A Social Distancing-Based Facility Location Approach for Combating COVID-19

TL;DR

This paper introduces a new facility location problem that maximizes social distancing in city networks during COVID-19, proposing heuristic solutions and analyzing their effectiveness through experiments.

Contribution

It formulates the social distancing-based facility location problem, models it mathematically, and proposes heuristic and simulation methods to solve this NP-hard problem.

Findings

Heuristic approach outperforms simulation-based method.

Proposed models effectively maximize social distancing.

Extensive experiments validate the approaches.

Abstract

In this paper, we introduce and study the problem of facility location along with the notion of \emph{`social distancing'}. The input to the problem is the road network of a city where the nodes are the residential zones, edges are the road segments connecting the zones along with their respective distance. We also have the information about the population at each zone, different types of facilities to be opened and in which number, and their respective demands in each zone. The goal of the problem is to locate the facilities such that the people can be served and at the same time the total social distancing is maximized. We formally call this problem as the \textsc{Social Distancing-Based Facility Location Problem}. We mathematically quantify social distancing for a given allocation of facilities and proposed an optimization model. As the problem is \textsf{NP-Hard}, we propose a simulation-based and heuristic approach for solving this problem. A detailed analysis of both methods has been done. We perform an extensive set of experiments with synthetic datasets. From the results, we observe that the proposed heuristic approach leads to a better allocation compared to the simulation-based approach.