Optimizing Technician Staffing and Scheduling in Medical Procedure Services Using Two-Stage Stochastic Integer Programming

Technicians in medical procedure services are essential for ensuring smooth procedures. Widely seen in procedure rooms and operating rooms, fixed work shifts can cause a mismatch between technician demand and supply, resulting in overtime. It may compromise patient care quality and technician job satisfaction. While flexible shift scheduling can help balance workload, it remains challenging to determine the optimal technician team configuration to avoid both understaffing and overstaffing. The uncertainties of technician workload and paid time off (PTO) further complicate the problem. To address these challenges, we propose a two-stage stochastic programming model integrating staffing and scheduling decisions, while accounting for both workload and PTO uncertainties. We propose an algorithm based on Benders’ decomposition to identify high-quality solutions. Numerical experiments suggest that the proposed algorithm solves large-scale problems with high solution quality and faster speed than the direct use of Gurobi Optimizer. Our analysis also highlights the effectiveness of adopting 8- and 10-hour work shifts with different start times, and the benefits of incorporating PTO.