A categorization of observed uses of operational research models for fundamental surprise events: Observations from university operations during COVID-19

Figures & data

Table 1. An overview of model use and adaptations by the COVID-19 response of Clemson University.

	Pre-pandemic	Fall 2020	Spring 2021
Classroom scheduling	Banner, with historical classroom data, and input from department-level schedulers and students	Banner with classroom data update in accordance with COVID-19 guidelines	Banner, with classroom data update in accordance with COVID-19 guidelines
Cohort scheduling (hybrid)	Policy not in place: No model needed	Cohort based MIP model	Cohort-based MIP model
Hand sanitizer location	Resource not deployed: No model needed	Heuristic-driven demand model, (No math modeling)	Application of facility location models

Table 2. An overview of our categorization scheme for how OR models are used in response to fundamental surprise events.

		Model characteristics vs. pre-surprise
Category	Definition	Decision	Model	Data	Adaptation by
Adapt data	The underlying model remains the same but old data is no longer useful	Same	Same	New	User
Add constraints	The underlying model remains the same, but the decision-maker gives less freedom to the model.	Same	Modified	New	Either modeler or user
Model switch	The underlying model is substantially revised or switches to a different model altogether	Same	New or modified	New	Modeler and user
Pull model from toolkit	A new decision that needs to be made due to the surprise event could be made better by applying existing modeling approaches.	New	New	New	Modeler and user
Create new model	An entirely new model is needed for effective decision-making.	New	New	New	Modeler and user

Table 3. Categorization of a sampling of OR papers for university response to COVID-19 within our framework.

					Type of Adaptation
Reference	University	Problem	Model/approach	Implemented?	Adapt data	Add constraints	Model switch	Model toolkit	New model
Murray, 2020	University of California, Santa Barbara	Classroom physical distancing (capacity and layout)	Location modeling	Yes				x
Chen et al., 2021	Michigan	Bus operations to reduce transmission (locations, routes, schedules)	MIP; simulation	Yes			x
Greenberg et al., 2021	Cornell	Classroom seating layout	IP and computer vision	Yes				x
Johnson & Wilson, 2022	Oklahoma State University	Assigning hybrid classes to classrooms while considering rotations of students	IP	Yes				x
Navabi-Shirazi et al., 2022	Georgia Tech	Decide class modality; assign classrooms for in-person classes	MIP	Advisory					x
Barnhart et al., 2022	MIT	Term planning (which terms to offer courses in) and timetabling (scheduling the courses)	IP	Advisory and implemented					x

Figure 1. Interactions between the user, modeler, OR model, and the external dynamics in the real world. This is a specific application of the general framework of Eisenberg et al. (2019) presented in their Figure 1, p. 1873 to OR model usage.

Murray, A. T. (2020). Planning for classroom physical distancing to minimize the threat of COVID-19 disease spread. PLoS One, 15(12), e0243345. https://doi.org/10.1371/journal.pone.0243345

 PubMed Web of Science ®Google Scholar

Chen, G., Fei, X., Jia, H., Yu, X., Shen, S. (2021). An optimization-and-simulation framework for redesigning university campus bus system with social distancing. Available at https://arxiv.org/abs/2010.10630.

 Google Scholar

Greenberg, K. K., Hensel, T., Zhu, Q., Aarts, S., Shmoys, D. B., Gutekunst, S. C. (2021). An automated tool for optimal classroom seating assignment with social distancing constraints. IIE Annual Conference Proceedings, 429–434.

 Google Scholar

Johnson, C., & Wilson, R. L. (2022). Practice summary: A multiobjective assignment model for optimal socially distanced classrooms for the Spears School of Business at Oklahoma State University. INFORMS Journal on Applied Analytics, 52(3), 295–300. https://doi.org/10.1287/inte.2021.1103

 Google Scholar

Navabi-Shirazi, M., El Tonbari, M., Boland, N., Nazzal, D., & Steimle, L. N. (2022). Multicriteria course mode selection and classroom assignment under sudden space scarcity. Manufacturing & Service Operations Management, 24(6), 3252–3268. https://doi.org/10.1287/msom.2022.1131

 Google Scholar

Barnhart, C., Bertsimas, D., Delarue, A., & Yan, J. (2022). Course scheduling under sudden scarcity: Applications to pandemic planning. Manufacturing & Service Operations Management, 24(2), 727–745. https://doi.org/10.1287/msom.2021.0996

 Google Scholar

Eisenberg, D., Seager, T., & Alderson, D. L. (2019). Rethinking resilience analytics. Risk Analysis: An Official Publication of the Society for Risk Analysis, 39(9), 1870–1884. https://doi.org/10.1111/risa.13328

 PubMed Web of Science ®Google Scholar