ArticleHealth care management science2022
A two-stage robust optimization approach for the master surgical schedule problem under uncertainty considering downstream resources.
Article in Health care management science, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Leveraging the potential of the German operating room benchmarking initiative for planning: A ready-to-use surgical process data set.Health care management science · 2024Article
- Assessing resource allocation based on workload: a data envelopment analysis study on clinical departments in a class a tertiary public hospital in China.BMC health services research · 2023Article
- Robust Operating Room Scheduling Model with Violation Probability Consideration under Uncertain Surgery Duration.International journal of environmental research and public health · 2022Article
- Balancing control and autonomy in master surgery scheduling: Benefits of ICU quotas for recovery units.Health care management science · 2022Article
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This paper addresses a planning decision for operating rooms (ORs) that aim at supporting hospital management. Focusing on elective patients, we determined the master surgical schedule (MSS) on a one-week time horizon. We assigned the specialties to available sessions and allocated surgeries to them while taking into consideration the priorities of the outpatients in the ambulatory surgical discipline. Surgeries were selected from the waiting lists according to their priorities. The proposed approach considered operating theater (OT) restrictions, patients' priorities and accounted for the availability of both intensive care unit (ICU) beds and post-surgery beds. Since the management decisions of hospitals are usually made in an uncertain environment, our approach considered the uncertainty of surgery duration and availability of ICU bed. Two robust optimization approaches that kept the model computationally tractable are described and applied to deal with uncertainty. Computational results based on a medium-sized French hospital archives have been presented to compare the robust models to the deterministic counterpart and to demonstrate the price of robustness.
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