A stochastic programming approach to perform hospital capacity assessments

This article introduces a bespoke risk averse stochastic programming approach for performing a strategic level assessment of hospital capacity (QAHC). We include stochastic treatment durations and length of stay in the analysis for the first time. To the best of our knowledge this is a new capabilit...

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Published inPloS one Vol. 18; no. 11; p. e0287980
Main Authors Burdett, Robert L., Corry, Paul, Spratt, Belinda, Cook, David, Yarlagadda, Prasad
Format Journal Article
LanguageEnglish
Published San Francisco Public Library of Science 09.11.2023
Public Library of Science (PLoS)
Subjects
Algorithms
Analysis
Decision making
Health care policy
Heuristic methods
Hospital size
Hospitals
Integer programming
Investment analysis
Length of stay
Medicine and Health Sciences
Methods
Mixed integer
Pandemics
Patients
Physical Sciences
Random variables
Research and Analysis Methods
Stochastic programming
Australia
Online AccessGet full text
ISSN1932-6203
1932-6203
DOI10.1371/journal.pone.0287980

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Abstract This article introduces a bespoke risk averse stochastic programming approach for performing a strategic level assessment of hospital capacity (QAHC). We include stochastic treatment durations and length of stay in the analysis for the first time. To the best of our knowledge this is a new capability, not yet provided in the literature. Our stochastic programming approach identifies the maximum caseload that can be treated over a specified duration of time subject to a specified risk threshold in relation to temporary exceedances of capacity. Sample averaging techniques are applied to handle probabilistic constraints, but due to the size and complexity of the resultant mixed integer programming model, a novel two-stage hierarchical solution approach is needed. Our two-stage hierarchical solution approach is novel as it combines the application of a meta-heuristic with a binary search. It is also computationally fast. A case study of a large public hospital has been considered and extensive numerical tests have been undertaken to highlight the nuances and intricacies of the analysis. We conclude that the proposed approach is effective and can provide extra clarity and insights around hospital outputs. It provides a way to better calibrate hospitals and other health care infrastructure to future demands and challenges, like those created by the COVID pandemic.
AbstractList This article introduces a bespoke risk averse stochastic programming approach for performing a strategic level assessment of hospital capacity (QAHC). We include stochastic treatment durations and length of stay in the analysis for the first time. To the best of our knowledge this is a new capability, not yet provided in the literature. Our stochastic programming approach identifies the maximum caseload that can be treated over a specified duration of time subject to a specified risk threshold in relation to temporary exceedances of capacity. Sample averaging techniques are applied to handle probabilistic constraints, but due to the size and complexity of the resultant mixed integer programming model, a novel two-stage hierarchical solution approach is needed. Our two-stage hierarchical solution approach is novel as it combines the application of a meta-heuristic with a binary search. It is also computationally fast. A case study of a large public hospital has been considered and extensive numerical tests have been undertaken to highlight the nuances and intricacies of the analysis. We conclude that the proposed approach is effective and can provide extra clarity and insights around hospital outputs. It provides a way to better calibrate hospitals and other health care infrastructure to future demands and challenges, like those created by the COVID pandemic.
