We present Sadcher, a real-time task assignment framework for heterogeneous multi-robot teams that incorporates dynamic coalition formation and task precedence constraints. Sadcher is trained through Imitation Learning and combines graph attention and transformers to predict assignment rewards between robots and tasks. Based on the predicted rewards, a relaxed bipartite matching step generates high-quality schedules with feasibility guarantees. We explicitly model robot and task positions, task durations, and robots’ remaining processing times, enabling advanced temporal and spatial reasoning and generalization to environments with different spatiotemporal distributions compared to training. Trained on optimally solved small-scale instances, our method can scale to larger task sets and team sizes. Sadcher outperforms other learning-based and heuristic baselines on randomized, unseen prob-
lems for small and medium-sized teams with computation times suitable for real-time operation. Wealso explore sampling-based variants and evaluate scalability across robot and task counts. To address performance limitations identified for large-scale problem instances, we experiment with using Reinforcement Learning (RL) to fine-tune the imitation-learned model. Both discrete and continuous RL formulations are explored, leveraging Proximal Policy Optimization (PPO). This allows for training on larger problem instances, for which optimal solutions for IL are infeasible to obtain. Our RL experiments provide insights into the comparative advantages and trade-offs of IL and RL methods. In addition, we release our dataset of 250,000 optimal schedules to facilitate future research. We include a detailed description of the instance generation and the Mixed-Integer Linear Programming (MILP) formulation used to solve them optimally. Furthermore, this thesis contributes a lightweight
simulation environment with visualization tools for benchmarking different task assignment algorithms.

This research investigates the value of transport mode flexibility in OCCUS supply chains, particularly under uncertain CO2 supply during the early phases of CCUS development. This study aims to develop a strategic decision support model that quantifies the economic and environmental benefits of transport flexibility within the supply chain.
The literature review provides two key insights. First, it identifies the essential steps in the CO2 sup- ply chain: CO2 is captured onboard ships, temporarily stored onboard in solvent, and transported to onshore facilities for regeneration and liquefaction, after which the liquefied CO2 is transported to per- manent underground storage. Second, the review reveals that no comprehensive studies currently model the full OCCUS supply chain while incorporating uncertainty in CO2 supply. Consequently, no established approaches exist to address transport flexibility under such uncertainty within this context. However, real options analysis has been successfully applied in land-based CCUS projects to value in- vestment and operational flexibility under uncertainty. Building on this proven methodology, the present research adopts a real options approach to quantify the value of the option to switch between transport modes.

This research applies the developed real options model to a case study centered on the Port of Rotter- dam. The supply chain model follows the Value Maritime approach: CO2 is captured onboard ships, stored in CO2 -rich solvent and offloaded at the Maasvlakte terminal. Transport from the port to the re- generation and liquefaction facility is done with containerized trucks, with the option to switch to barges. Two barge types are considered: the smaller CEMT-IVa and the larger CEMT-Va or a combination of the two. The LCO2 is subsequently transported by truck to an underground storage site. The model evaluates scenarios under both a fixed average CO2 price (€136/t) and a variable CO2 price increasing over time based on market forecasts.

The results indicate that while the average CO2 price is insufficient to achieve economic viability at any time and outcome, incorporating the option to switch from truck to barge transport adds value if CO2 supply grows. The option to switch to the smaller CEMT-IVa (€630345) barge shows greater economic benefits compared to the larger CEMT-Va (€131555), mainly due to its better alignment with expected supply volumes during the early implementation phase. The combined switching option (€632010) only yields a marginal additional value, as the larger barge is only required at the highest and least probable supply scenario.

Under the variable CO2 price scenario, the truck-only strategy reaches a positive total value by 2031 with a 30% probability. Introducing the option to switch to the smaller CEMT-IVa barge accelerates this to 2029 with a 55% probability, reflecting earlier and more frequent switching. The larger CEMT-Va barge lags behind, with switching and positive value only occurring from 2030 onward and at a lower 11% probability, indicating less frequent and delayed use.

