Cornell University Uses Integer Programming to Optimize Final Exam Scheduling

Abstract

This paper presents an integer programming-based optimization framework designed to address Cornell University’s complex final exam scheduling challenges. By generating and comparing multiple scheduling variants using Group-then-Sequence and Layer-Cake heuristic approaches, the framework empowers the University Registrar to select schedules that dramatically reduce exam conflicts and enhance student and faculty satisfaction. The models have been successfully implemented at Cornell for several consecutive semesters, demonstrating significant advantages over the historical lecture time-based scheduling method.This paper presents an integer programming–based optimization framework designed to effectively address the complex final exam scheduling challenges encountered at Cornell University. With high flexibility, the framework is specifically tailored to accommodate a variety of different constraints, including the front-loading of large courses and the exclusion of specific time slots during the exam period. By generating multiple scheduling model variants and incorporating heuristic approaches, our framework enables comprehensive comparisons of different schedules. This empowers the university registrar to make informed decisions, considering trade-offs in terms of schedule comfort measured by different levels of exam conflicts. Our results demonstrate significant advantage over the historical lecture time–based approach, providing time and effort savings for the university administration while enhancing student and faculty satisfaction.History: This paper was refereed.