A stochastic model for operating room planning under capacity constraints
Aida Jebali and
Ali Diabat
International Journal of Production Research, 2015, vol. 53, issue 24, 7252-7270
Abstract:
The present paper describes a two-stage stochastic programme for operating room planning that takes into account capacity constraints of three hospital resources: operating rooms, beds in the intensive care unit (ICU) and beds in the ward (or medium care unit). Operating room planning consists of deciding on the elective surgeries to perform over each period of the planning horizon, while considering uncertainties related to surgery duration as well as patient length of stay in the ICU and the ward. Sample average approximation is then used to solve the planning problem, aiming to minimise the sum of patient-related costs and expected resource utilisation costs. Computational experiments are conducted to evaluate the performance of the proposed solution method. The obtained results highlight the robustness of operating room plans obtained by a stochastic approach, in comparison to those generated by a deterministic approach, and the importance of considering both ICU and ward beds in operating room planning.
Date: 2015
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (14)
Downloads: (external link)
http://hdl.handle.net/10.1080/00207543.2015.1033500 (text/html)
Access to full text is restricted to subscribers.
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:taf:tprsxx:v:53:y:2015:i:24:p:7252-7270
Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/TPRS20
DOI: 10.1080/00207543.2015.1033500
Access Statistics for this article
International Journal of Production Research is currently edited by Professor A. Dolgui
More articles in International Journal of Production Research from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().