Safe distancing in the time of COVID-19

• Published in: European journal of operational research Vol. 304; no. 1; pp. 139 - 149
• Main Authors: Fischetti, Martina, Fischetti, Matteo, Stoustrup, Jakob
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 01-01-2023
• Subjects: Combinatorial optimization; COVID-19; Mathematical optimization; Mixed integer linear programming; OR in pandemics; Social distancing; COVID-19; Mixed integer linear programming; Combinatorial optimization; Social distancing; Mathematical optimization
• ISSN: 0377-2217; 1872-6860; 0377-2217
• DOI: 10.1016/j.ejor.2021.07.010

•We study a facility layout problem under social distancing constraints.•Our goal is to find layouts that minimize the overall risk of COVID-19 infection.•Algorithms originally designed for offshore wind farms are applied.•We show how OR can produce optimal layouts improving customers safety.•In the optimal layouts, facilities are not equally distanced as typically believed.
The spread of viruses such as SARS-CoV-2 brought new challenges to our society, including a stronger focus on safety across all businesses. Many countries have imposed a minimum social distance among people in order to ensure their safety. This brings new challenges to many customer-related businesses, such as restaurants, offices, theaters, etc., on how to locate their facilities (tables, seats etc.) under distancing constraints. We propose a parallel between this problem and that of locating wind turbines in an offshore area. The discovery of this parallel allows us to apply Mathematical Optimization algorithms originally designed for wind farms, to produce optimized facility layouts that minimize the overall risk of infection among customers. In this way we can investigate the structure of the safest layouts, with some surprising outcomes. A lesson learned is that, in the safest layouts, the facilities are not equally distanced (as it is typically believed) but tend to concentrate on the border of the available area—a policy that significantly reduces the overall risk of contagion.