EconPapers    
Economics at your fingertips  
 

Estimating operating room utilisation rate for differently distributed surgery times

Haim Shore

International Journal of Production Research, 2023, vol. 61, issue 2, 447-461

Abstract: A method is developed to determine the required sample size to estimate utilisation rate (UR) of a facility, where blocks of work processes/jobs with i.i.d execution times are consecutively executed, and different blocks possibly pursuing different distributions. It is assumed that within-block processes may be repetitive (constant work-content; execution time normally distributed), semi-repetitive (work-content somewhat varies between cycles) or memoryless (no characteristic work-content; exponentially distributed). Surgeries are known to comprise all three types of work processes. In this article, we use operating theatres as prototype facility to estimate UR, assuming that surgeries are allocated in blocks, in conformance with the specified scenario. A recently developed model for surgery duration, bridging the gap between duration models for repetitive and memoryless processes, is used to estimate UR. A database of ten thousand surgeries serve to compare sample sizes, calculated under normality (the traditional method) or lognormality, with the correct model-based values. The latter deviate appreciably from the former, corroborating the need for the new methodology.Abbreviations: OF: objective function; OR: operating room; SD: Surgery duration; SDD: Surgery duration distribution; UR: utilisation rate

Date: 2023
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/00207543.2021.2009141 (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:61:y:2023:i:2:p:447-461

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/TPRS20

DOI: 10.1080/00207543.2021.2009141

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 ().

 
Page updated 2025-03-20
Handle: RePEc:taf:tprsxx:v:61:y:2023:i:2:p:447-461