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以人为主的新冠肺炎检测中心选址决策:泰米尔纳德邦案例研究

Human Centered Decision-Making for COVID-19 Testing Center Location Selection: Tamil Nadu-A Case Study.

作者信息

Saroja S, Madavan R, Haseena S, Pepsi M Blessa Binolin, Karthick Alagar, Mohanavel V, Muhibbullah M

机构信息

Department of Information Technology, Mepco Schlenk Engineering College, Sivakasi, 626005 Tamil Nadu, India.

Department of Electrical and Electronics Engineering, PSR Engineering College, Sivakasi, 626140 Tamil Nadu, India.

出版信息

Comput Math Methods Med. 2022 Mar 10;2022:2048294. doi: 10.1155/2022/2048294. eCollection 2022.

Abstract

This paper proposes a blend of three techniques to select COVID-19 testing centers. The objective of the paper is to identify a suitable location to establish new COVID-19 testing centers. Establishment of the testing center in the needy locations will be beneficial to both public and government officials. Selection of the wrong location may lead to lose both health and wealth. In this paper, location selection is modelled as a decision-making problem. The paper uses fuzzy analytic hierarchy process (AHP) technique to generate the criteria weights, monkey search algorithm to optimize the weights, and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method to rank the different locations. To illustrate the applicability of the proposed technique, a state named Tamil Nadu, located in India, is taken for a case study. The proposed structured algorithmic steps were applied for the input data obtained from the government of India website, and the results were analyzed and validated using the government of India website. The ranks assigned by the proposed technique to different locations are in aligning with the number of patients and death rate.

摘要

本文提出了三种技术的融合方法来选择新冠病毒检测中心。本文的目的是确定一个合适的地点来建立新的新冠病毒检测中心。在需求地区建立检测中心将对公众和政府官员都有益。选择错误的地点可能会导致健康和财富的双重损失。在本文中,选址被建模为一个决策问题。本文使用模糊层次分析法(AHP)技术来生成标准权重,使用猴子搜索算法来优化权重,并使用逼近理想解排序法(TOPSIS)对不同地点进行排名。为了说明所提出技术的适用性,以印度的泰米尔纳德邦为例进行案例研究。将所提出的结构化算法步骤应用于从印度政府网站获得的输入数据,并使用印度政府网站对结果进行分析和验证。所提出的技术分配给不同地点的排名与患者数量和死亡率一致。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0ad7/8930239/ff734173f6db/CMMM2022-2048294.001.jpg

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