140. Multi-Resource Elective Case Scheduling under Uncertainty
Contributed abstract in session TC-1: Poster session, stream Posters.
Tuesday, 14:00-15:30Room: Auditorium
Authors (first author is the speaker)
| 1. | Sonja Weiland
|
| University of Technology Nuremberg (UTN) | |
| 2. | Dominik Grimaldi
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| OrgaCard Siemantel & Alt GmbH | |
| 3. | Alexander Martin
|
| Liberal Arts and Sciences, University of Technology Nuremberg (UTN) | |
| 4. | Lorenza Moreno
|
| Computer Science, Federal University of Juiz de Fora | |
| 5. | Alexander Müller
|
| OrgaCard Siemantel & Alt GmbH |
Abstract
At the operational level, a central task in the operating room management is to plan upcoming elective surgeries. In the literature, this problem is referred to as Elective Case Scheduling (ECS). We consider the ECS with a planning horizon of one day and multiple resources such as anesthesiologists and trays and develop a decision support tool. Collaboration with German hospitals of different sizes provides us with both practitioners` requirements for the scheduling process and historical data.
First, we consider the deterministic ECS, where we assume that for every planned surgery its duration is given. We model this problem as a mixed-integer program that determines the operating room and the starting time for every scheduled surgery, subject to resource constraints. The main objectives are to minimize overtime, minimize the number of medical department changes within an operating room, and produce a schedule that can be adapted well to incoming emergencies. Using a commercial solver, we obtain solutions that meet the requirements of the practice.
We extend this approach to the stochastic version of the ECS, where the surgery duration is assumed to be uncertain. Applying a machine learning approach to the historical data yields a discrete probability distribution of the surgery duration for each planned surgery. Based on this information about the distributions, we want to develop a stochastic optimization model.
Keywords
- Operating room planning and scheduling
- Decision support
- Optimization algorithms
Status: accepted
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