This article introduces a bespoke risk averse stochastic programming approach for performing a strategic level assessment of hospital capacity (QAHC). We include stochastic treatment durations and length of stay in the analysis for the first time. To the best of our knowledge this is a new capability, not yet provided in the literature. Our stochastic programming approach identifies the maximum caseload that can be treated over a specified duration of time subject to a specified risk threshold in relation to temporary exceedances of capacity. Sample averaging techniques are applied to handle probabilistic constraints, but due to the size and complexity of the resultant mixed integer programming model, a novel two-stage hierarchical solution approach is needed. Our two-stage hierarchical solution approach is novel as it combines the application of a meta-heuristic with a binary search. It is also computationally fast. A case study of a large public hospital has been considered and extensive numerical tests have been undertaken to highlight the nuances and intricacies of the analysis. We conclude that the proposed approach is effective and can provide extra clarity and insights around hospital outputs. It provides a way to better calibrate hospitals and other health care infrastructure to future demands and challenges, like those created by the COVID pandemic.This article introduces a bespoke risk averse stochastic programming approach for performing a strategic level assessment of hospital capacity (QAHC). We include stochastic treatment durations and length of stay in the analysis for the first time. To the best of our knowledge this is a new capability, not yet provided in the literature. Our stochastic programming approach identifies the maximum caseload that can be treated over a specified duration of time subject to a specified risk threshold in relation to temporary exceedances of capacity. Sample averaging techniques are applied to handle probabilistic constraints, but due to the size and complexity of the resultant mixed integer programming model, a novel two-stage hierarchical solution approach is needed. Our two-stage hierarchical solution approach is novel as it combines the application of a meta-heuristic with a binary search. It is also computationally fast. A case study of a large public hospital has been considered and extensive numerical tests have been undertaken to highlight the nuances and intricacies of the analysis. We conclude that the proposed approach is effective and can provide extra clarity and insights around hospital outputs. It provides a way to better calibrate hospitals and other health care infrastructure to future demands and challenges, like those created by the COVID pandemic.
Audience Academic
Author Yarlagadda, Prasad
Corry, Paul
Burdett, Robert L.
Spratt, Belinda
Cook, David
AuthorAffiliation 3 Princess Alexandra Hospital, Woolloongabba, Brisbane, Qld, Australia
Cyprus International University Faculty of Engineering: Uluslararasi Kibris Universitesi Muhendislik Fakultesi, TURKEY
2 School of Mechanical, Medical & Process Engineering, Queensland University of Technology, Brisbane, Qld, Australia
1 School of Mathematical Sciences, Queensland University of Technology, Brisbane, Qld, Australia
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Cites_doi 10.1016/j.omega.2017.04.006
10.1057/jors.2011.39
10.1007/s10957-012-0074-x
10.1016/j.cor.2019.104834
10.1016/B978-0-323-88506-5.50083-8
10.1016/j.ejor.2018.08.037
10.1007/s10729-012-9219-6
10.1016/j.cie.2018.04.047
10.1109/TSG.2019.2903767
10.1007/s10957-010-9754-6
10.1109/ACCESS.2020.2990726
10.1109/ACCESS.2015.2509013
10.1016/j.ejor.2016.11.043
10.1016/j.ejor.2017.05.007
10.1016/j.ejor.2010.03.014
10.1016/j.ijpe.2020.107764
10.1016/j.ejor.2017.01.026
10.1016/j.ejor.2015.03.020
10.1016/j.eswa.2017.01.050
10.1016/j.ejor.2017.06.051
10.1007/s10729-019-09476-2
10.1007/s10107-017-1172-1
10.1016/j.tre.2019.04.011
10.1016/j.cor.2021.105224
10.1007/s10729-015-9337-z
10.1007/s10696-019-09349-4
10.1016/j.ejor.2019.11.039
10.1016/j.ejor.2019.01.036
