An optimization-based framework for personal scheduling during pandemic events
DOI:
https://doi.org/10.5753/jbcs.2024.3589Keywords:
Pandemic, Integer programming, COVID-19, personal schedulingAbstract
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.
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Copyright (c) 2024 Flávio Oscar Hahn, Bruno Nogueira, Rian G. S. Pinheiro
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
