In this paper, operating room planning and scheduling problems have been studied. In operating room planning, the allocation of patients to operating rooms and their sequencing are critical in determining the performance of operating rooms. In this paper, three surgery scheduling decisions are considered, including the number of operating rooms to open, the allocation of surgeries to operating rooms, and the sequencing of surgeries in allocated operating rooms. All the surgeries under consideration are elective, and surgery durations are considered deterministic. Further, it is considered that the surgeries have different specialties, and each operating room can accommodate a particular specialty of surgeries, i.e., heterogeneous operating rooms are considered in the current study. Before performing a surgery, setup time is required for operating room turnover and sterilization, and it is considered sequence dependent. A mixed integer nonlinear programming (MINLP) model is developed to minimize the overtime costs of operating rooms for allocation and surgery sequencing with sequence dependent setup times. An outer approximation (OA) method is proposed to solve the problem near optimally. Experiments are conducted to compare the performance of the proposed OA method with the standard mixed integer nonlinear programming model. Computational results show the efficiency of the proposed OA method. Later, a case data from a case hospital is collected and a case study is solved.
Planning and scheduling critical resources in hospitals is significant for better service and profit generation. The current research investigates an integrated planning and scheduling problem at different levels of operating rooms, intensive care units, and wards. The theory of constraints is applied to make plans and schedules for operating rooms based on the capacity constraints of the operating room itself and downstream wards. A mixed integer linear programming model is developed considering shifting bottleneck resources among the operating room, intensive care unit, and hospital wards to maximize the utilization of resources at all levels of planning. Different sizes of planning and scheduling problems of the hospital, including small, medium, and large sizes, are created with variable arrivals and surgery durations and solved using a CPLEX solver for validating the developed models. Later, the application of the proposed models in the real world to develop planning systems for hospitals is discussed, and future extensions are suggested.
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