University Course Scheduling Optimization under Uncertainty based on a Probability Model

In this thesis, we present a way to model uncertainty when optimizing the UniversityTimetabling Problem. It is an NP-hard, combinatorial and highly constrained problem.In this thesis, we first propose a standardized model based on the data from MalmöUniversity. Then, we propose our extended model, which, during the creation of the solution, accounts for the probability of unexpected events to occur and changes the solution accordingly. To implement our model, we use a Particle Swarm Optimization (PSO) algorithm.In our experiments, we find problems with the algorithm converging too early.We analyze the performance of our extended model compared to the standardized model,using a benchmark devised by us, and find that it performs well, reducing the number ofconstraint violations by 32%. We then suggest further areas of research in regards to thisuncertainty model