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 probabilistic damage stability method offers great design freedom when used as a base of design. However, due to the complexity of the calculation and amount of parameters that influence the attained index, much of this freedom is not being harnessed by designers. This research tries to give the designer more insight in where to look when trying to comply with the regulations, by providing an initial subdivision design and create an overview of the influence of the parameters on the attained index. Many parameters have been found that either direct or indirect influence the damage stability calculation. A selection of parameters is chosen from this list as a starting point that are commonly used in the subdivision of large single hold vessels. A parameterised base ship has been made in the DELFTship program that is used for the execution of the optimisation and sensitivity analysis. The exploration for a suitable optimisation method and sensitivity analysis is based on the properties of these methods and method requirements that apply to this specific research. The most important requirement for both methods is the number of iterations needed to obtain a reasonable result, as the damage stability calculation can take up to 15 minutes. This resulted in the choice for the SACOBRA optimisation algorithm. To guarantee the effectiveness of the design, a second level to the optimisation is added where, the number of bulkheads is optimised, while simultaneously optimising the steel weight. During the research, the cargo hold volume was added as this proved to be an effective objective to ensure the efficiency of the design. This resulted in the change to the SAMO-COBRA algorithm, where the single objective SACOBRA algorithm was still used as a verification method and to investigate if it could be used for experimenting with certain design choices. For the sensitivity analysis the Morris method was chosen, mainly for its low number of sample points needed to converge. This method can be applied to a broad range of models and is characterised by its simplicity. However, this simplicity resulted in a relatively low amount of insight generated regarding the influence of the parameters on both the objectives as well as each other. A correlation matrix was added to further provide knowledge and insight. A sensitivity analysis by hand was performed to verify the results of both analysis methods.

The first optimisation stage showed to be a relatively fast way to determine the amount of bulkheads compared to the attained index that can be expected. However, a relatively large margin of error is observed in this stage and more information is needed to be able to make a decision on how many bulkheads is used to further optimise. The use of the SAMO-COBRA method in the second optimisation stage proved to be effective at providing the naval architect with a range of design proposals, where the probabilistic damage stability regulations were used as a base of design. Furthermore, it is shown that for single hold ships in general, the priority of the algorithm followed the influence of the parameters on the distance they were able to create between the cargo hold and the outer hull. The influence of the parameters, resulting from the sensitivity analyses endorse these claims. The Morris method showed the high non-linear and non-monotonic behaviour of the parameters that were investigated. This made it difficult to distinguish the level of influence between the parameters. The combination of the Morris method, Pearson correlation matrix and the sensitivity by hand proved to be sufficient for determining the behaviour of the probabilistic damage stability calculation. In the end, this research proposes a new foundation of designing a ship with the probabilistic damage stability regulations as a base of design. After the initial design from the two stage optimisation all other design requirements are implemented in the design. If the ship then fails to comply with the regulations, the knowledge and insight from this research can be used to increase the survivability of the ship in order for it to comply again.

The offshore wind market is growing rapidly, and new offshore wind projects are being launched more than ever. To keep up with demand, parts for these wind turbines are being produced all over the world. To get all the parts needed to their destination on time, they must be transported on heavy transport vessels (HTVs).

Today, business cases focused on maximizing profits dictate the design of these vessels. Despite the upcoming energy transition, the environmental impact of these vessels is usually neglected. Therefore, there is a need for a method to evaluate the economic and environmental performance of HTVs.

One basis for such a method is Blended Design. This method is able to cope with market uncertainties in the early stages of ship design by expanding knowledge when design freedom is still high by combining market forecasts for offshore wind farms with multiple vessel designs. An uncertainty model then evaluates the financial performance of the vessels in the market, allowing designers to explore the performance on basic main dimensions of an offshore wind installation vessel. The main limitations of this method are that it is not able to optimize on environmental performance and evaluate the financial impact of alternative fuels.

Due to growing concerns about climate change, Ulstein has seen an increase in requests for the use of alternative fuels in ship designs. For this research, methanol, ammonia and liquid hydrogen are being investigated as these alternative fuels are considered future-proof.

The use of alternative fuels in ship design involves some adjustments to the
Blended Design method. Due to the different gravimetric and volumetric densities of these alternative fuels, this has implications for the endurance of the ship design and the amount of cargo the ship can carry. Changing system and installation requirements and varying fuel costs also drastically alter the financial performance of alternative fuels on ship designs.

The proposed methodology accounts for these changes and quantifies alternative fuel emissions by converting them to CO2-equivalent emissions. In this way, other greenhouse gases such as CH4 and N2O are also included in this metric. The environmental performance is then expressed in the EEXI-equivalent: the amount of CO2-e emitted by the ship design per tonmile. The EEXI-e index makes it possible to compare the various alternative fuels in terms of their environmental performance on the same basis.

In this work the EEXI-e method is combined with the original Blended Design method and the method is adapted to include alternative fuels. This makes it possible to evaluate the environmental performance and associated financial impacts of different alternative fuels.

Because financial and environmental performance are now linked, it is possible to examine the impact of different ranges of carbon taxes. The case study results presented in this thesis show that the carbon tax has a large impact on the financial performance of the ship when it is applied. When enforced, the choice of an alternative fuel system becomes more attractive.

The proposed method has provided a guide to ship designers to make better decisions about the main dimensions of a ship at an early stage of ship design. In addition, the method can provide much-needed insight in the selection of alternative fuels by evaluating the financial and environmental performance of alternative fuel systems.