In the industry various decisions are taken that are sequential and of which the impact is uncertain due to numerous random factors. Often these decisions are taken manually based on a number of simple rules of thumb. This can be done much better by developing smart algorithms that leverage data. 

In this project, we will focus on a set of specific decision problems for which we will develop such smart algorithms. Therefore, we will apply various methods, ranging from classic optimzation to more recent self-learning methods.