There is increasingly more expensive maintenance that needs to be performed on the Dutch railway network. Good maintenance schedules reduce costs, minimise hindrance to passenger and freight travel, and follow restrictions imposed by available resources, legislation and other agreements. The railway maintainer has modelled this maintenance scheduling problem, among others, on an annual level. This problem definition is very precise with different conflicting non-linear constraints and cost parts. The demands of the solving method depend on the phase of the scheduling process. In early phases, low runtime is most important, whereas best solution quality outweighs this runtime when the maintenance work is more finalised. In 2019, a simple greedy algorithm found good solutions fast, and a hybrid greedy-evolutionary algorithm was developed that resulted in the best schedules. However, since then, the problem definition has been made more realistic and thus complex. Therefore, this hybrid greedy-evolutionary algorithm is no longer feasible, and creating a maintenance schedule takes significantly longer than before. Research is necessary to better understand the impact of the more realistic model, and to once more have a good trade-off between solving time and solution quality available for the schedulers. In this thesis, we aim to achieve this by improving different aspects of the problem and solving methods. Most experiments were done with the maintenance schedule of 2024, and results were verified on the years of 2023 and 2025. First, general problem analysis and implementation improvements reduced the runtime from around twenty-four hours to three hours with the greedy algorithm. Then, approximations were applied in the passenger hinder to further reduce the runtime by around half with a negligible negative impact on the solution quality. Due to these speed-ups, it was possible to use more elaborate solving methods. New experimental results showed that the greedy algorithm still finds solutions fast. The hybrid greedy-evolutionary algorithm found better quality schedules, but required more runtime. Furthermore, a novel solving method with look-aheads was proposed, which showed some potential for cost reductions, but was dominated by the hybrid algorithm. Every algorithm uses the greedy heuristic as a subroutine. Results showed the importance of finding the right order for greedily scheduling the requests. A proposed new order function improved the quality of the resulting maintenance schedules even further. To conclude, the increase in complexity of the problem definition in recent years has made solving more difficult. To still find good solutions in a similar time, a better performing greedy heuristic was necessary. By applying different runtime optimisations to the objective evaluation and improving the solution quality of the greedy heuristic, a good trade-off between runtime and solution quality, using different solving methods, was realised for creating annual maintenance schedules for the Dutch railway network.

With the increasing amount of container freight transport and the increasing size of container vessels, for the Port of Antwerp, the second largest container port in Europe, a critical task is port planning. A simulation model provides the means to gain proper insight in the effect of future expansions. Macomi, a company specialized in simulation and optimisation, has been working on a simulation model to aid the Port of Antwerp in their port planning. One important issue in this simulation model is how the berth allocation of vessels is handled. Berth allocation is the problem of assigning vessels a time and location at the quay wall where the vessel can be loaded and unloaded. In this thesis, the aim is to develop decision models for both the preliminary berth planning and the real-time recovery of this plan during simulation. For the first part, a cyclic baseline berth allocation plan is created which takes into account the tidal dependencies vessels have when entering the port of Antwerp. This preliminary berth plan is used as a basis for the simulation model as the arrival times are based on this plan. However, during the simulation disruptions might occur; vessels can arrive earlier or later or take longer to load and unload. To deal with these disruptions a real-time disruption management decision model is proposed which aims to solve all disruptions while staying as close to the theoretical berth plan as possible. Using the proposed models, several experiments have been conducted regarding the influence of uncertainty, occupation and robustness on the quality of the solutions that the decision models found. Regarding occupancy rates, results show that a tipping point exists where the recovery model has more difficulty to find a good solution. Results also show that when the expected occupation of a terminal is higher, adding robustness has more effect and is therefore more important. The decision models presented in this thesis have been implemented in the Macomi port simulation model and have been demonstrated to the Port of Antwerp. Both parties have expressed their satisfaction with the models.