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.

Amid increasing pressure to decarbonize logistics operations, hybrid distribution strategies that combine direct plant shipments with conventional two-echelon networks are gaining attention in the fast-moving consumer goods (FMCG) sector. This thesis investigates the trade-offs between logistics costs and environmental performance in such hybrid distribution networks through a case study at Procter & Gamble (P&G). A novel flow-based adaptation of the two-echelon vehicle routing problem (2E-VRP) is developed and implemented within a multi-objective optimization framework. Applying scalarization and ε-constraint methods, the analysis shows that while direct shipments from plants can lower logistics costs, they do not necessarily improve environmental performance. When emissions are directly minimized, two-echelon flows are often preferred due to their higher vehicle utilization. Sensitivity analyses reveal that carbon pricing alone has limited influence on routing decisions, whereas optimizing emissions under cost constraints leads to more sustainable but more centralized logistics patterns. These findings indicate a structural misalignment between cost-efficient and environmentally optimal strategies. The study underscores the importance of route-specific eligibility rules and operational constraints, such as minimum vehicle fill rates, to support logistics decisions aligned with sustainability goals.

Winter weather can significantly disrupt airport operations, leading to flight delays, cancellations, and substantial economic losses. Proactively forecasting airport capacity during snowfall is essential to ensure timely flight cancellations and maintain operational continuity. While aircraft stand availability is a key determinant of overall airport capacity, data-driven insights into this process are lacking. This study addresses this gap by developing a forecasting model to predict aircraft stand snow removal capacity, using Amsterdam Airport Schiphol as a case study. A classification algorithm is employed to extract operational cleaning activities, cleaning, traveling, and idle periods, from historical radar data, enabling the assessment of current cleaning performance. Based on these insights, a simulation-based model was developed to forecast stand cleaning capacity under anticipated operational and environmental conditions. The model’s flexible design allows users to adjust scenario-specific input parameters, such as team availability and capacity restrictions, facilitating tailored scenario analysis. By simulating the alignment between available cleaning capacity and inbound flight schedules, the model identifies potential queuing situations and facilitates the assessment of additional capacity restrictions, supporting more informed capacity planning.

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.

The current operational processes in an airport handling system (BHS) are not suitable for the implementation robots for the loading of baggage. This study aims to contribute to the implementation of new operational strategies, named batch-based pull approach, in a BHS to create a more automated and efficient operation. In this work a deep reinforcement learning (DRL) model is developed that can generate a baggage loading planning in real time for a baggage handling system in the dynamical operating environment to enhance robotic loading. The loading operation was formulated as a Markov decision process, and proximal policy optimization (PPO) algorithm was used to train the DRL agent. The DRL was compared with Vanderlande’s heuristic and tested on a case study of Brussels Airport. It automatically learned how to make baggage load planning decisions in simulations of a real-world BHS, generally loading more bags with a robot, but used more load units (LU), highlighting a trade-off between robot use efficiency and LU usage. This study demonstrates the potential of using deep-reinforcement learning for real-time loading planning in dynamic baggage handling systems with loading robots. However, more work is needed for consistent performance and real-world implementation. The results obtained are strongly related to the current model formulation, necessitating
additional research to gain more insight into the operating performance. This research serves as a proof-of-concept for future applications.

The adaptation of battery-powered terminal trucks (BTTs) in rubber tired gantry crane terminals (RTG) will affect terminal productivity and costs. This research tests the performance of different charging strategies and compares costs. This is done with the use of a discrete-event simulation model including the energy consumption and charging of BTTs. Five charging strategies are presented: Out of Operations (OOP), Centralised Fixed Threshold (CFT), Decentralised Fixed Threshold (DFT), Decentralised Pre-Emptive (DPE) and Decentralised Opportunity Charging (DOP). The scenarios were tested in a terminal operations model of an RTG terminal, with configurations varying in charge lane location and numbers. Based on productivityOOP and DOP performed similarly to a diesel-benchmark of the same fleet size. It was found that charge location has little effect on overall terminal performance when the charge duration is large. Pre-Emptive charging has a positive effect on terminal productivity through high charge lane utilisation and little time loss associated with waiting on available chargers. Due to lower energy and maintenance costs, all battery-powered alternatives are more cost-effective than diesel-powered alternatives.

