Maximizing student opportunities for in-person classes under pandemic capacity reductions

Abstract

In this article, we describe the decision support system that was developed for the assignment of courses to teaching modalities and rooms for the Fall semester of 2020 at the University of Connecticut (UConn). With the adoption of safety/mitigation standards imposed by the COVID-19 pandemic, the seating capacities of rooms were reduced by more than 70%, thus making virtually every existing room assignment for Fall 2020 infeasible. The demand for in-person instruction required the reassignment of a large number of courses to rooms, where not all requests for physical space could be accommodated. In order to maximize opportunities for in-person instruction, UConn introduced a teaching modality in which class meetings are attended on campus by only 50% of the enrolled students. As decision makers were given partial flexibility to assign teaching modalities to classes, the complexity of the assignment problem increased considerably, especially because the real-world instances involved hundreds of rooms and thousands of classes and required a quick solution turnaround in practice. In this article, we introduce this flexible assignment problem and describe the two mixed-integer programming formulations that were used to solve the real-world instances of the problem; in particular, one of the formulations leverages structural properties presented in this work in order to represent the problem in a more compact way. We explain how we tailored our algorithms to solve the real-world problem, describe the dynamics of the interactive decision support system created in this initiative, and present insights derived from our study.