关键词: COVID-19疫情, 核酸采样, 选址, 人员配置, 工作时长

Abstract: Once a COVID-19 epidemic breaks out, regular nucleic acid testing becomes a major measure for epidemic prevention and control in various regions. Through regular nucleic acid testing, the government can effectively achieve the goal of “early detection, early reporting and early isolation” of infected persons and reduce the risk of epidemic transmission. Taking Suzhou City as an example, since the COVID-19 broke out in February 2022, the government fully launched normalized nucleic acid testing policy, requiring residents to undergo nucleic acid testing every 48 hours and present a test certificate before entering or leaving the community. However, due to the sudden onset of the epidemic, large sampling demand and lack of staff, it is easy to cause disorder in the sampling process if there is no reasonable sampling site location and personnel allocation plan in advance. In this situation, how to locate testing stations? How are residential areas allocated? How many “sampling booths” should be set up at each testing station? How to configure testing personnel and set their working hours? These are all decision-making issues faced by epidemic prevention and control departments.
Obviously, the optimal layout of testing stations belongs to the facility location problem. As a kind of classical combinatorial optimization problem, facility location problem has been widely concerned by scholars in the field of management science. However, the existing location models mainly focus on the p-median, p-center, set covering and maximum covering models, and they are mainly applied to the location of commercial facilities. At present, there is very little literature on the location of testing stations. Different from the traditional facility location model, thetesting station location not only has the common characteristics of traditional location-allocation problem but also needs to further determine service capacity (e.g., the number of testing booth in every testing station), optimize personnel allocation (e.g., how to allocate residents and doctors to every testing station?) and determine working hours(how many hours is each testing station open every day?). Therefore, according to the current policy of “dynamic zero clearing”, this paper proposes a new model of the nucleic acid sampling location problem to improve the sampling efficiency. Different from the traditional location-allocation problem, this model extends the traditional location-allocation problem, and further considers the optimization decisions of service capacity, staffing and working hours, as well as related constraints. The above problem is formulated as a nonlinear mixed integer model, which can be transformed into linear equivalence by adding variables and constraints.
Finally, the above linear equivalent model is solved by commercial software such as Cplex. In particular, in order to test how large a problem our solution method can solve in an acceptable time, we perform a grid test. The results show that: 1)As the problem size increases, the computational time continues to increase. The reason is that the expansion of the problem size leads to an increase in model constraints and variables, greatly increasing the computational time cost of the branch and bound algorithm in Cplex. 2)In a given time of 3,600 seconds, the largest problem we can solve with the Cplex software contains 525 nodes, which indicates that our model has certain applicability in real-world scenarios. Furthermore, our model is also applied to the nucleic acid sampling case of Shuangta Street in Suzhou City. The calculation results not only help the government obtain an optimal testing station location and personal allocation plan, but also verify the feasibility and effectiveness of our model.

Key words: COVID-19 epidemic, nucleic acid sampling, location, staffing, working time