172. Elective Case Scheduling under Surgery Duration Uncertainty
Invited abstract in session TC-3: Optimisation, stream Sessions.
Tuesday, 13:30-15:00Room: NTNU, Realfagbygget R9
Authors (first author is the speaker)
| 1. | Sonja Weiland
|
| University of Technology Nuremberg (UTN) | |
| 2. | Martina Kuchlbauer
|
| Fraunhofer Institute for Integrated Circuits IIS | |
| 3. | Lorenza Moreno
|
| Computer Science, Federal University of Juiz de Fora | |
| 4. | Alexander Müller
|
| OrgaCard Siemantel & Alt GmbH |
Abstract
At the operational level, a central task of the operating room management is to plan upcoming elective surgeries. In the literature, this problem is referred to as Elective Case Scheduling (ECS). We develop a decision support tool for the ECS with a planning horizon of one day and uncertain surgery durations. For each planned surgery, we are given a discrete probability distribution of the surgery duration. These distributions are obtained using a machine learning approach applied to historical data from a large German hospital. We model the ECS as a mixed-integer program that determines the operating room and the starting time for each scheduled surgery, subject to resource constraints. In our baseline model, the expected values of the surgery durations are used as parameters of this mixed-integer program. To account for the uncertainty of the surgery durations more accurately, we add chance constraints to the model. So, we can control the probability that at most a certain number of overtime hours occur. We compare the baseline model and the chance constraint model.
Keywords
- Operating room planning and scheduling
- Optimization algorithms
- Decision support
Status: accepted
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