The break-even price for the truck-only transport strategy is €184.21/tCO2 . The inclusion of switch- ing options reduces this threshold across all configurations: the CEMT-IVa barge option achieves a 4.35% reduction to €176.19/tCO2 , while the CEMT-Va barge provides a modest 0.92% reduction to €182.52/tCO2 . The combined strategy yields the largest reduction of 4.36%, lowering the break-even price to €176.17/tCO2 .

The introduction of the shipping container revolutionised global trade by significantly reducing handling costs, improving efficiency and enabling intermodal transportation. This development paved the way for the expansion of international trade and the development of highly interconnected global supply chains. Congestion, sustainability concerns, and vulnerability to disruption have become major obstacles. Recent examples of such obstacles are the COVID-19 pandemic and the blockage of the Suez Canal. Synchromodality has been identified as a potential solution for mitigating some of these concerns.

As a relatively new concept, synchromodality has mainly been studied at a theoretical level, focusing on its definition and potential. As the concept of synchromodality seems to gain attention from a broader public, more recent research has also focused on the more quantitative side. These quantitative studies primarily model the transport planning side of synchromodality. In these studies, some aspects in the supply chain have been overlooked so far, mainly the impact on critical infrastructure such as container terminals.

This research addresses this overlooked area in the existing academic landscape. Aspects such as the loading, unloading and stacking of containers are explicitly modelled in combination with synchromodal transport planning optimisation. This allows for an assessment of how synchromodality influences container terminal operations and how constraints and the dynamics at container terminals influence the transport planning. A multi-agent system approach is used as a framework for this model, as this presents a good option to model the different stakeholders involved in synchromodal transport.

The performance was analysed based on key performance metrics, including container relocation frequency, dwell times, and cost efficiency. The findings indicate that the integration of synchromodal transport planning with container terminal operations yields significant improvements. An iterative feedback loop between the transport planning agent and terminal agents facilitates more effective decision-making, leading to feasible transport planning, smoother operations, and improved resource utilisation.

Scenario analysis yielded further interesting results in terms of how a synchromodal planner would adapt to disruptions. The two most interesting findings are a decrease in the number of transshipments and a modal shift towards faster, more flexible, but also more expensive and more polluting transport modes.

In conclusion, this research demonstrates that the integration of synchromodal transport planning and container terminal operations improves the efficiency and adaptability of synchromodal logistics networks. Through these advances, this research contributes to ongoing efforts in the planning of synchromodal transport and the optimisation of container terminals, offering valuable information for both academia and industry.

This research investigates the optimal design of a charging infrastructure network for heavy-duty battery electric vehicles (HD BEVs) in the Netherlands. It addresses the challenges posed by the limited range of HD BEVs and the need for a robust charging network to support logistics operations. The study identifies key factors influencing fleet owners' charging behaviour and develops a qualitative decision model to estimate the demand distribution between private and public charging. Additionally, an optimization model is proposed to determine the optimal locations for public charging facilities, balancing the objectives of minimising infrastructure costs and maximising the number of trucks that can recharge.

Punctuality in railway transport is a critical concern for passengers, especially during unavoidable disturbances. Therefore, a sufficiently robust timetable is necessary. This thesis proposes a new timetabling method that minimizes passenger delay as the optimization objective, aiming to create a passenger-centric robust timetable. The model is implemented in the Dutch railway network in the Eindhoven-Den Bosch-Tilburg area to verify and validate its correctness and functionality. Experimental results demonstrate that the proposed model significantly reduces passenger delays when dealing with specific disturbances, and the improvement in robustness becomes more pronounced as the severity of the disturbances increases.

