Data‑driven hospital personnel scheduling optimization through patients prediction

With the rapid development of the modern city, technologies of smart cities are indispensable for solving urban problems. Medical services are one of the key areas related to the lives of urban residents. In particular, how to effectively manage the human resources of a hospital is a complex and challenging problem to improve treatment capabilities. Due to the grievous shortage of medical personnel, hospitals have to make quality schedules to improve the efficiency of the hospital and the utilization rate of human resources. Although there have been a large number of researches on hospital staff scheduling, few people also consider future patient population forecasts, doctor scheduling and hospital structure. These factors are very important in the hospital staff scheduling problem. Concerning this, this paper establishes an optimization system combining a two-layer mixed-integer linear programming and an extended prophet model for the hospital personnel scheduling. The model considers factors such as weather, disease types, number of patients, room resources, doctor resources, working hours, etc., and can quickly obtain a timetable with complex constraints. Finally, the convergence and the practicability of the model has been verified with real data from a hospital in China