Scheduling surgery groups considering multiple downstream resources
| Autor: | Erwin W. Hans, A. J. Thomas Schneider, Mijke Carlier, J. Theresia van Essen |
|---|---|
| Přispěvatelé: | Mathematics of Operations Research, Center for Healthcare Operations Improvement and Research, Industrial Engineering & Business Information Systems |
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Surgery type clustering
medicine.medical_specialty Information Systems and Management General Computer Science Computer science Computation Master surgery scheduling 0211 other engineering and technologies Scheduling (production processes) UT-Hybrid-D ComputerApplications_COMPUTERSINOTHERSYSTEMS 02 engineering and technology Management Science and Operations Research Surgery scheduling Industrial and Manufacturing Engineering Scheduling (computing) Surgery procedure 0502 economics and business medicine OR in health services Resource allocation Integer programming 050210 logistics & transportation 021103 operations research 05 social sciences 22/2 OA procedure Surgery Modeling and Simulation Simulated annealing Ward and ICU occupancy |
| Zdroj: | European Journal of Operational Research, 282(2) European journal of operational research, 282(2), 741-752. Elsevier |
| ISSN: | 0377-2217 |
| Popis: | Surgery groups are clustered surgery procedure types that share comparable characteristics (e.g. expected duration). Scheduling OR blocks leaves many options for operational surgery scheduling and this increases the variation in usage of both the OR and downstream beds. Therefore, we schedule surgery groups to reduce the options for operational scheduling, ultimately bridging the gap between tactical and operational scheduling. We propose a single step mixed integer linear programming (MILP) approach that approximates the bed and OR usage and a simulated annealing approach. Both approaches are compared on a real-life data set and results show that the MILP performs best in terms of solution quality and computation time. Furthermore, the results show that our model may improve the OR utilization from 71% to 85% and decrease the bed usage variation from 53 beds to 11 beds compared to historical data. To show the potential and robustness of our model, we discuss several variants of the model requiring minor modifications. The use of surgery groups makes it easier to implementation our model in practice and, for operational planners, it is instantly clear where to schedule different types of surgery. |
| Databáze: | OpenAIRE |
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