Scheduling Optimization Study of Intelligent Mixing Algorithm for People in Public Places Based on Cross-Infection Risk Consideration

As China's sudden public health events step into normalized prevention and control, public places are gradually developed to the public. However, as the virus still continues to mutate, there are still certain difficulties and problems in the management of public places, especially the optimization of personnel scheduling in public places. At present, it is difficult to find a relatively good balance between service quality, labor costs, and employee satisfaction in personnel scheduling arrangements in public places. In order to intelligently optimize personnel scheduling in public places, the main factors affecting the staff scheduling arrangement and the relationships between different factors are studied and analyzed firstly, and then a staff scheduling model for public places is constructed based on the consideration of the risk of cross-infection. In order to solve the personnel scheduling model in public places, the study took different measures and also designed four different algorithms, and better performing algorithms were selected through experiments. The results show that the staff scheduling model constructed by the study can solve the scheduling scheme better, and the gap between the solved value and the exact value of the designed hybrid algorithm on the small-scale problem is 0.40%, and the gaps of the other three algorithms are 1.25%, 5.45%, and 0.44%. At a medium-sized scale, the intelligent hybrid algorithm designed by the research institute converged after nearly 36 iterations, while the other three algorithms began to converge after nearly 42, 142, and 38 iterations, respectively. It can be seen that the intelligent hybrid algorithm designed by the research institute has better performance, solving accuracy and speed. The results of the study can help public places develop a staff scheduling program that considers the risk of cross-infection to protect the lives of the people.