Manufacturing companies today face increasing global competition, labor shortages, and pressure to maximize efficiency. Automation is a key strategy for maintaining competitiveness, but determining the optimal level of automation in production line design is complex. Companies must weigh the strengths of human operators, such as flexibility and problem-solving, against the consistency and efficiency offered by machines. Although these decisions are crucial, there is currently no comprehensive methodology that allows companies to systematically evaluate the trade-offs between different levels of automation. This evaluation should integrate both quantitative and qualitative factors during the early design stages of a production line. To achieve a comprehensive understanding, the following key factors have been identified to systematically assess the trade-offs associated with automation options: production performance, cost, quality, flexibility and work environment.

This research adopts a design science methodology to develop and refine a stepwise evaluation framework for automation trade-offs in production line design. The methodology begins with a thorough documentation of the production context, requirements, and constraints. It then systematically generates low, medium, and high automation scenarios for each production step, considering both cognitive and physical tasks. These scenarios are conceptualized using structured models (IDEF0 diagrams) and layout maps, followed by the development of dynamic models, to quantitatively assess system dynamics, throughput, and bottlenecks. Each scenario is evaluated across the five key factors using both model outcomes and expert review sessions, enabling a holistic comparison. The methodology is validated through a practical test case at Quooker, a company developing a new production line, and refined through feedback from industry experts and academic reviewers.

The final version of the designed automation evaluation methodology provides a structured and comprehensive approach for evaluating automation trade-offs in early stage production line design. Its main strengths include the integration of both quantitative and qualitative assessments, the explicit consideration of five key factors, and the ability to generate actionable insights that broaden perspectives beyond intuitive thinking. Application to the Quooker test case demonstrated that the methodology supports informed, data-driven discussions and enables the identification of balanced automation scenarios. Feedback from industry and academic experts confirmed its practical relevance and alignment with industry needs. However, limitations include the time-intensive nature of dynamic modeling, the dependency on expert input, and challenges in quantifying certain factors at early design stages. Despite these constraints, the methodology offers a robust foundation for making informed automation decisions and structuring early design discussions in manufacturing environment.

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.

Port systems face numerous challenges, including limited storage capacity at terminal yards, congestion in road port access due to overwhelmed infrastructure, and inefficient inter-terminal transportation. Close dry ports, situated between 10-40 km from the port, offer a potential solution, but lack flexible and reliable transport connections that efficiently handle transportation without adding extra handling moves. Recent technological advances in rail systems, particularly fully automated magnetic levitation (maglev) cargo shuttles, provide promising solutions for connecting dry ports with seaport terminals.

This research explores the integration of maglev technology into port logistics, focusing on connecting a dry port terminal to seaport terminals, with a direct connection to the berth. Using the Transport System Bögl (TSB) Cargo system as a reference, five different designs were developed for integration. These designs required a redesign of the dry port and terminal yard, as well as the design of the system’s berth connection and connections between all terminals.

Simulation modeling using Siemens Tecnomatix Plant Simulation software evaluated the performance of these designs under various scenarios based on Port of Hamburg demand input data. Results showed that all designs improved median berth times compared to the German benchmark of 18.96 hours, with Design 5 demonstrating the best performance together with Design 2.

Recommendations include continued collaboration with port authorities through simulation case studies, serving as proof of performance for potential integration projects, and exploration of collaboration opportunities with smaller scale ports facing space availability and road access issues. Further research should expand the model to accommodate new export container loading requirements and include all terminals, while studying new algorithms to balance the volumes and requests of all three terminals. This research demonstrates the feasibility of integrating maglev systems into port logistics, challenging the ’status quo’ and opening up new possibilities for improving port operations. Through a systematic approach, this study offers valuable insights for the integration of maglev technology into port logistics, paving the way for future advancements in the field.

This research focuses on optimizing the performance of a hybrid warehouse system that combines automation with manual picking processes. The study utilizes a case study of an online grocer, Picnic, and investigates the allocation of orders within a buffer and the configuration of the manual picking process.

The research identifies the key performance metrics for the system, including the number of late totes, average sojourn time, average stack throughput time, and picker productivity. Several factors impacting the system's performance are examined, such as buffer lane selection and group formation strategies, minimum and maximum group sizes, number of workers, pick times, and input data.

