一种基于社会距离的抗COVID-19设施选址方法
A Social Distancing-Based Facility Location Approach for Combating COVID-19
摘要：
在本文中，我们介绍和研究的设施选址问题与\emph{`social distancing'}的概念。该问题的输入是城市的道路网络，其中节点是住宅区，边是连接区域的路段以及它们各自的距离。我们亦有每个区的人口数据，有多少项设施将会开放，以及每个区的需求。该问题的目标是定位的设施，使人们可以得到服务，并在同一时间的总社会距离最大化。我们正式地把这个问题称为\textsc{Social Distancing-Based Facility Location Problem}。我们在数学上量化了给定的设施分配的社会距离，并提出了一个优化模型。由于问题是\textsf{NP-Hard}，我们提出了一个基于模拟和启发式的方法来解决这个问题。对这两种方法进行了详细的分析。我们用合成数据集进行了一系列广泛的实验。从结果中，我们观察到，所提出的启发式方法导致更好的分配相比，基于模拟的方法。
英文摘要：
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.
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