10.1111/poms.12941
10.1016/j.ejor.2010.06.018
10.1287/ijoc.1090.0372
10.1056/NEJMsb2005114
10.1504/IJOR.2008.019170
10.1137/S1052623499363220
10.4203/ijrt.4.4.4
10.1016/j.omega.2017.11.005
10.1007/s00291-008-0143-6
10.1007/s10479-018-3091-9
10.1021/acs.iecr.6b02683
10.1007/s10479-017-2662-5
10.1016/j.ejor.2015.12.033
10.1016/j.cie.2017.07.015
10.1016/j.ejor.2016.05.041
10.1007/s10107-018-1295-z
10.1016/S0305-0548(00)00076-9
10.1111/poms.13012
10.1016/j.ejor.2014.04.036
10.1016/j.ijmedinf.2020.104174
10.1016/j.ejor.2019.06.023
10.1007/s10729-015-9342-2
10.1080/00207543.2015.1033500
10.1016/j.omega.2019.07.002
10.1007/978-1-4939-1384-8_8
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References S McRae (pone.0287980.ref023) 2018; 23
L Zhou (pone.0287980.ref028) 2017; 81
KS Shehadeh (pone.0287980.ref039) 2020; 283
Y. Deng (pone.0287980.ref009) 2016
Y Wang (pone.0287980.ref040) 2019; 273
J Zhang (pone.0287980.ref041) 2019; 276
A Goli (pone.0287980.ref054)
V Valdmanis (pone.0287980.ref021) 2010; 207
A Jebali (pone.0287980.ref033) 2017; 114
PM Esfahani (pone.0287980.ref053) 2018; 171
RL Burdett (pone.0287980.ref058) 2018; 264
X Chen (pone.0287980.ref022) 2015; 4
A Moosavi (pone.0287980.ref035) 2018; 120
S Hof (pone.0287980.ref004) 2017; 20
L Adam (pone.0287980.ref008) 2020; 292
B Bevrani (pone.0287980.ref017) 2020; 32
J Kudela (pone.0287980.ref047) 2020; 8
A Jebali (pone.0287980.ref032) 2015; 53
RL Burdett (pone.0287980.ref060) 2021; 130
RL Burdett (pone.0287980.ref001) 2016; 255
JD Hawkinson (pone.0287980.ref007) 2018; 27
MC Campi (pone.0287980.ref046) 2011; 148
R Qiu (pone.0287980.ref052)
KS Shehadeh (pone.0287980.ref038) 2019; 279
GM Ostrovsky (pone.0287980.ref048) 2017; 56
FB Abdelaziz (pone.0287980.ref026) 2012; 63
B Bevrani (pone.0287980.ref016) 2020
ZL Chen (pone.0287980.ref055) 2002; 29
RL Burdett (pone.0287980.ref018) 2015; 4
RL Burdett (pone.0287980.ref056) 2010; 32
B Pagnoncelli (pone.0287980.ref045) 2012; 155
JA Paul (pone.0287980.ref024) 2008; 3
RL Burdett (pone.0287980.ref019) 2015; 245
JP Watson (pone.0287980.ref043) 2010; 22
B Bevrani (pone.0287980.ref015) 2017; 263
EJ Emanual (pone.0287980.ref002) 2020; 382
AJ Kleywegt (pone.0287980.ref011) 2002; 12
RL Burdett (pone.0287980.ref020) 2016; 251
Z Yahia (pone.0287980.ref027) 2016; 19
PJH Hulshof (pone.0287980.ref006) 2013; 16
S Ahmed (pone.0287980.ref044) 2018; 170
J Zhang (pone.0287980.ref042) 2020; 229
D Min (pone.0287980.ref036) 2010; 206
K Baker (pone.0287980.ref050) 2019; 10
C Gicquel (pone.0287980.ref049) 2018; 264
AR Andersen (pone.0287980.ref029) 2017; 260
S Alodhaibi (pone.0287980.ref014) 2020
A Ghaderi (pone.0287980.ref010) 2019; 127
RL Burdett (pone.0287980.ref057) 2014; 238
N Liu (pone.0287980.ref034) 2019; 28
S McRae (pone.0287980.ref031) 2020; 97
RL Burdett (pone.0287980.ref059) 2020; 115
RL Burdett (pone.0287980.ref003) 2017; 77
S Neyshabouri (pone.0287980.ref037) 2017; 260
BRP Oliveira (pone.0287980.ref025) 2020; 141
B You (pone.0287980.ref051) 2021
(pone.0287980.ref005) 2019
N Freeman (pone.0287980.ref030) 2018; 76
S Kim (pone.0287980.ref012) 2015
C Li (pone.0287980.ref013) 2021; 2
References_xml – volume: 76
  start-page: 160
  year: 2018
  ident: pone.0287980.ref030
  article-title: An iterative approach for case mix planning under uncertainty.
  publication-title: Omega
  doi: 10.1016/j.omega.2017.04.006
– volume: 63
  start-page: 530
  issue: 4
  year: 2012
  ident: pone.0287980.ref026
  article-title: A multi-objective stochastic program for hospital bed planning
  publication-title: Journal of the Operational Research Society
  doi: 10.1057/jors.2011.39
– volume: 155
  start-page: 707
  year: 2012
  ident: pone.0287980.ref045
  article-title: Risk-return trade-off with the scenario approach in practice: A case study in portfolio selection
  publication-title: Journal of Optimisation Theory and Applications
  doi: 10.1007/s10957-012-0074-x
– volume: 115
  start-page: 104834
  year: 2020
  ident: pone.0287980.ref059
  article-title: A flexible job shop scheduling approach with operators for coal export terminals–a mature approach.