The ongoing global push towards energy transformation and decarbonisation underscores the urgent need for reducing greenhouse gas emissions, particularly in the maritime sector, which accounts for a significant portion of global trade and emissions. This study focuses on inland waterway transportation (IWT) as a key area for implementing zero-emission solutions, especially through the use of battery swapping technology. Given the average age and expected lifespan of vessels in the IWT sector, the need for immediate action is critical to make significant strides towards climate neutrality by 2050. This research delves into the complexities of multi-objective optimisation, balancing investment costs with operational efficiencies. It provides an in-depth sensitivity analysis of the impact of battery capacity, battery costs, the availability of docking station spots, and loading state of vessels on the overall system configuration.

The study emphasises the importance of carefully attributing and balancing weights to each objective to optimise network design and total costs. Moreover, the findings support the continued development of higher battery capacities and strategic resource allocation to enhance the efficiency and sustainability of IWT. The significance of this research lies in its potential to inform the deployment of renewable energy-powered, zero-carbon ships, contributing to global efforts to mitigate climate change and promote sustainable development in the maritime sector. Moreover, the insights gained in this thesis inform recommendations, aimed at guiding future research.

Semiconductor product development becomes increasingly challenging due to diminishing product life cycles, miniaturization, introduction of new physical principles, and new manufacturing processes. These problems are compounded in the absence of standardized development processes for the most complex semiconductor products like MEMS technologies, because the manufacturing of these products is often outsourced. Suppliers play a pivoting role in the realization of the product, from product design until process design and ramp-up. The supplier selection problem in this industry denotes the challenges in finding the right supplier while meeting all the technical, process and business requirements. The contribution of this research is in presenting how to develop a generic methodology for data-driven co-development. Thereby, this work presents a novel product development framework that leverages co-development and supply chain integration through data-driven decision-making. Co-development is reached through standardized methods for generating the required engineering output for supplier selection. Supply chain integration is introduced in the early stages of product development. This synthesizes with the outsourced manufacturing processes. Clustering algorithms are used to effectively shortlist suppliers based on their competences, and provide insights into supplier profiles and gaps. The latter is used to draw strategies for developing unattainable technologies. Using the framework, the required engineering output for supplier selection was generated in 77% less time while reducing information asymmetries between actors in the product development process. Furthermore, the framework made it possible to quantify decisions, allowed for supplier profile recognition and gap identification through its hybrid automated approach, and supplier shortlisting in 95% less time. This efficiency does not only showcase the immediate benefits of the proposed methodology, but also lays the foundation for future research towards a fully automated approach in semiconductor product development. The developed framework includes information flows between actors and steps in the product development process, their interfaces and demonstrates its added value and potential for a fully automated yet efficient future approach in semiconductor product development.

The development of the global COVID-19 pandemic from 2020 onward has had significant impact on the world and specifically the maritime industry. Striking examples were COVID-19 outbreaks onboard the Diamond Princess cruise vessel and the U.S.S. Theodore Roosevelt aircraft carrier at the start of the pandemic. Contagious disease management onboard large passenger ships remains a complex issue, amplified by the international character of the industry, confined environment and shared facilities. This report therefore presents an investigation into the effect of ship layout design, operational and behavioral measures on COVID-19 airborne infection risk onboard large passenger vessels. The novelty of this research lies with the integrated infection and crowd behavior model used to calculate agent-specific infection risk, incorporating guest and crew circulation through a passenger ship layout.

Quality control is considered an important process in manufacturing to minimise waste related to the manufacturing process. A way of performing quality control is with help of machine vision. Understanding all decisions that are to be made when designing a machine vision system for quality control is essential, but challenging. This research presents a method that aids decision-makers in the process of designing these machine vision systems for quality control so the efficiency of the decision-making process can be increased. The method results in a knowledge base and a tool that can both be considered as useful to decision-makers. The method was designed by following a Design Science Research approach and the method was applied on a use case in the automotive industry to prove that it addresses its goal.

