Surgery Sequencing Coordination with Recovery Resource Constraints
Miao Bai, Robert H. Storer, Gregory L. Tonkay (2021) Surgery Sequencing Coordination with Recovery Resource Constraints. INFORMS Journal on Computing 34(2):1207-1223. https://doi.org/10.1287/ijoc.2021.1089
54 Pages Posted: 18 Aug 2020 Last revised: 17 Aug 2022
Date Written: July 17, 2020
Abstract
Surgical practice administrators need to determine the sequence of surgeries and reserved operating room (OR) time for each surgery in the surgery scheduling process. Both decisions require coordination among multiple ORs and the recovery resource in the post-anesthesia care unit (PACU) in a surgical suite. Although existing studies have addressed OR time reservation, surgery sequencing coordination is an open challenge in the stochastic surgical environment. In this paper, we propose an algorithmic solution to this problem based on stochastic optimization. The proposed methodology involves the development of a surrogate objective function that is highly correlated with the original one. The resulting surrogate model has network-structured sub-problems after Lagrangian relaxation and decomposition, which makes it easier to solve than the impractically difficult original problem. We show that our proposed approach finds near-optimal solutions in small instances and outperforms benchmark methods by 13% to 51%, or equivalently an estimated saving of $760 to $7420 per day in surgical suites with 4 to 10 ORs. Our results illustrate a mechanism to alleviate congestion in the PACU. We also recommend that practice administrators prioritize sequencing coordination over the optimization of OR time reservation in an effort for performance improvement. Furthermore, we demonstrate how administrators should consider the impact of sequencing decisions when making strategic capacity adjustments for the PACU.
Keywords: Surgery Sequencing Coordination, Recovery Resource, Stochastic Optimization, Surrogate Model
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