Through comprehensive experimentation and simulation modelling, the study reveals that prioritizing the deadline of totes for buffer lane selection and the IPB time for group formation results in the best system performance. The research also suggests that the configuration of the manual process should consider the group size should be adjusted based on the expected workload.

The study identifies the consolidation stations as a significant bottleneck in the system, and repairing them promptly on busy days is essential to maintain performance. The research provides practical implications for Picnic, emphasizing efficient buffer allocation and group formation strategies.

In conclusion, this research offers valuable insights into enhancing the performance of hybrid warehouse systems. Companies like Picnic can improve operational efficiency and customer satisfaction by optimizing buffer allocation, buffer lane selection, and group formation. The findings from this research can serve as a basis for further optimization and decision-making in similar warehouse setups.

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.

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.

Climate change is triggering an ever-growing demand for renewable energy. The U.S. is still far behind Europe when it comes to offshore wind energy. They have made ambitious plans to reach 30 GW in offshore wind energy by 2030 while currently 42 MW is installed. One of the main challenges in the U.S. is the installation method since a legislation called the Jones act prevents the usage of European installation vessels for shuttling (the conventional method). Building a Jones act. compliant installation vessel is a large investment which comes with risks and long lead times. Feedering is an alternative strategy, but barely any research on it is available. Here, a feeder vessel sails back and forth from the storage port to the (non-Jones act. compliant) installation vessel to supply Wind Turbine Generator (WTG) components. These components need to be lifted from the floating feeder in order to be installed. In the literature, this step is deemed to be the riskiest. However, barely any technical research is available with regards to the lift-off.

In the first thesis of this double degree program, a lift/installation sequence called the direct installation method is deemed to be highly interesting with respect to the logistics and costs. However, this research misses a technical study in order to understand if it is technically reachable to directly install these components. In this offshore engineering thesis, a barge is used as a feeder vessel and tower segments of a 20 MW WTG are chosen as the to-be lifted components. This research focuses on the pre-tension phase before the lift-off. This contains the steps where the crane of the installation vessel is already attached to the tower, pre-tension is building up and the release of the sea-fastening. Here, pre-tension is a percentage of the load that is taken in the crane before the lift-off. This research aims to increase the understanding of whether a tower can be released safely on the floating barge and what can be done in order to realise the idea of a direct installation method.

Frequency, as well as time-domain simulations, are used to investigate the problem. The results show that snap loads occur for pre-tensions up to 10\%. From 30\% and higher, the tower will start toppling. Toppling is initiated due to the inertia of the large tower segment when it is released from its sea-fastening. Toppling the tower is not allowed since this could damage the tower itself, the sea-fastening and/or other components on deck of the feeder. Increasing the limiting wave height is a must in order to make the direct installation method more practicable. This can firstly be done by using more tower segments. Therefore, reducing the size of each segment. Another option is to implement a motion compensation tool that decouples the motions of the feeder and the tower. The third option is to design a seafastening system that reduces the moment after the release, a temporary counteracting toppling system. All in all, can be stated that safely releasing a 20 MW tower segment on a floating barge is highly challenging and more research is required to solve the issues that are found in this research. This is necessary to allow the direct feeder method to be used for future offshore wind installation projects in the U.S.

Collaborative Innovation can serve to elevate the production potential of firms and organisations as well as preparing them for the future. This process of two organisations collaborating together is very complex and becomes even more difficult when organisations are large and very different in their organisational culture or function. This research describes the design of a tool to aid Collaborative Innovative Capacity in public - private organisational partnerships. The research is based on a case study within the Dutch national rail infrastructure management organisation and focuses on the relationship between this organisation and its maintenance contractors. Through literature research and semi-structured interviews the studied situation is found to consist of two parties trying to innovate together using an imperfect collaborative system. Their separate, struggling relationships with this system are not shared or understood by the other party. This gives rise to a wall of misunderstanding. The designed tool is based on the assumption that through better understanding of the barriers which are present in the other party’s struggling relationship with the collaborative system, the wall of misunderstanding can be lowered which in turn increases Collaborative Innovative Capacity in the public - private organisational partnership. This tool takes the form of a serious game which presents players with scenarios which (potentially) hinder them in their game objective. These scenarios are representations of real-world barriers to innovation. Through confronting players with scenarios which are related to another organisational role than the one they occupy in their professional life, they should gain new perspectives about the struggling relationship of the other party with the collaborative system. Thereby lowering the wall of misunderstanding and increasing Collaborative Innovative Capacity. The results of the design and preliminary testing show that the developed serious game does have an effect in the form of gained attitudes towards the importance of sharing different experienced barriers towards innovations with one another. However measurements show no actual newly gained insights into practical barriers which were not already known to the players. While further research, more extensive testing and measurements, and further specification and expansion of scenarios can be undertaken to increase the value of this tool, after reconsideration a revised design objective is formulated which focuses on the interaction between the participants and relaxes its rigid view on the wall between them. This could yield a tool which is more in tune with the essence of the problem which would also cause it to be much simpler in nature.

