Models for managing the impact of an epidemic

TL;DR

This paper develops robust optimization models for emergency staff deployment during a flu pandemic, aiming to maintain critical operations and minimize pandemic impact using realistic data and advanced solution techniques.

Contribution

It introduces a novel robust optimization framework for emergency staffing in pandemics, employing infinite linear programming and Benders decomposition for effective resource management.

Findings

Robust models effectively maintain critical staff levels during pandemics.

Numerical experiments demonstrate the approach's efficiency with realistic data.

Methodology reduces pandemic impact on organizational operations.

Abstract

In this paper we consider robust models for emergency staff deployment in the event of a flu pandemic. We focus on managing critical staff levels at organizations that must remain operational during such an event, and develop methodologies for managing emergency resources with the goal of minimizing the impact of the pandemic. We present numerical experiments using realistic data to study the effectiveness of our approach. The underlying methodology is that of robust optimization; we model the problem as an infinite linear program which is approximately solved using a variant of Benders decomposition.