Operating rooms (OR) are one of the most expensive parts of a hospital with complex processes, and the efficient use of resources is of utmost importance. Therefore, proper management and operation of operating rooms are extremely crucial. OR scheduling ensures that the surgeries are performed at the proper time, patients are treated effectively and safely, resources are used effectively, and staff is increased in work efficiency. Furthermore, accurately scheduled surgeries are safer for patients' healing processes. This is dependent on factors such as the availability of qualified personnel at the appropriate time, the readiness of surgical equipment, and the provision of proper sterilization and hygienic conditions. Surgical team scheduling ensures that surgeries begin on time, are completed effectively, and patients are treated safely. It is also critical to reduce employee fatigue and balance the workload. As a result, integrating surgical teams into operating room scheduling problems provides significant benefits. Accordingly, 29 research articles focusing on the problem of OR scheduling, within the scope of constraints on surgical team members, scheduling strategies, uncertainties, and solution methods, are thoroughly reviewed in this study.

The paper discusses the advanced simulated annealing metaheuristic approach to solving the difficult operating room (OR) scheduling issue. A detailed mathematical formulation for the multi-day OR scheduling problem is presented that takes into account patient urgency scores, surgeon compatibility, room capacity limits, and time restrictions. Realizing that exact methods would be computationally untenable for larger-sized instances, we propose a highly sophisticated simulated annealing algorithm that uses a new way of representing solutions, makes strategic neighborhood moves, and applies adaptive penalty functions. The algorithm shows solid performance over many different scales of problems and easily deals with instances that mixed-integer linear programming methods find prohibitive. Computational tests have shown that the method suggested secures high-quality solutions while keeping computational cost low, thus giving hospitals a useful tool for increasing OR scheduling efficiency.

In a pioneering way, this thorough research develops and tests a new mathematical programming framework for Human Factors and Ergonomics (HFE) optimization in hospitals. The main focus is a mixed-integer linear programming model that takes into account, at the same time, operating room scheduling on the basis of patient safety, staff well-being, operational efficiency, and resource allocation. By conducting very large computational trials through the MATLAB optimization toolbox, we show the model's ability to produce schedules that not only put critical clinical tasks first but also keep the staff workloads balanced. Our findings demonstrate different fundamental aspects: optimal solutions in a way prioritize high-risk procedures, disclose the natural capacities of the system, and point out where the workflow can be improved. The optimization has been able to assign all the critical tasks (appendectomy, intubation, and code blue) without workload imbalances among the clinical staff. Nonetheless, it also revealed that the resources indeed were not fully utilized and thus more patients could be treated. This study is a very reliable tool for the healthcare managers to make evidence-based scheduling decisions that are in the time period of the hospitals and are reconcilable with the other objectives in the complex clinical environment as well.