In this thesis we analyse the class of maximum coverage problems. For all discussed problems, linear programs are formulated. Using the notion of submodularity, we prove that for the weighted version of the basic Maximum Coverage problem, where the weights differ per set, a polynomial-time greedy algorithm guarantees a (1 - 1/e)-approximation of the optimal solution. This improves the already known bound of (1 - 1/e - ε), for all ε > 0. We then show that the same result holds true, if we allow elements to be covered by multiple sets. Furthermore, a completely novel extension is introduced, where weights differ per combination of sets. It is proved that, under the assumption that the weights are submodular and increasing, a greedy algorithm still provides a (1 – 1/e)-approximation.  The latter algorithm is tested in the framework of optimal sensor selection. To this end, we consider all official weather stations in the Netherlands as our sensor group. We test the performance of the approximation algorithm, if some of the assumptions do not hold and no theoretical bounds exists. Corresponding weights are calculated, using two approaches: inverse distance weighting and multiple linear regression. For both approaches the in practice performance of the greedy algorithm is shown to be even higher than (1 – 1/e), even though not all assumptions hold. Finally, the corresponding selection of weather stations is shown.

In response to the European Commission's initiative to shift freight transport from road to inland waterways, there is a need to expand the capacity of inland ports to accommodate larger cargo ships. This expansion requires lowering the water bottom, which will increase the forces exerted on the quay walls. The quay walls should therefore be reinforced by applying structural adjustments, for minimal costs and environmental impact. The goal of this thesis is therefore to develop a decision support tool, which is able to directly determine the preferred configuration of structural adjustments. To maximise the aggregated preference of all stakeholders involved while satisfying the systems constraints, the a-priori design optimisation method of the Preferendus is applied. The tool, developed using computational science, uses Python because of its capability to automate repetitive calculations. It integrates with the program D-Sheet Piling that is used for specific sheet pile calculations. The tool is designed to be used during the preliminary design phase, enabling quick assessment of potential reinforcement adjustments and facilitating insight into the preferred solution. It evaluates three main strategies for quay wall reinforcement: lowering active soil stress, increasing passive soil stress, and enhancing pile stability. The tool uniquely focuses on maximising the aggregated preference of involved stakeholders and is capable of evaluating failure mechanisms of sheet piles. It is tested on three case studies, all located in the industrial harbour Loven in Tilburg. The results show that the tool effectively proposes which structural adjustments are applicable to create a sheet pile design that satisfies. The thesis concludes by drawing specific conclusions for each structural adjustment considered in the project. Moreover, it concludes that the development of a decision support tool has been successful. In particular, the tool enhances efficiency in sheet pile calculations, offers detailed insights into the environmental and financial impact of adjustments and enables the direct determination of the preferred configuration of structural adjustments. This eliminates the need to choose the preferred configuration from a number of designed variants, which is the current approach. Additionally, the thesis recommends to conduct a follow-up research on the applicability of underwater anchors, which have shown significant structural potential. Furthermore, it recommends to conduct laboratory tests to potentially improve the cohesion and angle of internal friction of soil layers. If those soil parameters can be improved, no structural adjustment may be necessary to reinforce quay walls at all.

Background Scheduling is a form of decision-making that plays a very important role in manufacturing industries. It is the method by which work gets assigned to resources that compute it. In this thesis we want to assign jobs (cycles of a lithography process) to different machines in such a way that the machines are running for as little time as possible. The difficulty lies in the fact that some jobs require the same resource, and thus cannot be processed simultaneously. Results It appears that the following scheduling problems are NP-hard: • P2kCmax and R2kCmax • P3|res · 11, pj = 1|Cmax and R3|res · 11, pj = 1|Cmax • P3|res · 11, pj = 1| P Cj and R3|res · 11, pj = 1| P Cj We try to approximate an optimal solution for the scheduling problem Rm|res · 11| P Cj by calculating a ratio with a lower bound, where we ignore resource constraints, and an upper bound, that is found by a Greedy algorithm (w.r.t. resource constraints). Conclusions It appears that Greedy is not a very good approximation algorithm for this problem, since the ratio in which the optimal Total Completion Time should lie is about 8,33% of the average calculated Total Completion Time

