Cornell University Uses Integer Programming to Optimize Final Exam Scheduling.

This article focuses on an integer programming (IP)–based optimization framework developed to address the complex final exam scheduling challenges at Cornell University. The framework combines a multistage Group-then-Sequence (GtS) model with a Layer-Cake heuristic algorithm to generate multiple exam schedules that minimize conflicts such as overlapping exams, back-to-back exams, and multiple exams within short time frames, thereby improving student and faculty satisfaction. Since spring 2022, the framework has been successfully implemented for several semesters, consistently outperforming the historical lecture time–based scheduling method by eliminating or drastically reducing direct conflicts and other undesirable scheduling patterns. The framework is highly flexible, accommodating constraints like front-loading large exams and excluding specific time slots, and has demonstrated competitive performance on benchmark data sets beyond Cornell, highlighting its adaptability to diverse institutional requirements.