Robustness Approaches for the Examination Timetabling Problem under Data Uncertainty
It addresses scheduling exams before student registration for universities, which is an incremental improvement over existing post-enrollment models.
The paper tackles the examination timetabling problem under data uncertainty by discussing and applying robust optimization approaches, analyzing their impact on real-world and generated instances.
In the literature the examination timetabling problem (ETTP) is often considered a post-enrollment problem (PE-ETTP). In the real world, universities often schedule their exams before students register using information from previous terms. A direct consequence of this approach is the uncertainty present in the resulting models. In this work we discuss several approaches available in the robust optimization literature. We consider the implications of each approach in respect to the examination timetabling problem and present how the most favorable approaches can be applied to the ETTP. Afterwards we analyze the impact of some possible implementations of the given robustness approaches on two real world instances and several random instances generated by our instance generation framework which we introduce in this work.