Robustness Approaches for the Examination Timetabling Problem under Data Uncertainty

TL;DR

This paper explores robust optimization methods to handle data uncertainty in examination timetabling, proposing approaches applicable to real-world university scheduling scenarios with uncertain student registration data.

Contribution

It analyzes and adapts robust optimization techniques for ETTP, providing practical implementation insights and testing on real and generated instances.

Findings

Robust approaches improve timetable stability under data uncertainty.

Application of methods reduces scheduling conflicts in uncertain data scenarios.

Framework for generating test instances enhances evaluation of robustness methods.

Abstract

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.