To better govern inventory control of spare parts in tandem with scheduling of maintenance operations in a railway setting, a Railway Spare Inventory & Maintenance Scheduling (R-SIMS) model is developed. To suit the railway industry setting the model includes an inventory control strategy which envokes continuous monitoring and a reorder level as well as an order up-to level. Moreover a condition based maintenance (CBM) strategy is used with periodical inspections. These strategies are combined in a simulation model which assumes stochastic step-wise deterioration of parts as well as stochastic lifetime lengths to predict the expected cost per part per period. The three maintenance scheduling and spare inventory decisions to be minimised in terms of their resulting costs are 1) when to replace parts based on their condition, 2) when to buy new spare parts and 3) how many spare parts to procure at each order. These decisions are represented by values of the three decision variables: the CBM threshold L_p, the reorder level s and the order up-to level S. The minimisation of these decision variables is performed through surrogate modelling, a branch of machine learning optimisation techniques which deals with complex black-box functions. It does so by running experiments and estimating a surrogate function which in turn is optimised mathematically. Multiple surrogate modelling methods are compared in experiments to test their performance, speed and stability. These experiments showed that the Tree-structured Parzen Estimator algorithm as used in the HyperOpt Python library was one of the best performing methods (only rivaled by the MVRSM algorithm), moreover it was shown to be the fastest as well as the most stable of the tested algorithms for this particular problem application. This research contributes to the existing theory by creating a modelling framework for the joint optimisation of spare inventory and maintenance scheduling decisions which is specifically tailored to the railway context and includes possibilities for minimal maintenance. In practice this model can be used by maintenance providers to increase the financial success of their maintenance operations and by infrastructure managers to increase insight into their maintenance providers' operations.

At the UN conference of 2015, the promotion of inland waterway transport (IWT) was named as a Sustainable Development Goal by the United Nations. While IWT has been identified to make goods transport more CO2- efficient, the long-term viability of the Dutch IWT sector is at risk.

Currently, Dutch IWT businesses benefit from increased demand for transport capacity during climate change induced periods of low river depth by being able to charge higher prices to shippers, thereby increasing short term profits. This thesis research project concludes that this behaviour is unsustainable due to the disregard for providing price competitive and reliable services to shippers, which will ultimately damage the reputation of the Dutch IWT sector. This can lead to shippers choosing other modes of transport, which will damage the long-term viability of IWT demand.

A lack of evident optimal climate adaptation measures that would better guarantee the long-term viability of an IWT business under any climate scenario is one of the reasons why IWT businesses have thus far been hesitant towards implementing climate adaptation measures. This thesis research project studies the potential of using a robust decision analysis in supporting IWT businesses’ decision-making on climate adaptation measures that could safeguard the long- term viability of the Dutch IWT sector. It conducts a robust decision analysis and then evaluates its potential using feedback from stakeholders of the Dutch IWT sector.

This thesis research project present the results from the robust decision analysis to IWT stakeholders to evaluate how these could impact their decision-making. Stakeholders noted that they believe that the produced influence diagram could be used as a standard communication object to produce a better consensus of how measures influence the long-term viability of IWT demand. Furthermore, stakeholders expressed that the robust decision analysis provided them a better understanding of which measures were able to better guarantee the long-term viability of IWT demand. Results from evaluation interviews with stakeholders therefore provide grounds to believe that robust decision analyses have potential to support IWT businesses’ decision- making on climate adaptation measures that could safeguard the long-term viability of the Dutch IWT sector.