Quay cranes on modern container terminals have the possibility to lift two 40ft. containers side by side. In order to be able to perform this type of lift, the crane must be equipped with a tandem spreader. As this type of spreader cannot be used for performing certain lifts, such as the handling of hatch covers, switches between single spreader and tandem spreader are inevitable. However, there is no method in current literature nor in practice to determine when the spreader changes should be performed. Therefore, the aim of this research is to develop a model that calculates the optimal moments to switch spreaders. As the performance of tandem lifting depends on the cycle times, a cycle time prediction model is developed first. This Artificial Neural Network model predicts the cycle time based on the type of lift, the actual position of the container(s) on the ship, the weight of the load and the current wind speed. The predictions yield a Mean Absolute Percentage Error of less than 11%. Afterwards, the cycle time prediction model is used to develop an optimal spreader switching strategy model in the form of a Mixed Integer Linear Programming model. A case study, in which several different bay layouts of a container ship need to be handled, shows a decrease in handling time of up to 22% compared to the traditional spreader switching strategy.

Team Sunweb, a professional cycling team and our client, is constantly looking for innovations to help them win races. They tasked us with creating an application which could assist coaches with determining the strategy during a race. This application, which we dubbed TelaSol, is supposed to run on a tablet that is mounted on the dashboard inside the coach car. For this project we developed an application that allows races to be prepared on a desktop computer and tracked during a race on a tablet-optimized interactive dashboard. On this dashboard, there will be information on the riders, the route and comments that can be added before the race.

During development we have considered existing solutions, relevant literature and useful technologies to get an idea of what was possible and how we could achieve our goal. We used this knowledge to create our initial set of requirements. We then proceeded development of application using an agile approach, which involves regular feedback moments from our client to update the requirements and adjust our focus accordingly. To verify the quality of our product we relied on a combination of automated tests, user testing and validation through the client.

Initially the application was supposed to integrate live data coming from the riders during the race, but due to a regulation change we had to change our focus. Instead, we focused primarily on creating the application for playback purposes, while still keeping it adaptable to live data. The application performs the main tasks that were initially defined properly. After further development on live data and extensive situational testing, the app can be used to its full potential. Using TelaSol, Team Sunweb will improve their ability to analyze races and increase their chances of winning.