Accurate forecasting in Reverse Supply Chain (RSC) management is crucial for the semiconductor industry, particularly for companies like ASML, which must efficiently manage the return flow of defective machinery parts. This study addresses key gaps by developing and evaluating time series-based forecasting models tailored to ASML's RSC. Using a modified ABC-analysis, parts were categorized based on defect frequency and economic impact, focusing on the most critical components. The research applied and optimized models including SES, ARIMA, ARIMAX, and LSTM, using five years of historical defect data. The analysis showed that LSTM models excel in high-frequency (weekly) forecasts for parts with frequent early-life defects, achieving an average mMAPE of 26.32\%. ARIMAX models performed best for lower-frequency (monthly) data, particularly in sparsely represented classifications, with mMAPE as low as 4.31\% to 10.80\%. Despite a higher mMAPE of 85.42\% in one outlier, ARIMAX emerged as the most balanced model, offering a practical trade-off between accuracy and computational efficiency. Furthermore, the study highlights the computational efficiency of ARIMAX, which, although more demanding than SES, provided a favorable balance, with ARIMA and LSTM being more resource-intensive. These findings demonstrate ARIMAX's suitability for long-term forecasting and broader trend analysis, making it the preferred model for ASML's RSC. This research provides a robust, data-driven framework that enhances inventory management and capacity planning, making it possible to predict return flows with low error rates on a monthly basis, particularly in high-tech industries where many defective parts are returned. Future research should incorporate additional dynamic variables, explore hybrid models, and refine data splitting techniques to further improve predictive accuracy and support sustainable supply chain operations.

Due to recent legislative changes and shifts in global markets, the task of ship design has grown considerably more complex. The integration of groundbreaking technologies, such as remotely controlled or autonomous vessels, poses additional challenges during the concept design phase, primarily due to a lack of familiarity and experience with these technologies. Simultaneously, these advancements make designing a vessel that can consistently meet its objectives throughout its entire life cycle increasingly difficult.

In the early stages of the design process, designers often face the challenge of making critical decisions without fully understanding their potential consequences. This situation is compounded by the increasing costs associated with these decisions and their influence on design freedom. Consequently, the conceptual design stages become especially crucial, as making changes later in the shipbuilding process can result in significant cost escalation for the project.
Considering the various factors impacting the ship design process, there arises a necessity for a novel methodology empowering designers to assess the operational capabilities and performance of conceptual designs during the early stages of ship design.

The presented report introduces the project titled 'Validating Operational Scenarios Through Simulation' and presents findings from a literature review on the utilization of simulation in early-stage design processes.
The introduction chapter begins with an initial description of the problem to be addressed, introduces the involved company, and outlines the motivation behind the project. Following the introduction, the results of the literature review are presented.
The second section aims to answer the first set of research questions by examining the current implementation of simulation software within design processes and presenting available options for software to be utilized in the project. The process of selecting the software is then outlined, along with the conclusions drawn from the research conducted.
Subsequently, the formulated methodology is described, containing all steps necessary to structure the workflow during evaluation. The following chapter details the anticipated method of introducing the formulated method into existing design processes, including the main method of evaluation through simulation and the method of integration, using a case study example.
Finally, the last chapter concludes the report by providing considerations from the entire process and presenting recommendations for potential improvements of the formulated process.

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.

This paper investigates the optimisation of Automated Storage and Retrieval Systems (AS/RS) in warehousing by minimising performance losses during partial downtime. Given the increasing automation in logistics, AS/RS systems play a pivotal role, yet the operation of those systems during partial downtime remains a topic ignored in literature. This research fills this gap by exploring the effects of partial downtime in AS/RS through a reusable Discrete Event Simulation model which was developed in Python. This model incorporates the influence of both upstream and downstream systems, a characteristic notably absent from the limited number of publicly-available AS/RS models. Collaborating with Jumbo Supermarkets, the study utilises their highly automated distribution centre with an Order Consolidation Buffer housing 4 dual-crane AS/RS units as a case study. The study identifies operational policies to mitigate partial downtime effects, developed for scenarios with one or both cranes down within an AS/RS. Results suggest strategic workload distribution adjustments among AS/RS can significantly reduce performance degradation, particularly during high workload periods. After comparing both scenarios, it was concluded that for most scenarios, it is beneficial to keep operating the remaining crane when a crane breaks down, even though this slows down repairs. Overall, this research offers insights into parallel AS/RS dynamics under partial downtime and provides practical guidelines for effective operations.

