An optimization-based framework for personal scheduling during pandemic events

Abstract

In recent years, companies have faced the challenge of adapting to new guidelines and strategies aimed at preventing and reducing the transmission of COVID-19 within the workplace. An essential aspect of this adaptation is effectively managing the workday schedule to minimize social contact.This paper introduces a comprehensive optimization framework designed to automate the planning of employee schedules during pandemic events. Our framework utilizes integer linear programming to establish a set of general constraints that can accommodate various types of distancing restrictions and cater to different objective functions.To employ the framework, a company simply needs to instantiate a subset of these constraints along with an objective function based on its specific priorities. We conducted tests on our scheduling framework within three distinct real-life companies, yielding promising results. Our approach successfully increased the number of in-person workers by 15%, all while adhering to the social distancing restrictions mandated by these companies. Furthermore, the solutions generated by our method were implemented and validated within these organizations.