A goal programming model for two-stage COVID19 test sampling centers location-allocation problem

Abstract

The COVID19 virus, which first appeared in Wuhan, China, and has become a pandemic in a short time, has threatened the health system in many countries and put it into a bottleneck. Simultaneously, the second wave's expectation spread it necessary to plan the health services correctly. In this study, a location-allocation problem in the two-echelon system, which considers different test sampling alternatives, is examined to obtain test sampling centers' location-allocation. The problem is modeled as a goal programming model to create a network that tests samples at a minimum total distance, establishes a minimum number of test sampling centers, and reaches the distance of PCR test laboratories at minimum total distances. The proposed model is applied as a case study for the two cities located in Turkey, and the obtained locations and inventory levels of each location are presented. Besides, different scenarios are examined to understand the structure of the model. As a result, only testing in hospitals will increase the risk of contamination. Since testing at all points will not be possible administratively, it will be ensured that the most appropriate location-allocation decisions are taken by considering all the proposed model's objectives.

Location-Allocation Model to Improve the Distribution of COVID-19 Vaccine Centers in Jeddah City, Saudi Arabia

“…Studying and COVID-19 health facilities (vaccination and samples test) locations have attracted many researchers recently. These studies have looked into COVID-19 vaccination distributions sites [ 57 ], vaccination centers [ 58 ], and sample test sites [ 59 ]. These studies were conducted using various location-allocation models and applied to different European and Asian cities.…”

“…The model was demonstrated in a case study of a healthcare center in Yogyakarta, Indonesia. Kuvvetli [ 59 ] developed a goal programming model for the location-allocation problem to optimize sample test locations to minimize total distance while maintaining the maximum availability and the minimum number of test sampling centers. The model was applied to two cities in Turkey.…”