The research objective of this award is to formulate, calibrate and solve operating room (OR) scheduling models that can adapt to unexpected events, such as unusually long procedures or patient ?no shows.? These models will produce initial schedules that will facilitate flexible reactions after the surgical day has begun, including moving procedures to different operating rooms and/or time slots. Unfortunately, the increased flexibility of such mathematical models comes at a cost: such models are very difficult to solve and existing techniques are sure to fail on even small examples. Deliverables will include generalizable insights into what aspects of OR schedules make them flexible, and a variety of widely applicable techniques for solving these difficult problems, as well as closely related planning problems under uncertainty arising in different domains.
Healthcare costs in the US continue to rise significantly, and surgery accounts for a large portion of these costs. If successful, the results of this research will provide approaches for the design of OR schedules that are adaptable and flexible in response to unexpected events. We anticipate that there are large opportunities for optimizing surgical procedures, resulting in significant savings. The educational impacts of this proposal will benefit multiple groups. We will incorporate results of this research into courses aimed at a variety of levels. Students and medical researchers will be also trained through mentoring and their direct involvement in the research work, including the participation of undergraduate students via the REU program.