Efficient production capacity planning is essential for industries facing uncertain demand, such as semiconductor manufacturing. This thesis explores machine learning models to improve demand forecasting and optimize production capacity, focusing on the ASML Field Service Hub. Models including XGBoost, Random Forest, and ARIMA are evaluated to predict demand across multiple product flows under uncertainty. These forecasts are integrated into an optimization model to minimize costs related to overcapacity, undercapacity, and labor fluctuations.
The proposed framework demonstrates substantial improvements over ASML’s current benchmarks, reducing forecast errors by up to 37.4% and simulated costs by 48.3%. Additionally, robustness testing was conducted using jitter tests, ensuring the model’s stability in the face of noisy data. The explainability and feature importance of the machine learning models were investigated using SHAP values. The study’s results highlight the model’s adaptability, offering a valuable tool for improving capacity planning in various industries dealing with demand volatility.

With the development of society and the requirements for clean energy, offshore wind farms (OWFs) that can generate steady and continuous electricity have been built. With harsher weather conditions and more powerful wind resources, the wind turbines at offshore wind farms are significantly larger than those at onshore wind farms. The complex weather conditions and mechanical structure of wind turbines pose problems for their operation and maintenance (O&M).
The cost of vessel chartering significantly contributes to the overall cost of operating and maintaining an offshore wind farm. By selecting the optimal fleet mix for executing the maintenance, the vessel chartering cost can be reduced, reducing the operation and maintenance costs of the offshore wind farm. This reduces the levelized cost of energy from the offshore wind farm. This report developed a simulation and optimization model based on mixed integer linear programming to determine the opti-mal fleet mix for executing the maintenance tasks by minimizing the vessel acquisition cost. The Monte Carlo simulation is implemented to statistic an optimal strategy for chartering the vessels.
Chapter 1 introduces the offshore wind farm (OWF) and its operation and maintenance (O&M) activi-ties. Chapter 2 provided a literature review on the latest progress of the offshore wind farm’s operation and maintenance. Chapter 3 provides a simulation model for component wear, maintenance require-ment generation, and maintenance task execution. The process of optimization is explained in detail. Chapter 4 presents the mathematical model of the optimizer, which arranges the vessels and executes maintenance tasks. Chapter 5 presents a case study based on the latest progress data. The Monte Carlo simulation yields the optimal initial purchased fleet mix derived based on the Monte Carlo simu-lation. Finally, Chapter 6 gives the conclusion and recommendations for future studies.

The planning of railway operations is a very complex process, in which timetables and other logistical plans need to be both reliable and robust to effectively prevent and cope with disturbances. However, research in the robustness of initial stabling plans, designed in an earlier planning phase, during the following planning phases, has been lacking, with the models created in research in the Train Unit Shunting Problem (TUSP) being generally deterministic in nature, even though stabling plans could quickly become infeasible when the stabling demand in the form of arriving and departing trains changes.
This thesis therefore proposes a definition of the robustness of an initial stabling plan to changes in the stabling demand, such as changes in train lengths, as well as provide an assessment method of said robustness. The created Robustness Assessment Model (RAM) first generates an initial stabling demand and stabling plan, then performs a Monte Carlo simulation to generate a set of stabling plans created for variations of the initial stabling demand, based on changes in stabling demand such as train length. Finally, the RAM estimates the robustness of the initial stabling plan by analysing the differences between the initial and these generated stabling plan variations and how efficiently the initial plan is able to change to these plans to optimally facilitate the variations of the initial stabling demand.
Running this model across three locations, each with three capacity utilisation scenarios, has shown that the model is able to estimate the robustness of an initial stabling with acceptable confidence, and furthermore is able to give insight into how capable the stabling plan creation method is in generating a robust solution. Furthermore, the RAM can also be used to investigate patterns in stabling plans which could predict the robustness of said stabling plans. Current shortcomings to the RAM are its relatively long runtime, the simple stabling plan creation process used in the RAM, and that only one-sided stabling yards without servicing scheduling have been incorporated. Further research is therefore recommended for extending the RAM with other stabling yard layouts and the scheduling of services, as well as improve the stabling plan generation process to take more constraints into account.

