findings demonstrate that inventory-driven delays can be effectively incorporated into a dispatch model, enabling soft handling of inventory constraints. The PVFA consistently outperforms the FIFO benchmark across all scenarios, demonstrating its ability to anticipate even in reactive systems. It also exhibits lower variance on Key Performance Indicators (KPIs), indicating robustness under different replenishment settings. As such, the model is well suited for environments where order policies vary or inventory-driven delays are common. Future work may address dynamic order generation, parameter uncertainty, and extensions toward the general Inventory Routing Problem (IRP). ... Read more

Online grocery delivery has seen accelerated growth in the last decade. This growth introduced new challenges in operations, such as damaged groceries upon delivery. Companies have started collecting relevant data, yet operations remain largely disconnected from the data. Motivated by this gap in the industry, we formulated the main research question of this study: "How to improve quality of service by integrating customer feedback in a bin-packing model in the context of online groceries?". A review of recent literature revealed two complementary gaps: limited applications of machine learning in offline one-dimensional bin packing where ML directly assists with the packing process, and the lack of customer feedback integration in bin packing models to improve quality of service.

Therefore, we propose a novel two-part theoretical framework to process historical data of customer damage reports into quantifiable parameters that can be used by a bin packing model. In the first part, we formulate a predictive task and propose a classification machine learning model which provides a probability of damage for the bag, given a set of bag characteristics obtained from customer data. In the second part, we propose to integrate the machine learning component into a bin packing model as a weighted term in its objective function. This integrated model comprises our damage-aware bin packing model.

The proposed methodology was implemented and evaluated through a case study using real data from the daily operations of the online supermarket Picnic. As part of the experiments, we analysed over 60 million articles across 2.2 million deliveries. We started with a comprehensive data analysis to explore the relationships in the data and identify trends. We, then, developed and trained two machine learning variants, a logistic regression and an ensemble extreme gradient boost model (XGBoost) to fulfil the predicting task of bag-damage probability estimation. We applied random undersampling to the training dataset to mitigate the extreme class imbalance (0.41% damage rate). Then, we used the logistic regression model as the ML component and implemented a damage-aware bin packing model. We defined strategic empirical metrics to measure its performance and constructed an evaluation framework using counterfactual analysis on 6000 representative deliveries.

Our findings validate the technical feasibility of integrating customer feedback data into a bin packing algorithm. The damage-aware variant recorded a 14.1% relative reduction in the average probability of damage across all bags of the deliveries tested. On the other hand, extreme class imbalance severely limited the performance of the ML models trained (1% precision score in real operational conditions). As a result, a meaningful review of the economic impact of the model is not possible. The experiments illustrated sensible item movements across the bags tested, measured with some empirical key risk parameters, such as item categories, packaging types, and bag density. The implementation showed a tolerable computational overhead of around 18%, indicating that it is realistic to deploy an efficient model to real operations. ... Read more

Public transportation systems operate in dynamic and unpredictable environments, necessitating close monitoring of real-world operations. Integrating data into route design, scheduling, and policy formulation is essential for creating an efficient and sustainable transit network. This thesis utilises historical bus travel time data to predict future bus travel times, enhancing the analysis of network reachability.

A literature review identified four prediction models to be investigated: Historical Average, Vector AutoRegression, Random Forest, and Long Short-Term Memory deep neural network. A case study involving six bus lines in Groningen was formulated, providing the necessary KoppelVlak 6 and GTFS schedule datasets to be used as inputs for the prediction models. During model development, patterns in historical travel time data were identified, and prediction accuracy was evaluated. The output from the most accurate prediction model was then utilised as input for the reachability analysis.

The analysis demonstrated that complex machine learning models, such as Random Forest and Long Short-Term Memory deep neural networks, yielded the most accurate predictions. The time of day when the journey occurs is particularly predictive of travel and dwell times. Integrating these predictions into a reachability analysis revealed instances of increased reachability, decreased reachability, and missed transfers compared to the original schedule.

The findings demonstrate that travel time prediction models can significantly enhance reachability analyses. This more accurate representation of reachability can be used to identify systemic issues in the design of the public transportation network, allowing for interventions to improve performance. ... Read more

Recent years show an increasing number of people who use regional public transport, resulting in higher pressure on the operators. This increased pressure results in more disruption for passengers. This paper aims to develop a methodology for estimating the criticality of assets in a regional public transport network, taking the Rotterdam tram system as an example. The consequences of disruptions of each asset in the network are analysed by calculating the effect the disruption has on the average path length for all passengers. Each asset in the network is disconnected one by one to simulate this breakdown. The lines used in Rotterdam are analysed using real passenger data. Results show that 'Erasmus brug' is the most critical asset in the network, as expected by experts from RET. Aside from this, the model presents that the assets around 'Rotterdam Centraal' are less critical than expected because of the alternative routing options for the trams and passengers. This highlights that using this model can give new insights into critical assets in networks. ... Read more

