A Decomposition Algorithm for the Two-Stage Chance-Constrained Operating Room Scheduling Problem
The required time for surgical interventions in operating rooms (OR) may vary significantly from the predicted values depending on the type of operations being performed, the surgical team, and the patient. These deviations diminish the efficient utilization of OR resources and result in the disrupt...
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doaj-4c9d5b720d1c48958cbbe47e2cf7a5832021-03-30T02:40:42ZengIEEEIEEE Access2169-35362020-01-018801608017210.1109/ACCESS.2020.29910319079829A Decomposition Algorithm for the Two-Stage Chance-Constrained Operating Room Scheduling ProblemAmirhossein Najjarbashi0https://orcid.org/0000-0002-1963-0956Gino J. Lim1https://orcid.org/0000-0003-2259-2310Department of Industrial Engineering, University of Houston, Houston, TX, USADepartment of Industrial Engineering, University of Houston, Houston, TX, USAThe required time for surgical interventions in operating rooms (OR) may vary significantly from the predicted values depending on the type of operations being performed, the surgical team, and the patient. These deviations diminish the efficient utilization of OR resources and result in the disruption of projected surgery start times. This paper proposes a two-stage chance-constrained model to solve the OR scheduling problem under uncertainty. The goal is to minimize the costs associated with OR opening and overtime as well as reduce patient waiting times. The risk of OR overtime is controlled using chance constraints. Numerical experiments show that the proposed model provides a better trade-off between minimizing costs and reducing solution variability when compared to two existing models in the literature. It is also shown that the three models converge as the overtime probability threshold approaches one. Moreover, it is observed that the individual chance constraints result in the opening of fewer rooms, lower waiting times, and shorter solution times when compared to that of joint chance constraints. A decomposition algorithm is applied that solves large test instances of the OR scheduling problem, that of which is known to be NP-hard. Strong valid inequalities are derived in order to accelerate the convergence speed. The proposed approach outperformed both a commercial solver and a basic decomposition algorithm after solving all test instances with up to 89 surgeries and 20 ORs in less than 48 minutes.https://ieeexplore.ieee.org/document/9079829/Chance constraintsmixed-integer programmingoperating room schedulingtwo-stage stochastic programminguncertainty |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Amirhossein Najjarbashi Gino J. Lim |
spellingShingle |
Amirhossein Najjarbashi Gino J. Lim A Decomposition Algorithm for the Two-Stage Chance-Constrained Operating Room Scheduling Problem IEEE Access Chance constraints mixed-integer programming operating room scheduling two-stage stochastic programming uncertainty |
author_facet |
Amirhossein Najjarbashi Gino J. Lim |
author_sort |
Amirhossein Najjarbashi |
title |
A Decomposition Algorithm for the Two-Stage Chance-Constrained Operating Room Scheduling Problem |
title_short |
A Decomposition Algorithm for the Two-Stage Chance-Constrained Operating Room Scheduling Problem |
title_full |
A Decomposition Algorithm for the Two-Stage Chance-Constrained Operating Room Scheduling Problem |
title_fullStr |
A Decomposition Algorithm for the Two-Stage Chance-Constrained Operating Room Scheduling Problem |
title_full_unstemmed |
A Decomposition Algorithm for the Two-Stage Chance-Constrained Operating Room Scheduling Problem |
title_sort |
decomposition algorithm for the two-stage chance-constrained operating room scheduling problem |
publisher |
IEEE |
series |
IEEE Access |
issn |
2169-3536 |
publishDate |
2020-01-01 |
description |
The required time for surgical interventions in operating rooms (OR) may vary significantly from the predicted values depending on the type of operations being performed, the surgical team, and the patient. These deviations diminish the efficient utilization of OR resources and result in the disruption of projected surgery start times. This paper proposes a two-stage chance-constrained model to solve the OR scheduling problem under uncertainty. The goal is to minimize the costs associated with OR opening and overtime as well as reduce patient waiting times. The risk of OR overtime is controlled using chance constraints. Numerical experiments show that the proposed model provides a better trade-off between minimizing costs and reducing solution variability when compared to two existing models in the literature. It is also shown that the three models converge as the overtime probability threshold approaches one. Moreover, it is observed that the individual chance constraints result in the opening of fewer rooms, lower waiting times, and shorter solution times when compared to that of joint chance constraints. A decomposition algorithm is applied that solves large test instances of the OR scheduling problem, that of which is known to be NP-hard. Strong valid inequalities are derived in order to accelerate the convergence speed. The proposed approach outperformed both a commercial solver and a basic decomposition algorithm after solving all test instances with up to 89 surgeries and 20 ORs in less than 48 minutes. |
topic |
Chance constraints mixed-integer programming operating room scheduling two-stage stochastic programming uncertainty |
url |
https://ieeexplore.ieee.org/document/9079829/ |
work_keys_str_mv |
AT amirhosseinnajjarbashi adecompositionalgorithmforthetwostagechanceconstrainedoperatingroomschedulingproblem AT ginojlim adecompositionalgorithmforthetwostagechanceconstrainedoperatingroomschedulingproblem AT amirhosseinnajjarbashi decompositionalgorithmforthetwostagechanceconstrainedoperatingroomschedulingproblem AT ginojlim decompositionalgorithmforthetwostagechanceconstrainedoperatingroomschedulingproblem |
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