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Operating room pooling and parallel surgery processing under uncertainty
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Date
2011-03-01
Author
Batun, Sakine
Huschka, Todd R.
Schaefer, Andrew J.
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This work is licensed under a
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
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Operating room (OR) scheduling is an important operational problem for most hospitals. In this study, we present a novel two-stage stochastic mixed-integer programming model to minimize total expected operating cost given that scheduling decisions are made before the resolution of uncertainty in surgery durations. We use this model to quantify the benefit of pooling ORs as a shared resource and to illustrate the impact of parallel surgery processing on surgery schedules. Decisions in our model include the number of ORs to open each day, the allocation of surgeries to ORs, the sequence of surgeries within each OR, and the start time for each surgeon. Realistic-sized instances of our model are difficult or impossible to solve with standard stochastic programming techniques. Therefore, we exploit several structural properties of the model to achieve computational advantages. Furthermore, we describe a novel set of widely applicable valid inequalities that make it possible to solve practical instances. Based on our results for different resource usage schemes, we conclude that the impact of parallel surgery processing and the benefit of OR pooling are significant. The latter may lead to total cost reductions between 21% and 59% on average.
Subject Keywords
Operating room scheduling
,
Multiple operating rooms
,
Two-stage stochastic mixed-integer programs
,
Operating room pooling
,
Parallel surgery processing
URI
https://hdl.handle.net/11511/38162
Journal
INFORMS Journal on Computing
DOI
https://doi.org/10.1287/ijoc.1100.0396
Collections
Department of Industrial Engineering, Article
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S. Batun, T. R. Huschka, and A. J. Schaefer, “Operating room pooling and parallel surgery processing under uncertainty,”
INFORMS Journal on Computing
, pp. 220–237, 2011, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/38162.