A Framework for Operations Research Model Use in Resilience to Fundamental Surprise Events: Observations from University Operations during COVID-19

TL;DR

This paper presents a framework for how operations research models can be adapted or created during fundamental surprise events like COVID-19, based on a university's response, to enhance resilience.

Contribution

It introduces a formal framework detailing adaptations of OR models during surprise events, supported by empirical evidence from university case studies.

Findings

OR models were adapted through data changes, constraints, and model switching.

The framework highlights the dynamic role of OR in responding to unforeseen events.

Implications for improving resilience using OR during crises are discussed.

Abstract

Operations research (OR) approaches have been increasingly applied to model the resilience of a system to surprise events. In order to model a surprise event, one must have an understanding of its characteristics, which then become parameters, decisions, and/or constraints in the resulting model. This means that these models cannot (directly) handle fundamental surprise events, which are events that could not be defined before they happen. However, OR models may be adapted, improvised, or created during a fundamental surprise event, such as the COVID-19 pandemic, to help respond to it. We provide a framework for how OR models were applied by a university in response to the pandemic, thus helping to understand the role of OR models during fundamental surprise events. Our framework includes the following adaptations: adapting data, adding constraints, model switching, pulling from the modeling toolkit, and creating a new model. Each of these adaptations is formally presented, with supporting evidence gathered through interviews with modelers and users involved in the university response to the pandemic. We discuss the implications of this framework for both OR and resilience.