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.

As Machine Learning is becoming more accessible to small businesses, thanks to the rapid advance in computing power, smaller start-ups such as Sjauf (a ride sharing start-up) are starting to get interested in implementing Machine Learning solutions in their product. Sjauf needed a system that could automatically tell its customers how much a certain trip would cost them. Using this information multiple different models were developed and integrated into an ensemble. This ensemble as well as the models used by it were then used for price prediction. This project is a proof of concept to show that Machine Learning is capable of solving this problem in real time.

After researching state of the art Machine Learning models for price recommendation, the architecture of the system was designed. The supplied data was preprocessed, after which a custom Genetic Algorithm was developed for optimising models and ensembles. After validation on real-life company data, a comparison using empirical metrics was conducted. We use these empirical metrics to show that a bagging ensemble is the most efficient and accurate model for this purpose. This bagging ensemble outperformed the currently implemented functions, whilst adhering to the set boundaries on response times. Lastly, recommendations are made to the company with an overview of potential future work in this subject.