Participants: Maya Bam, Brian Denton, Mark Van Oyen, Mark Cowen
Project Contact: Maya Bam, [email protected]
Project Synopsis: Surgery scheduling is complicated by the post-anesthesia care unit, the typical recovery resource. Based on collaboration with a hospital, we developed a fast 2-phase heuristic that considers both surgery and recovery resources. Each phase of the heuristic has a tight provable worst-case performance bound. Moreover, the heuristic performs well compared to optimization based methods when evaluated under uncertainty using a discrete event simulation model. The heuristic is also easy to understand and implement, thus lowering the barrier on implementation.
Papers, Posters, & Presentations:
Papers
- M. Bam, B. Denton, M. Van Oyen, and M. Cowen: Surgery Scheduling with Recovery Resources (under review)
Presentations
- Stochastic Operating Room Planning with Recovery Flow, INFORMS Healthcare Conference, July 2015
- Stochastic Surgery Scheduling with Recovery Flow, INFORMS Annual Meeting, November 2014
- An Optimization Model for Surgery Scheduling with Limited Recovery Resources, INFORMS Annual Meeting, October 2013
Posters
- Surgery Scheduling with Recovery Resources, INFORMS Annual Meeting, November 2015
- An Optimization Model for Surgery Scheduling with Limited Recovery Resources, INFORMS Healthcare Conference, June 2014
Acknowledgements: We thank Jennifer Czerwinski for her significant efforts to create the data set that was used to conduct this research.
This research is also based in part upon work supported by the National Science Foundation under Grant Number CMMI 0844511 (Denton). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.