• Choosing Optimal Locations for Temporary Health Care Facilities During Health Crisis Using Binary Integer Programming

    Author(s):
    Nikhil Patel, Sandeep Trivedi (see profile)
    Date:
    2020
    Subject(s):
    Artificial intelligence, Machine learning
    Item Type:
    Abstract
    Tag(s):
    #healthcare, Alternative Care Site (ACS), Binary Integer Programming, covid-19
    Permanent URL:
    https://doi.org/10.17613/b93f-5843
    Abstract:
    An Alternative Care Site (ACS) is a healthcare facility constructed in a non-traditional environment during a public-health crisis to provide extra capacity to provide medical care within a specific region. Many nations are proposing to develop ACS to improve capacity in response to expected resource shortages associated with COVID-19 treatment and testing. In many situations, these facilities are meant to be interim and are designed to satisfy an urgent need during a health crisis. When selecting where to establish new temporary facilities various variables need to be addressed, including the practicality of possible locations, current resource availability, projected use, and proximity between people and the new location. In this research, a facility site optimization model was designed using Binary Integer Programming to enable decision makers to select the optimal area, or locations, to construct a healthcare facility to satisfy predicted medical needs. The proposed model is concerned with the best positioning of ACS from a collection of candidate sites in order to reduce the travel distance between the healthcare facilities and the patients. Patients are presumed to be serviced by the facility that is geographically nearest to them. When the number of patients to be considered is excessive, it is possible to organize the patients into clusters. Then, instead of using the individual patient locations, the cluster centers can be used. This pre-processing works on the premise that the healthcare facility that is tasked with serving a particular cluster's customers will care for all of the patients who belong to that cluster. We showed 3 different case scenarios with varying parameters. Within the framework of Binary Integer Programming, the k-means method is employed, which seeks to split n patients into k unique and non-overlapping groups.
    Notes:
    Paper link : https://journals.sagescience.org/index.php/ssraml/article/view/7/7
    Metadata:
    Published as:
    Journal article    
    Status:
    Published
    Last Updated:
    7 days ago
    License:
    Attribution-NonCommercial
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