  publication-title: Computers and Operational Research
  doi: 10.1016/j.cor.2019.104834
– year: 2021
  ident: pone.0287980.ref051
  article-title: Joint chance constraint approach based on data-driven models for optimization under uncertainty applied to the Williams-Otto Process.
  publication-title: Proceedings of the 31st European Symposium on Computer Aided Process Engineering (ESCAPE31), June 6–9, İstanbul, Turkey.
  doi: 10.1016/B978-0-323-88506-5.50083-8
– volume: 273
  start-page: 740
  issue: 2
  year: 2019
  ident: pone.0287980.ref040
  article-title: A distributionally robust optimization approach for surgery block allocation
  publication-title: European Journal of Operational Research
  doi: 10.1016/j.ejor.2018.08.037
– volume: 16
  start-page: 152
  year: 2013
  ident: pone.0287980.ref006
  article-title: Tactical resource allocation and elective patient admission planning in care pathways.
  publication-title: Health Care Man. Sci
  doi: 10.1007/s10729-012-9219-6
– volume: 120
  start-page: 216
  year: 2018
  ident: pone.0287980.ref035
  article-title: Scheduling of elective patients considering upstream and downstream units and emergency demand using robust optimization
  publication-title: Computers & Industrial Engineering.
  doi: 10.1016/j.cie.2018.04.047
– year: 2016
  ident: pone.0287980.ref009
  article-title: Decomposition algorithms and parallel computing for chance-constrained and stochastic integer programs with applications. PhD diss
  publication-title: University of Michigan
– volume: 10
  start-page: 6376
  issue: 6
  year: 2019
  ident: pone.0287980.ref050
  article-title: Joint chance constraints in AC optimal power flow: improving bounds through learning
  publication-title: IEEE Transactions on Smart Grid
  doi: 10.1109/TSG.2019.2903767
– volume: 148
  start-page: 257
  year: 2011
  ident: pone.0287980.ref046
  article-title: A sampling and discarding approach to chance-constrained optimization: Feasibility and optimality
  publication-title: Journal of Optimization Theory and Application
  doi: 10.1007/s10957-010-9754-6
– volume: 8
  start-page: 79397
  year: 2020
  ident: pone.0287980.ref047
  article-title: Pool & Discard algorithm for chance constrained optimization problems.
  publication-title: IEEE access.
  doi: 10.1109/ACCESS.2020.2990726
– volume: 4
  start-page: 135
  year: 2015
  ident: pone.0287980.ref022
  article-title: Patient flow scheduling and capacity planning in a smart hospital environment.
  publication-title: IEEE Access.
  doi: 10.1109/ACCESS.2015.2509013
– volume: 260
  start-page: 21
  issue: 1
  year: 2017
  ident: pone.0287980.ref037
  article-title: Two-stage robust optimization approach to elective surgery and downstream capacity planning
  publication-title: European Journal of Operational Research
  doi: 10.1016/j.ejor.2016.11.043
– volume: 263
  start-page: 864
  issue: 3
  year: 2017
  ident: pone.0287980.ref015
  article-title: A capacity assessment and querying approach for multi-modal transportation systems
  publication-title: European Journal of Operational Research
  doi: 10.1016/j.ejor.2017.05.007
– volume: 206
  start-page: 642
  issue: 3
  year: 2010
  ident: pone.0287980.ref036
  article-title: Scheduling elective surgery under uncertainty and downstream capacity constraints
  publication-title: European Journal of Operational Research
  doi: 10.1016/j.ejor.2010.03.014
– volume: 229
  start-page: 107764
  year: 2020
  ident: pone.0287980.ref042
  article-title: Column-generation-based heuristic approaches to stochastic surgery scheduling with downstream capacity constraints
  publication-title: International Journal of Production Economics
  doi: 10.1016/j.ijpe.2020.107764
– volume: 260
  start-page: 1152
  issue: 3
  year: 2017
  ident: pone.0287980.ref029
  article-title: Optimization of hospital ward resources with patient relocation using Markov chain modelling
  publication-title: European Journal of Operational Research
  doi: 10.1016/j.ejor.2017.01.026
– volume: 245
  start-page: 489
  issue: 2
  year: 2015
  ident: pone.0287980.ref019
  article-title: Multi-objective models and techniques for analysing the absolute capacity of railway networks
  publication-title: European Journal of Operational Research
  doi: 10.1016/j.ejor.2015.03.020
– volume: 77
  start-page: 170
  year: 2017
  ident: pone.0287980.ref003
  article-title: A mixed integer linear programming approach to perform hospital capacity assessments.