Fast-Moving Consumer Goods (FMCG) companies face the critical challenge of selecting appropriate locations for their distribution centers to maintain operational efficiency and customer satisfaction. This thesis introduces a structured 5 step of phased method to address the distribution center site selection problem, considering various company-specific strategic objectives. The method incorporates determining factors identification, constraints selection, requirements outlining, alternatives generation and alternatives evaluation. A morphological chart is used in the alternative generation phase to create a range of potential locations alternatives. The Multi-Criteria Decision-Making (MCDM) method is applied for the systematic evaluation of the alternatives. The thesis suggests that expanding the set of determining factors, incorporating a broader range of stakeholder inputs and further enriching the alternatives through diversified means could enhance the method’s applicability and accuracy. This method offers FMCG companies a robust decision-making tool for strategic distribution center location selection, catering to individual business objectives and market dynamics.

The use of petroleum-based fuels in the shipping sector creates harmful emissions that need to be reduced to reach climate goals. Ammonia is being considered as a possible fuel to reduce emissions, but there exist several challenges that prevent its implementation. Research into these several of these challenges is being done, but there is little research into the bunkering process of ammonia. Therefore, the purpose of this research is to identify an installation to bunker ammonia that is suitable for ports that handle sea-going vessels. In order to do this, requirements, functions and alternatives are proposed for this system. Subsequently, these alternatives are evaluated through an ordinal analysis and a decision is made on a configuration through the Best Worst Method (BWM). To do this, Stakeholders from the port of Rotterdam are surveyed and used as input for the BWM. A viable configuration is found that adheres to all requirements. Finally, it is concluded that the configuration is applicable to multiple ports due to the general prioritization of safety, robustness of the result and similar results between stakeholders with different secondary priorities. Furthermore, the effectiveness of mitigation strategies implies sufficiently small risk contours, to allow ports that are in close proximity to vulnerable objects to be able to build an ammonia bunker installation as well.

A relatively new concept within demand management for time window assignment is green labeling; time windows which contribute to improving the routing performance in terms of sustainability. Certain time slots are given a so-called green label. From literature, limited information is known about the choice preference with regard to green labeling and its impact on routing performance. Via choice modeling, certain attributes are estimated based on a data set from an e-grocer. Together with a beta estimate on green labeling from literature, the effect of green labeling on choice behavior is analyzed. The results are used for route optimization where the effect of various static and dynamic approaches are tested. Results show that dynamic green labeling has the most promising effect on routing performance in terms of costs and sustainability. Especially when customers are more nudged toward the largest time windows in less popular day parts. The CO2 emissions decrease by 127.4 CO2 per order and the costs decrease by 1.03 euro per order on average. These results are based on the specific situation of the e-grocer in the case study. With regard to choice modeling, this research is limited to only time window characteristics. The improvements in terms of costs and sustainability per green labeling approach, based on the attributes resulting from real-data choice modeling, contribute to more knowledge on the effect of green labeling on routing performance.

The research on sustainable energy is growing, among which, wind energy catching growing attention and the potential has been supported by more and more countries. Compared with onshore wind farms, offshore wind farms have more advantages including the abundant wind resource at the offshore location and more possible construction areas. While for an offshore wind farm, the operation and maintenance cost is the most significant part and fleet management contributes a lot to it.

In order to optimize the fleet size and mix problem for an offshore wind farm based on a simulation method, this thesis has performed a few research steps. Firstly, a literature view on the modeling methods of fleet size and mix problems for offshore wind farms is finished. Different modeling methods and different factors considered in the model are viewed. Then, two simulation models, the open-loop simulation model and the feedforward simulation model, are introduced, including the model inputs, model agent and process, and model outputs. Afterward, the simulation-optimization methodology is introduced and the optimization algorithm used in this research is introduced. Next, one case study using two models separately for a long-term optimization and a short-term optimization is executed and followed by the results of these two simulation models as well as the comparison of the results from them.

This thesis aims to combine the optimization method with a simulation model for offshore wind farms, which can be regarded as a decision support tool for fleet size and mix problems and is expected to be a practical technology for the operator/researcher of the offshore wind farm in the future.