The Automated Guided Vehicle (AGV) is the most commonly used piece of horizontal movement that uses electric power from an internal battery pack. With much focus on the charging strategy, current charge operation models lack the ability to model large scale terminal operations and to incorporate new types of horizontal movement. The objective of this research is to show the impact of recharge operations on terminal performances. A discrete event simulation model is based on the characteristics of APMT MVII to model the current impact. The results of numerical experiments show charging operations have a limited impact on the productivity of Lift-AGV. An exception is the change in battery type. Using a Li-ion battery lowers the need for recharging and increases total productivity. The findings show the importance of integrating modelling to accurately project terminal operations.

The growing demand for parcel deliveries is causing significant problems for freight logistics, particularly in cities. The increasing number of delivery vehicles leads to traffic congestion, high emissions, and rising costs. To address these problems, new and sustainable delivery methods must be implemented for
the parcel last-mile. However, estimating the impact of a different logistics system is complex, as it depends on consumer adoption of these new delivery methods.
This paper presents a new simulation model that captures and explores the interconnections between multiple delivery methods and consumer preferences for those delivery methods. Consumers’ reaction to the performance and availability of the delivery methods is simulated, next to knowledge progress via
word of mouth and familiarisation. The developed hybrid model uses system dynamics to simulate the evolution of consumer preferences and the last-mile delivery via van, self-collection, crowdshipping and drone delivery at an aggregate level, while an agent-based model provides input on the operational performance of the delivery methods. With this structure, consecutive
interactions can be simulated, and by that, data on consumer preferences and the delivery method operations can be obtained at multiple time points.
Results of a case study on the province of South Holland show that consumers change their preferences due to the introduction of new delivery methods. However, the vehicle kilometres and emissions of van delivery do not reduce at the same rate as the demand. The study also highlights zonal differences in last-mile services and the importance of delivery quality to consumers.

A monopile installation project consists of the whole process of loading monopiles to vessels, transporting monopiles to the wind farm and drilling monopiles in the seabed. Each installation project has different characteristics as the weather at the installation location, the distances to the suppliers and the vessels that are available differ between projects. Different project characteristics require different choices to be made when planning a typical installation project in order to optimally plan or reduce the expected cost of a project. One of these choices is determining which supply chain configuration will be used to get the monopiles from the supplier to the offshore wind farm. The choice of configuration is important as it determines the type of vessels that are required and if intermediate storage ports need to be rented, which requires initial investments.

The purpose of the research study is to assess the performance of different supply chain configurations. The performance of these different configurations is assessed on the key performance indicators project cost, project duration, utilization of the vessels and the waiting on weather duration. In order to assess the effect of using different supply chain configurations on the key performance indicators, 6 different experiments were performed within a developed simulation model.

Planners can use the qualitative insights of this report as a starting point for the choice of the preferred supply chain configuration when planning an offshore wind farm installation project. This will enable planners to make educated choices in the planning phase and give these planners insight into the different factors that are important in the supply chain of offshore wind farm installation projects. From a theoretical point of view, this report contributes to the sparse literature on the supply chain and logistical decisions in the field of offshore wind farm installation. Next to these insights, the weather module used to generate synthetic weather data improves Markov models discussed in literature and provides a solution to the problem of persistence that is present in conventional Markov models.

As connections between customers and suppliers become increasingly complex, the design of transport networks is under growing pressure to achieve efficiency. In response to this challenge, this paper proposes a service network design that takes into account both the forward flows from a production facility to a customer and the returning flows from customers to the production facility. The design allows for products to be shipped directly or to be consolidated and sorted at a depot. To assess the benefits of optimising both the forward and returning flows simultaneously in terms of network costs, the paper formulates an arc-based model and applies it to a real-life case. The results indicate that the network costs can be reduced by optimising simultaneously, as depots are built in the optimal locations and with the right capacity. This contrasts with the sequential method, which does not account for the return flows and can lead to suboptimal depot placement.