Commercial waste collection in Dutch cities is a free market in which many waste collectors operate independently. Collaboration among waste collectors is a relatively recent phenomenon that is still in its infancy. This study develops a Python-based simulation model using OSMnx and
Google OR-tools to assess the potential benefits of collaboration in waste collection in Rotterdam. Four collaborative scenarios are simulated, three of them in a collaborative setting. Results show that collaboration can significantly reduce the distance traveled by vehicles and the costs for waste collectors, with smaller waste collectors benefiting the most. While full collaboration offers the greatest efficiency gains, even constrained partnerships improve operations. These findings highlight the potential for collaboration to enhance urban logistics and sustainability. ... Read more

Urban waterways play a crucial role in transportation, recreation, and logistics, yet their traffic capacity remains understudied compared to, for example, road and pedestrian traffic. This paper presents a novel methodology for estimating the capacity of urban waterway networks using empirical data, with a case study focusing on Amsterdam. Existing theoretical capacity models require calibration with real-world observations, which are often lacking. This study uses empirical data to develop a macroscopic capacity estimation model based on intensity-density relationships. Due to the distinct characteristics of urban water traffic, such as reaction times, risk adversity, regulations, and the dominant use case, traditional fundamental diagram models are not trivial to apply. Instead, an intensity percentile-based approach is introduced to define capacity, complemented by an important capacity marker that determines whether the estimated capacity represents the likelihood of the capacity being the actual capacity. The methodology is calibrated using expert-identified bottlenecks and validated through expert validation. Results indicate that the developed methodology shows promise for the capacity estimation of urban canals from dynamic data. However, further refinements are needed in the input data, the calibration of the model, and improving the methodology to account for missing key influencing factors to the traffic characteristics. Still this research provides a foundation for empirical data-driven waterway traffic research, offering policymakers insights for optimizing urban water transport operations. ... Read more

Global container trade continues to grow while climate policy tightens emission and efficiency requirements in major hub ports. European ports such as Rotterdam must accommodate higher volumes and stricter reliability expectations without proportional expansions of quay length or yard space. Within this context, intra-port inter-terminal container exchanges remain a bottleneck. Inland barges provide a low-emission alternative to trucks, but fragmented planning and long waiting times undermine reliability and the business case for modal shift. This thesis examines whether autonomy-enabled modular splitting of barge calls can improve operational performance at the Maasvlakte container terminals.

A port-wide discrete event simulation is developed with deep sea, feeder, conventional barge, and autonomous module services calling five terminals that share a dedicated module-crane pool. Current multi-stop barge operations are compared with scenarios in which barges detach short-calling modules under different barge–module mixes, sea-freight demand levels, module-crane inventories, and module capacities. Performance is assessed using throughput, turnaround and waiting times, berth occupancy, crane utilisation, and anchorage behaviour.
In the recommended 50-50 barge-module mix, modular splitting increases port-wide throughput by about 12% and reduces barge turnaround by more than half compared with current operations, while deep sea and feeder vessels remain largely unaffected. These gains arise from parallel module calls that use residual quay pockets more effectively. Hybrid fleets with around half of inland work carried by modules therefore provide the best compromise between higher throughput and manageable growth in vessel calls. Configuration experiments indicate that two dedicated module cranes per large terminal are sufficient under the tested loads and that medium-sized modules around 24 to 36 TEU perform robustly. Within the limits of the stylised simulation, the results indicate that modular intra-port services can improve inter-terminal operational performance, support port sustainability and modal-shift objectives, and provide guidance for the design of MAGPIE Demo 6. ... Read more

Increasing congestion and environmental pressures in urban logistics, alongside growing population demands, necessitate innovative solutions. This thesis tackles synchronized two-echelon routing problems within multimodal logistics, specifically exploring the potential of integrating waterborne transport. By developing optimization models and solution approaches for the comprehensive design and management of these systems—encompassing strategic location, tactical allocation, and operational routing and synchronization—the results promote a transition toward more sustainable and efficient urban logistics, ultimately fostering healthier, more livable, and economically vibrant cities. ... Read more

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

This project presents a comprehensive study on the development of Integrated Water and Land-based Transportation (IWLT) Systems for city logistics. The research addresses the growing challenges of urban traffic by proposing a decision model for multi-modal transportation systems that leverages waterways alongside traditional road networks. The problem is defined as a two-echelon multi-trip location routing problem with satellite synchronisation (2E-MTLRP-SS), incorporating capacitated vehicles, multiple depots and time constraints. A decomposition-based decision model is introduced, breaking down the problem into manageable sub-problems interconnected through synchronisation in time, space, and load. The decision model uses metaheuristics to be able to handle large-scale, realistic problems and provide feasible solutions for real-life applications. The model’s effectiveness is demonstrated through a case study in Amsterdam, showing the potential of IWLT systems to reduce congestion-related issues and improve the livability of cities. Different scenarios for the IWLT system are investigated, to assist Amsterdam's system developers in making design choices for implementation. The proposed decision model is widely applicable to multi-modal transportation systems all over the world. ... Read more