Optimal Allocation of Surgery Blocks to Operating Rooms Under Uncertainty

• Published in: Operations research 2010-07, Vol.58 (4-part-1), p.802-816
• Main Authors: Denton, Brian T., Miller, Andrew J., Balasubramanian, Hari J., Huschka, Todd R.
• Format: Article
• Language: English
• Subjects: Analysis; Applied sciences; Computer Science; Exact sciences and technology; Fixed costs; Flows in networks. Combinatorial problems; Heuristic; Heuristics; Mathematical optimization; Mathematical programming; Numerical analysis; Operating rooms; Operational research and scientific management; Operational research. Management science; Operations Research; Optimal solutions; optimization; Optimization algorithms; Overtime; Overtime costs; Scheduling; Stochastic models; Stochastic programming; Studies; Surgery; Surgical specialties; Uncertainty
• ISSN: 0030-364X; 1526-5463
• DOI: 10.1287/opre.1090.0791

The allocation of surgeries to operating rooms (ORs) is a challenging combinatorial optimization problem. There is also significant uncertainty in the duration of surgical procedures, which further complicates assignment decisions. In this paper, we present stochastic optimization models for the assignment of surgeries to ORs on a given day of surgery. The objective includes a fixed cost of opening ORs and a variable cost of overtime relative to a fixed length-of-day. We describe two types of models. The first is a two-stage stochastic linear program with binary decisions in the first stage and simple recourse in the second stage. The second is its robust counterpart, in which the objective is to minimize the maximum cost associated with an uncertainty set for surgery durations. We describe the mathematical models, bounds on the optimal solution, and solution methodologies, including an easy-to-implement heuristic. Numerical experiments based on real data from a large health-care provider are used to contrast the results for the two models and illustrate the potential for impact in practice. Based on our numerical experimentation, we find that a fast and easy-to-implement heuristic works fairly well, on average, across many instances. We also find that the robust method performs approximately as well as the heuristic, is much faster than solving the stochastic recourse model, and has the benefit of limiting the worst-case outcome of the recourse problem.