Journal article
The effect of few historical data on the performance of sample average approximation method for operating room scheduling
International transactions in operational research, Vol.30(1), pp.126-150
01/2023
DOI: 10.1111/itor.13101
Abstract
We model the scheduling problem of a single operating room for outpatient surgery, with uncertain case durations and an objective function comprising waiting time, idle time, and overtime costs. This stochastic scheduling problem has been studied in diverse forms. One of the most common approaches used is the sample average approximation (SAA). Our contribution is to study the use of SAA to solve this problem under few historical data using families of log t distributions with varying degrees of freedom. We analyze the results of the SAA method in terms of optimality convergence, the effect of the number of scenarios, and average computational time. Given the case sequence, computational results demonstrate that SAA with an adequate number of scenarios performs close to the exact method. For example, we find that the optimality gap, in units of proportional weighted time, is relatively small when 500 scenarios are used: 99% of the instances have an optimality gap of less than 2.6 7% (1.74%, 1.23%) when there are 3 (9, many) historical samples. Increasing the number of SAA scenarios improves performance, but is not critical when the case sequence is given. However, choosing the number of SAA scenarios becomes critical when the same method is used to choose among sequencing heuristics when there are few historical data. For example, when there are only three (nine, many) historical samples, 99% of the instances have less than 25.38% (13.15%, 6.87%) penalty in using SAA with 500 scenarios to choose the best sequencing heuristic.
Details
- Title: Subtitle
- The effect of few historical data on the performance of sample average approximation method for operating room scheduling
- Creators
- Elvin Coban - Department of Industrial Engineering Ozyegin University Istanbul 34794 TurkeyEnis Kayış - Department of Industrial Engineering Ozyegin University Istanbul 34794 TurkeyFranklin Dexter - Department of Anesthesia University of Iowa Iowa City IA 52242 USA
- Resource Type
- Journal article
- Publication Details
- International transactions in operational research, Vol.30(1), pp.126-150
- DOI
- 10.1111/itor.13101
- ISSN
- 0969-6016
- eISSN
- 1475-3995
- Language
- English
- Electronic publication date
- 01/03/2022
- Date published
- 01/2023
- Academic Unit
- Health Management and Policy; Anesthesia
- Record Identifier
- 9984209493902771
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