Optimal Allocation of Surgery Blocks to Operating Rooms Under Uncertainty

Authors:
Brian T. Denton;Andrew J. Miller;Hari J. Balasubramanian;Todd R. Huschka
Affiliations:
Edward P. Fitts Department of Industrial and Systems Engineering, North Carolina State University, Raleigh, North Carolina 27695;IMB, Université Bordeaux 1, and RealOpt, INRIA Bordeaux Sud-Ouest, 33405 Talence, France;Department of Mechanical and Industrial Engineering, University of Massachusetts, Amherst, Massachusetts 01003;Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota 55905
Venue:
Operations Research
Year:
2010

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Abstract

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.