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.

翻译：文献中，考试时间表问题（ETTP）常被视为选课后问题（PE-ETTP）。在现实世界中，大学通常利用前几学期的信息，在学生注册前安排考试。这种方法的一个直接后果是所构建的模型中存在不确定性。本文讨论了鲁棒优化文献中的几种可用方法。我们考虑了每种方法对考试时间表问题的影响，并阐述了如何将最有利的方法应用于ETTP。随后，我们分析了所给鲁棒性方法的某些可能实现方式对两个现实世界实例以及由本文提出的实例生成框架生成的若干随机实例的影响。