This thesis introduces a model designed to provide port operators with insights into necessary infrastructure and adequate scheduling approaches when ships use electricity as their main power source. The operator manages a shipping port equipped with charging stations and provides a selection of electricity-powered freight cargo ships with container batteries. The model is designed for an arbitrary single-port waterway system. The focus is directed toward inland waterway systems, a choice influenced by the limited capacity of container batteries. Optional external revenue streams in the form of grid balancing and two core uncertainties of the maritime sector are incorporated into the model, namely energy consumption and arrival time uncertainty. Optimization approaches are formulated on the model to find the optimal number of batteries, charging stations, and grid balancing stints. The following six approaches are employed: approximation algorithm, MIP formulation, rolling horizon, probabilistic constraint, extreme value analysis, and value at risk. The strategies all produce a schedule that guides the operator in managing the infrastructure optimally in a different context. The approaches are tested on a simulated waterway system. The approximation algorithm is a great first step. The MIP formulation provides a valuable next step in insight into the complexities of the system. However, it scales too poorly to extend to bigger data cases or to integrate uncertainties. In scenarios involving uncertainty, the rolling horizon approach is recommended due to its adaptability and realistic modeling. Valuable insights about the limits of the system can be obtained by implementing values at risk and extreme value analyses. The probabilistic constraint approach is a suitable alternative if normally distributed uncertainty is inherent to the uncertainty data. The model with these optimization methods provides port operators with essential insights into the system they are managing.

In the field of urban development few challenges are as tough as the housing problem. The cause for this is the rapid expansion of urban populations combined with slow construction processes. The Open Design Systems (Odesys) methodology is a preference-based design methodology for creating and evaluating designs. For this it uses a-priori method of optimization, where the preferences of stakeholders for each project objective are determined in the beginning of the process. Designs are thereafter created based on these preferences. Central to the success of preference-based design is the attitude of stakeholders towards using the methodology, which in other words is acceptance for this methodology. How can acceptance be evaluated? To do this the Technology Acceptance Model (TAM) was used. The system design features in the TAM were categorized in the “Use”, “Functionality” and the “Presentation”. Modifications to the Odesys methodology were made in these categories to see the effect on the stakeholder acceptability using the TAM. This research attempted to raise the acceptability for the Odesys methodology with the use of 2 case studies. The conclusion was that for the “Use” category the acceptability was dependent on the project design phase and the involvement of stakeholders. For the “Functionality”, the result reliability and the running time were important factors. The “Presentation” category consisted of the interface and the distinction between group and individual sessions. Modifications to these were developed and tested. This resulted in an increased perceived usefulness and a perceived ease-of-use , which according to the TAM results in an increased acceptance.

In the field of urban development few challenges are as tough as the housing problem. The cause for this is the rapid expansion of urban populations combined with slow construction processes. The Open Design Systems (Odesys) methodology is a preference-based design methodology for creating and evaluating designs. For this it uses a-priori method of optimization, where the preferences of stakeholders for each project objective are determined in the beginning of the process. Designs are thereafter created based on these preferences. Central to the success of preference-based design is the attitude of stakeholders towards using the methodology, which in other words is acceptance for this methodology. How can acceptance be evaluated? To do this the Technology Acceptance Model (TAM) was used. The system design features in the TAM were categorized in the “Use”, “Functionality” and the “Presentation”. Modifications to the Odesys methodology were made in these categories to see the effect on the stakeholder acceptability using the TAM. This research attempted to raise the acceptability for the Odesys methodology with the use of 2 case studies. The conclusion was that for the “Use” category the acceptability was dependent on the project design phase and the involvement of stakeholders. For the “Functionality”, the result reliability and the running time were important factors. The “Presentation” category consisted of the interface and the distinction between group and individual sessions. Modifications to these were developed and tested. This resulted in an increased perceived usefulness and a perceived ease-of-use , which according to the TAM results in an increased acceptance.