Since the commercialisation of air travel, demand for air transport is increasing. Optimal system capacity utilisation at airports has therefore become crucial. As available resources such as land, buildings and human are scarce and airline tickets need to be financially affordable, airports optimise their system while maximising capacity within the constraints of safety and reliability. Regional airports, that often provide remote stands which are not connected to the terminal building, need to obey to and cope with an incremental number of operational planning rules in order to provide safe and secure apron operations. Current operational apron planning procedures imply a remote stand allocation that results in a corresponding gate assignment and a passenger apron transfer including a mode choice between pedestrian and bus transport. This study will add to research by fundamentally changing this approach into an integrated optimisation procedure. This entails simultaneously optimising the remote stand allocation, gate assignment and apron passenger transfer by means of an integrated mixed integer linear programming model. The model objective is to minimise the total apron transfer duration and therefore apron utilisation time. Experiments on a Rotterdam The Hague Airport case study show that the integrated optimisation procedure provides the potential of an average apron passenger time saving of 18% given the optimised stand-gate-route allocations. As a result of remaining model limitations, this research leaves the opportunity for the integration of apron aircraft movements to include the impact of jet blast. Furthermore, the potential of a rolling horizon approach could be researched to include system uncertainty over time and the potential to update the operational planning.

Until recently, tenders in Europe were awarded to wind farm developers based on the highest auction prices or the lowest subsidized bids. The wind industry has suggested that non-price-related criteria should be considered for tenders, like plans to reduce greenhouse gas emissions. As a result of the sustainable tender criteria, greenhouse gas emissions are a relatively new KPI for offshore wind farm developers.

Studies have shown that the costs and wind farm availability are sensitive to the fleet composition and were commonly used as criteria in offshore wind fleet optimization models. Offshore wind greenhouse gas emissions were shown to be sensitive to the offshore wind fleet composition as well but thus far not used as criteria for fleet composition decision-making. This study aims to develop an offshore wind O&M multi-objective fleet optimization model that includes GHG emissions as the third criterion for the fleet composition. The model is rendered as a deterministic MIP problem. An epsilon constraint method-inspired approach is proposed to reformulate the multi-objective into a set of perturbed single-objective models, which can be solved using a commercial MIP solver.

This thesis explores waiting and rebalancing strategies in urban waterborne transport, intending to improve the efficiency and operational performance of water transportation systems. The study is based on a review of the literature on waterborne transport and waiting and rebalancing strategies. This is followed by a case study where the found strategies will be implemented for the water taxi case in Rotterdam.

The literature review provides an overview of the current state of research on waterborne transport, highlighting its potential as a sustainable and efficient mode of urban mobility. The review also identifies waiting and rebalancing strategies as key factors for improving the performance of water transportation systems. Waiting strategies involve managing the distribution of slack time in the route of the vehicle, while rebalancing strategies aim to optimize the use of resources by redistributing vessels across the service area.

The implementation of waiting and rebalancing strategies on the water taxi system in Rotterdam provides a practical example of how waiting and rebalancing strategies can be implemented in a real-world context. This study analyzes the performance of the water taxi system using the actual data provided by Flying Fish. The analysis results in a clear overview of the requirements of the experimental setup that is needed to evaluate the strategies in this specific waterborne transport environment.

A detailed description of the simulation model is given. The simulation model is based on a mobility-on-demand simulation model. Multiple modifications needed to be made in order to fit the model to the water taxi scenario. After a working simulation model has been created, a benchmark is created to make a comparison between the strategies.

The experiments reveal that waiting and rebalancing strategies can be effective in improving the performance of water transportation systems, but their implementation requires careful planning and coordination. The study identifies several strategies that improve the systems performance in terms of one factor, but deteriorate other performance factors. The trade-off between pick-up delay on one side and the vehicle usage and traveled distance on the other side is a recurring theme when looking at the results of the strategies.

Overall, the findings of this research contribute to the growing body of knowledge on innovative solutions for urban mobility and provide a basis for further exploration and implementation of waiting and rebalancing strategies in waterborne transport systems. The study highlights the potential of these strategies to improve the efficiency of urban transport and underscores the importance of considering waterborne transport as a viable and valuable mode of urban mobility.