The Dutch railway network is a dense network with 7000 kilometers of railway tracks spread over only 42 000 square kilometers [1, 2]. Additionally, the railways are responsible for 1.4 million passengers every working day [1]. These factors make both the infrastructure and timetable of the Dutch railway network extremely intricate. On top of that, the amount of train trips is expected to grow with 40 % by 2040 [3] and with that the complexity of managing the railway network will increase even more. In the case of conflict in the timetable, action must be taken to resolve this conflict in order for the train operations to continue. In current Dutch practice this task resides with the train dispatchers. However it can be difficult for the dispatcher to oversee the situation when multiple trains get involved and taken actions will have consequences on yet another selection of trains. This increasingly complex task has resulted in a grown interest in so-called Traffic Management Systems (TMS), which are an intelligent systems that use a conflict resolution algorithm to find a solution to a conflict. A TMS can be used to support the train dispatcher...

Shipping companies aim to reach the climate goals by following the rules from the International Maritime Oranization (IMO), which includes reducing Scope 3 emissions. Maintenance is a great contributor to the Scope 3 emissions of a shipping company, especially the transportation of spare parts. The current state-of-the-art supply chain optimisation for spare parts is based on cost and risks. This study aims to find the potential influence of including Greenhouse Gas (GHG) emissions into the decision-making process for the logistics of spare parts. The element of the supply chain that makes it possible to plan when a spare part is needed is the maintenance policy. Planning in advance when a part is needed is possible for Preventive Maintenance (PM), which is therefore used in this research. A model can be created by limiting the amount of risk, optimising between the freight cost, the cost of GHG emissions and the cost of capital for alternative delivery locations, using brute force calculations. This model is applied to a case study of a chemical tanker from Stolt Tankers B.V., using available data on its job history and historical location data. From the case study follows that including the GHG emissions in the decision-making process adds an additional saving in emissions and cost with respect to the original situation. It can be concluded that the amount that is saved depends on the choices of the decision-maker. The model presented in this paper is a valuable tool for providing insights into the decision.

Efficient distribution is crucial in many industries today, especially in retail, where customer demand grows rapidly. The rise of online shopping and automation has led to a shift towards using Central Distribution Centers (CDCs). Retailers are part of this trend, which requires them to rethink how they handle their distribution. This study focuses on two main challenges: first, changing the distribution system to adapt to new CDCs, and second, dealing with the computational complexities when optimizing the distribution on a large scale. The aim is to design a large distribution system for the allocation of stores and the routing of vehicles. In this research, we introduce new elements to the mathematical model, which are mostly focused on the routing of vehicles. We also improve the computation by implementing a start solution, adjusting the parameters of the Gurobi optimization tool and including a callback function which bounds the run time. This model will determine the routing decisions for demand from several DCs to the stores and find the optimal vehicle routes. Through a case study at a company in the Netherlands, this paper provides insights into the changing world of distribution systems. It offers practical solutions for businesses trying to make their distribution processes better. The analysis revealed that consolidating demand at CDCs can lead to significant cost improvements while altering transport costs between CDCs and RDCs and adjusting order volumes can influence delivery strategies. Despite the absence of direct relationships between routing decisions, distances, and volumes, a logistic regression model provides guidance.

Long waiting times in ports for inland vessels to load and unload containers are a serious problem. The European project NOVIMOVE researches the inefficiencies and suggests solutions. One of the solutions for reducing waiting times in ports is the placement of Modular Mobile Terminals (MMTs). The focus of the research will be on the ports of Rotterdam and Antwerp. In this research assignment, three questions are addressed. The first question is: What are the criteria that a potential location must meet to be able to place one or more MMTs? The second question is: Which potential locations for the placement of MMTs in the ports of Rotterdam and Antwerp provide the highest time savings compared to the situation without MMTs? The last question is: What are the most suitable locations for MMTs based on the combination of the pre-defined criteria and the expected time savings? In order to be able to give answers to these questions, qualitative research will first be carried out on the basis of predefined criteria. Then, a quantitative research is done with a static time-saving model. The results of both the qualitative and quantitative research indicate that for the port of Rotterdam location 1 is the most suitable for the placement of MMTs. For the port of Antwerp, location 1 and location 2B are identified as the most suitable. Based on this investigation, a conclusion can only be drawn based on the qualitative and quantitative results. Follow-up research will be necessary to determine if there are changes when a dynamic time saving model is used. Follow-up research is also needed to include the economic aspects in the determination of the most suitable locations.

