Improved utilization of scarce resources such as health care personnel is necessary to address well-known problem of long waiting times within the health care. Implementing mathematically modeled scheduling in the operating theatre has the potential to result in more efficient allocation of resources and financial gains. Despite the promising results, the adoption rate of such models is low. This thesis examines the impact of a mixed-integer linear programming model using an overlapping strategy. We perform a computational experiment where both sequential and parallel schedules are produced with real surgery data from an orthopedic department at a Swedish university hospital. The generated schedules are compared against each other in measurements of cost productivity. Statistical analysis shows that there is a statistical significant difference between the two schedules, favoring the optimized schedule. The results further suggest that three operating rooms and four surgery teams is the most optimal combination of the 18 combinations analyzed, where operating rooms and surgery teams varies between 1-4 and 1-6, respectively.