In collaboration with a hospital, researchers examined problems related to the timing and scheduling of surgeries and patients’ stays in recovery units. They developed an integrated elective surgery assignment, sequencing, and scheduling problem (ESASSP) and devised new ways to solve it. Implementing solutions based on the study’s models could significantly reduce congestion in recovery units, delays in operating rooms, overtime, idle time, and costs, the authors conclude.
The study was conducted by researchers at Carnegie Mellon University, the University of Southern California (USC), Texas Tech University, and the Medical University of South Carolina. It is published in the European Journal of Operational Research .
“Our findings offer valuable insights into the ESASSP and demonstrate the practical impact of our integrated approaches,” says Rema Padman, professor of management science and healthcare informatics at Carnegie Mellon’s Heinz College, who coauthored the study.
The ESASSP is an important resource-constrained scheduling problem that presents a number of fundamental challenges, according to the study’s authors. Among them are:
Based on these challenges, the authors suggest that hospitals could benefit greatly from computationally efficient, integrated approaches for solving the ESASSP.
In their study, which evaluated three sets of surgery data, the authors proposed and analyzed distributionally robust optimization (DRO) approaches for the ESASSP. Specifically, they formulated a DRO model, which identified ESASSP decisions that minimized the fixed costs associated with performing or postponing surgeries plus the maximum expectation of the operational costs. Then they evaluated the worst-case expectation over probability distributions within predefined ambiguity sets of possible probability distributions for surgery durations and lengths of stay. They also analyzed a stochastic programming model, finding ESASSP decisions that minimized the fixed and expected operational costs.
Through a comprehensive computational analysis of three sets of real-world surgical data, the study offers the following insights for researchers and practitioners:
The authors caution that while their proposed models could provide guidelines for practitioners to make ESASSP decisions, they are not directly implementable in practice without further work to develop and test tools that ensure successful adaptation—especially since most hospitals lack staff with expertise in optimization.
“Prior studies have tackled isolated components of the ESASSP,” explains Karmel S. Shehadeh, assistant professor of industrial and systems engineering at USC, who led the study. “Ours introduces the first models that account for uncertainties and ambiguities in surgery durations and post-operative lengths of stay in recovery units, while also addressing the challenges of optimizing surgery schedules under the limited capacities of the ICU and other wards.”
European Journal of Operational Research
Operating room-to-downstream elective surgery planning under uncertainty
5-Aug-2025