  publication-title: Expert Systems and Applications
  doi: 10.1016/j.eswa.2017.01.050
– volume: 264
  start-page: 756
  issue: 2
  year: 2018
  ident: pone.0287980.ref058
  article-title: An integrated approach for scheduling health care activities in a hospital
  publication-title: European Journal of Operational Research
  doi: 10.1016/j.ejor.2017.06.051
– volume: 23
  start-page: 80
  year: 2018
  ident: pone.0287980.ref023
  article-title: Analyzing economies of scale and scope in hospitals by use of case mix planning
  publication-title: Health Care Management Science
  doi: 10.1007/s10729-019-09476-2
– ident: pone.0287980.ref052
  article-title: Affinely adjustable robust optimization for a multi-period inventory problem with capital constraints and demand uncertainties.
  publication-title: International Transactions in Operational Research.
– volume: 171
  start-page: 115
  year: 2018
  ident: pone.0287980.ref053
  article-title: Data-driven distributionally robust optimization using the Wasserstein metric: performance guarantee and tractable reformulations.
  publication-title: Mathematical Programming
  doi: 10.1007/s10107-017-1172-1
– volume: 127
  start-page: 49
  year: 2019
  ident: pone.0287980.ref010
  article-title: An integrated location and routing approach for transporting hazardous materials in a bi-modal transportation network.
  publication-title: Transportation Research Part E
  doi: 10.1016/j.tre.2019.04.011
– volume: 130
  start-page: 105224
  year: 2021
  ident: pone.0287980.ref060
  article-title: Stockpile scheduling with geometry constraints in dry bulk terminals
  publication-title: Computers and Operations Research
  doi: 10.1016/j.cor.2021.105224
– year: 2019
  ident: pone.0287980.ref005
  publication-title: Australian Institute of Health and WelfareHospital resources 2017–2018: Australian hospital statistics.
– volume: 19
  start-page: 382
  year: 2016
  ident: pone.0287980.ref027
  article-title: The operating room case-mix problem under uncertainty and nurses capacity constraints
  publication-title: Health Care Management Science
  doi: 10.1007/s10729-015-9337-z
– volume: 32
  start-page: 693
  issue: 3
  year: 2020
  ident: pone.0287980.ref017
  article-title: A multi commodity flow model incorporating flow reduction functions for multi modal transportation systems.
  publication-title: Flexible Services and Manufacturing
  doi: 10.1007/s10696-019-09349-4
– volume: 283
  start-page: 549
  issue: 2
  year: 2020
  ident: pone.0287980.ref039
  article-title: A distributionally robust optimization approach for outpatient colonoscopy scheduling
  publication-title: European Journal of Operational Research
  doi: 10.1016/j.ejor.2019.11.039
– volume: 276
  start-page: 602
  issue: 2
  year: 2019
  ident: pone.0287980.ref041
  article-title: A two-level optimization model for elective surgery scheduling with downstream capacity constraints
  publication-title: European Journal of Operational Research
  doi: 10.1016/j.ejor.2019.01.036
– volume: 27
  start-page: 2270
  issue: 12
  year: 2018
  ident: pone.0287980.ref007
  article-title: A capacity allocation planning model for integrated care and access management
  publication-title: Production and Operations Management
  doi: 10.1111/poms.12941
– start-page: 7
  year: 2020
  ident: pone.0287980.ref016
  article-title: (2020). A multi-criteria multi-commodity flow model for analysing transportation networks.
  publication-title: OR Perspectives.