On-demand ridesharing might be an effective solution to reduce traffic congestion and, as a result, reduce emissions to mitigate environmental issues. However, travel times are more uncertain in ridesharing services compared to non-shared transit services due to their shared nature. For example, at one moment, the users are informed that the travel time takes five minutes, a moment later, a request is added to the trip and the users are now confronted with a travel time of ten minutes instead. This uncertainty in travel time is called unreliability. Unreliability is perceived as inconvenience by the users and it has a negative impact on the users' travel habits regarding ridesharing service.

Therefore, this research provides a novel solution to deal with this problem while mitigating the negative effects on the other quality-of-service indices, e.g., waiting time, rejection rate, and delays. This is achieved by providing two indicators to each new user: (1) the possibility that an additional request will be added to the trip that negatively influences the trip duration of the user; and (2) a prediction of the magnitude of a possible delay for whenever the case occurs that a new request will be added to the trip. These indicators are based on predictions, created by shareability shadows which determine the number of requests that might be added to the trip, and implementing a simple time-series demand forecasting method named exponential smoothing. The shareability shadow makes predictions by confining the regions of ridesharing opportunities by analyzing the travel constraints of the involved users. The solution is validated using a state-of-the-art routing and assignment method and a test case of Manhattan, New York City. With the formulated measure, on average, 58% of the requests receive a correct prediction about the possibility of a negative change, 17% receive an indecisive prediction, i.e., a prediction stating that the possibility of a negative change is "medium", and 25% of the requests receive an incorrect prediction about the possibility of a negative change. Moreover, the measure successfully provides a predicted magnitude of the travel time increase with a 95% confidence interval. 

Optimizing delivery routes is a well-researched topic, however, most of the classical approaches do not incorporate preferences of drivers, as those approaches focus on minimizing the time or distance of the routes. As a result, the actual driven route of an experienced driver often deviates from the proposed route since the drivers have tacit knowledge about the real-life conditions of the road network. Amazon proposed a challenge to learn a delivery route planning strategy from historically driven routes and thus incorporate this tacit knowledge.
In this thesis, we will tackle the challenge using data-driven inverse optimization to learn the zone sequencing patterns of drivers. The zone sequences of expert drivers are assumed to be the solutions to a traveling salesman problem (TSP) in which the weights represent the preference of a driver to use a certain edge. The values of the weights will be learned through inverse optimization. Our final approach achieves a score that ranks 4th out of the 48 models that qualified for the final round of the challenge.

Quay cranes on modern container terminals have the possibility to lift two 40ft. containers side by side. In order to be able to perform this type of lift, the crane must be equipped with a tandem spreader. As this type of spreader cannot be used for performing certain lifts, such as the handling of hatch covers, switches between single spreader and tandem spreader are inevitable. However, there is no method in current literature nor in practice to determine when the spreader changes should be performed. Therefore, the aim of this research is to develop a model that calculates the optimal moments to switch spreaders. As the performance of tandem lifting depends on the cycle times, a cycle time prediction model is developed first. This Artificial Neural Network model predicts the cycle time based on the type of lift, the actual position of the container(s) on the ship, the weight of the load and the current wind speed. The predictions yield a Mean Absolute Percentage Error of less than 11%. Afterwards, the cycle time prediction model is used to develop an optimal spreader switching strategy model in the form of a Mixed Integer Linear Programming model. A case study, in which several different bay layouts of a container ship need to be handled, shows a decrease in handling time of up to 22% compared to the traditional spreader switching strategy.