Patient Allocation Scheduling Based on Improved Sparrow Algorithm
In the outbreak of sudden public health events,a large number of patient groups and the limited supply of medical resources will form a sharp conflict,affecting the efficiency of epidemic prevention,control and treatment.In order to meet the demand of patients and improve the efficiency of medical resources,we establish a mathematical model with the optimization objectives of minimizing the average admission time and minimizing the maximum admission time,introduce the sine-cosine search strategy in the sparrow search algorithm to make the generated individuals have diversity,and add the t-distribution perturbation strategy to the optimal group to avoid the algorithm falling into the local optimum.We use the sparrow search algorithm,the improved sparrow search algorithm,and the quantum particle swarm algorithm to solve the mathematical model proposed,and compare and analyze the results.Taking city Y as an example,city Y is divided into 9 population areas and 6 hospitals for the simulation experimental study of patient allocation scheduling.The proposed math-ematical model can accurately describe the patient consultation problem during the pandemic,and the proposed algorithm can quickly give the patient treatment allocation plan,so that patients can get the corresponding rescue in a shorter time,and also avoid the further spread of infectious epidemics.
patient allocationmathematical modelimproved sparrow search algorithmpublic health emergenciesmulti-objective opti-mization