– volume: 207
  start-page: 1628
  year: 2010
  ident: pone.0287980.ref021
  article-title: Hospital capacity, capability, and emergency preparedness
  publication-title: European Journal of Operational Research
  doi: 10.1016/j.ejor.2010.06.018
– volume: 22
  start-page: 543
  issue: 4
  year: 2010
  ident: pone.0287980.ref043
  article-title: Scalable heuristics for a class of chance-constrained stochastic programs
  publication-title: INFORMS J. Comput
  doi: 10.1287/ijoc.1090.0372
– volume: 382
  start-page: 2049
  issue: 21
  year: 2020
  ident: pone.0287980.ref002
  article-title: Fair allocation of scarce medical resources in the time of Covid-19
  publication-title: New England Journal of Medicine
  doi: 10.1056/NEJMsb2005114
– volume: 3
  start-page: 473
  issue: 5
  year: 2008
  ident: pone.0287980.ref024
  article-title: Models for hospital location and capacity allocation for an area prone to natural disasters
  publication-title: International Journal of Operational Research
  doi: 10.1504/IJOR.2008.019170
– volume: 12
  start-page: 479
  issue: 2
  year: 2002
  ident: pone.0287980.ref011
  article-title: The sample average approximation method for stochastic discrete optimization
  publication-title: SIAM Journal on Optimization
  doi: 10.1137/S1052623499363220
– volume: 4
  start-page: 73
  issue: 4
  year: 2015
  ident: pone.0287980.ref018
  article-title: Incorporating complex train paths in an analysis of absolute capacity
  publication-title: International Journal of Railway Technology
  doi: 10.4203/ijrt.4.4.4
– volume: 81
  start-page: 220
  year: 2017
  ident: pone.0287980.ref028
  article-title: Multi-objective capacity allocation of hospital wards combining revenue and equity.
  publication-title: Omega
  doi: 10.1016/j.omega.2017.11.005
– volume: 32
  start-page: 163
  issue: 1
  year: 2010
  ident: pone.0287980.ref056
  article-title: A sequencing approach for train timetabling.
  publication-title: OR Spectrum
  doi: 10.1007/s00291-008-0143-6
– volume: 292
  start-page: 683
  year: 2020
  ident: pone.0287980.ref008
  article-title: Solving joint chance constrained problems using regularization and Benders’ decomposition
  publication-title: Annals of Operations Research
  doi: 10.1007/s10479-018-3091-9
– year: 2020
  ident: pone.0287980.ref014
  article-title: An analytical optimisation framework for airport terminal capacity planning.
  publication-title: Complexity
– volume: 2
  start-page: 1
  year: 2021
  ident: pone.0287980.ref013
  article-title: A review of stochastic programming methods for optimization of process systems under uncertainty
  publication-title: Frontiers in Chemical Engineering
– volume: 56
  start-page: 3309
  year: 2017
  ident: pone.0287980.ref048
  article-title: Optimization of chemical process with joint chance constraints
  publication-title: Ind. Eng. Chem. Res
  doi: 10.1021/acs.iecr.6b02683
– volume: 264
  start-page: 123
  issue: 1–2
  year: 2018
  ident: pone.0287980.ref049
  article-title: A joint chance-constrained programming approach for the single-item capacitated lot-sizing problem with stochastic demand
  publication-title: Annals of Operations Research
  doi: 10.1007/s10479-017-2662-5
– volume: 251
  start-page: 783
  issue: 3
  year: 2016
  ident: pone.0287980.ref020
  article-title: Optimization models for expanding a railways theoretical capacity
  publication-title: European Journal of Operational Research
  doi: 10.1016/j.ejor.2015.12.033
– ident: pone.0287980.ref054
  article-title: A robust possibilistic programming framework for designing an organ transplant supply chain under uncertainty
  publication-title: Annals of Operations Research
– volume: 114
  start-page: 329
  year: 2017
  ident: pone.0287980.ref033
  article-title: A chance-constrained operating room planning with elective and emergency cases under downstream capacity constraints.
