The effect of few historical data on the performance of sample average approximation method for operating room scheduling

Name: The effect of few historical data on the performance of sample average approximation method for operating room scheduling
Author: Göktürk, Elvin Çoban, Kayış, Enis, Dexter, F.

İsim	The effect of few historical data on the performance of sample average approximation method for operating room scheduling
Yazar	Göktürk, Elvin Çoban, Kayış, Enis, Dexter, F.
Basım Tarihi:	2023-01
Basım Yeri	- Wiley
Konu	Few historical data, Operating room scheduling, Sample average approximation, Stochastic modeling
Tür	Süreli Yayın
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	0969-6016
Kayıt Numarası	64e761d9-ffce-4874-89df-da3f784fc621
Lokasyon	Industrial Engineering
Tarih	2023-01
Örnek Metin	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.
DOI	10.1111/itor.13101
Cilt	30

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The effect of few historical data on the performance of sample average approximation method for operating room scheduling

Yazar Göktürk, Elvin Çoban, Kayış, Enis, Dexter, F.

Basım Tarihi 2023-01

Basım Yeri - Wiley

Konu Few historical data, Operating room scheduling, Sample average approximation, Stochastic modeling

Tür Süreli Yayın

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 0969-6016

Kayıt Numarası 64e761d9-ffce-4874-89df-da3f784fc621

Lokasyon Industrial Engineering

Tarih 2023-01

Örnek Metin 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.

DOI 10.1111/itor.13101

Cilt 30