Dynamic Allocation of Hospital Beds During the COVID-19 Pandemic Outbreak: A Possibilistic Programming Approach

The health service network has problems such as a shortage of medical equipment and human resources. Due to the need for high expertise in supplying these facilities, this problem is much harder to be solved than other industrial ones. In the COVID-19 pandemic, maintaining tranquility in society is the most important factor. The tranquility is obtained by providing medical facilities in the health care network. Also, the COVID-19 pandemic imposes new restrictions on the network because of preventive guidelines. In this situation, the problem of resource allocation will become more sophisticated and will reduce system efficiency. In this paper, the problem of transferring hospital beds to patients infected by COVID-19 considering a predetermined capacity level is considered. To cope with these problems, a mixed-integer mathematical programming model is suggested. In addition, to consider the uncertainty in the demand of patients that occurs in the pandemic, the fuzzy programming approach is used. The suggested model is solved with the Benders decomposition algorithm (BDA) and applied for assigning beds in two samples. The results show that proper management of resources in crisis situations such as the COVID-19 outbreak is very effective. As a result, this issue causes to overcome pressure on medical staff and lack of hospital facilities, during pandemic conditions.