  publication-title: Computers & Industrial Engineering.
  doi: 10.1016/j.cie.2017.07.015
– volume: 255
  start-page: 505
  issue: 2
  year: 2016
  ident: pone.0287980.ref001
  article-title: A multi-criteria approach for hospital capacity analysis
  publication-title: European Journal of Operational Research
  doi: 10.1016/j.ejor.2016.05.041
– volume: 170
  start-page: 43
  issue: 1
  year: 2018
  ident: pone.0287980.ref044
  article-title: Relaxations and approximations of chance constraints under finite distributions.
  publication-title: Mathematical Programming. 2018
  doi: 10.1007/s10107-018-1295-z
– volume: 29
  start-page: 781
  year: 2002
  ident: pone.0287980.ref055
  article-title: A scenario-based stochastic programming approach for technology and capacity planning
  publication-title: Computers and Operations Research
  doi: 10.1016/S0305-0548(00)00076-9
– volume: 28
  start-page: 1735
  issue: 7
  year: 2019
  ident: pone.0287980.ref034
  article-title: Integrated scheduling and capacity planning with considerations for patients length-of-stays
  publication-title: Production and Operations Management
  doi: 10.1111/poms.13012
– volume: 238
  start-page: 741
  issue: 1
  year: 2014
  ident: pone.0287980.ref057
  article-title: An integrated approach for earthwork allocation, sequencing and routing
  publication-title: European Journal of Operational Research
  doi: 10.1016/j.ejor.2014.04.036
– volume: 141
  start-page: 104174
  year: 2020
  ident: pone.0287980.ref025
  article-title: A simulation-optimisation approach for hospital beds allocation
  publication-title: International Journal of Medical Informatics
  doi: 10.1016/j.ijmedinf.2020.104174
– volume: 279
  start-page: 721
  issue: 3
  year: 2019
  ident: pone.0287980.ref038
  article-title: Analysis of models for the stochastic outpatient procedure scheduling problem
  publication-title: European Journal of Operational Research
  doi: 10.1016/j.ejor.2019.06.023
– volume: 20
  start-page: 207
  year: 2017
  ident: pone.0287980.ref004
  article-title: Case mix planning in hospitals: a review and future agenda
  publication-title: Health Care Management Science
  doi: 10.1007/s10729-015-9342-2
– volume: 53
  start-page: 7252
  issue: 24
  year: 2015
  ident: pone.0287980.ref032
  article-title: A stochastic model for operating room planning under capacity constraints
  publication-title: International Journal of Production Research
  doi: 10.1080/00207543.2015.1033500
– volume: 97
  start-page: 102086
  year: 2020
  ident: pone.0287980.ref031
  article-title: Assessing the impact of uncertainty and the level of aggregation in case mix planning.
  publication-title: Omega
  doi: 10.1016/j.omega.2019.07.002
– start-page: 207
  volume-title: Handbook of simulation optimization
  year: 2015
  ident: pone.0287980.ref012
  doi: 10.1007/978-1-4939-1384-8_8
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Snippet This article introduces a bespoke risk averse stochastic programming approach for performing a strategic level assessment of hospital capacity (QAHC). We...
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StartPage e0287980
SubjectTerms Algorithms
Analysis
Decision making
Health care policy
Heuristic methods
Hospital size
Hospitals
Integer programming
Investment analysis
Length of stay
Medicine and Health Sciences
Methods
Mixed integer
Pandemics
Patients
Physical Sciences
Random variables
Research and Analysis Methods
Stochastic programming
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Title A stochastic programming approach to perform hospital capacity assessments
URI https://www.proquest.com/docview/3069280432
https://www.proquest.com/docview/2889238230
https://pubmed.ncbi.nlm.nih.gov/PMC10635551
https://doaj.org/article/60708737560c48a08bf9cb5c3686128c
http://dx.doi.org/10.1371/journal.pone.0287980
Volume 18
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