A Stochastic modeling framework for ICU resource allocation during health crises
##plugins.themes.bootstrap3.article.main##
Abstract
The unprecedented surge in demand for intensive care services during health crises such as the COVID-19 pandemic has revealed critical limitations in existing ICU resource allocation models, which often fail to adapt to uncertain and dynamic conditions. This study aims to develop and evaluate a stochastic modeling framework to optimize ICU resource allocation under crisis scenarios, accounting for probabilistic patient arrivals, fluctuating treatment durations, and constrained multi-resource environments. The framework integrates discrete-event simulation (DES), queueing theory (specifically M/M/c/K models), and stochastic optimization to simulate real-time ICU operations and support decision-making. A Monte Carlo simulation was conducted over a 24-hour period involving 100 replications, where key parameters included a patient arrival rate of 4 patients/hour, 5 ICU beds, and a service time distribution with an average of 6 hours. The results indicate a high blocking probability of 84.3%, ICU bed utilization of 94%, ventilator utilization of 90%, an average patient waiting time of 2.4 hours, and a delay-sensitive mortality rate of 8%. The expected system cost, incorporating waiting time, mortality, and resource inefficiency penalties, totaled 190 units. These findings demonstrate the model’s capability to reveal critical system bottlenecks and support adaptive, ethically grounded allocation policies. The proposed framework provides practical implications for hospital administrators and policymakers by offering a dynamic, evidence-based decision-support tool to improve ICU efficiency and patient outcomes during emergencies.
##plugins.themes.bootstrap3.article.details##
How to Cite
Rangkuti, S., Simatupang, C. D., Stephane, L. S., & Eloise, D. (2024). A Stochastic modeling framework for ICU resource allocation during health crises. International Journal of Basic and Applied Science, 13(3), 157–170. https://doi.org/10.35335/ijobas.v13i3.644
References
S. Medina-González, A. Shokry, J. Silvente, G. Lupera, and A. Espuña, “Optimal management of bio-based energy supply chains under parametric uncertainty through a data-driven decision-support framework,” Comput. Ind. Eng., vol. 139, no. 1, p. 105561, 2020, doi: https://doi.org/10.1016/j.cie.2018.12.008.
O. H. Olayinka, “Big data integration and real-time analytics for enhancing operational efficiency and market responsiveness,” Int J Sci Res Arch, vol. 4, no. 1, pp. 280–296, 2021, doi: https://doi.org/10.30574/ijsra.2021.4.1.0179.
B. Avinash and G. Joseph, “Reimagining healthcare supply chains: a systematic review on digital transformation with specific focus on efficiency, transparency and responsiveness,” J. Health Organ. Manag., vol. 38, no. 8, pp. 1255–1279, 2024, doi: https://doi.org/10.1108/JHOM-03-2024-0076.
H. Kashani, S. Valaei Sharif, S. Hosseini, and M. A. Hekmatian, “Dynamical modeling of outbreak and control of pandemics: Assessing the resilience of healthcare infrastructure under mitigation policies,” in The Science behind the COVID Pandemic and Healthcare Technology Solutions, Springer, 2022, pp. 329–351. doi: https://doi.org/10.1007/978-3-031-10031-4_16.
M. Ortiz-Barrios, S. Arias-Fonseca, A. Ishizaka, M. Barbati, B. Avendaño-Collante, and E. Navarro-Jiménez, “Artificial intelligence and discrete-event simulation for capacity management of intensive care units during the Covid-19 pandemic: A case study,” J. Bus. Res., vol. 160, no. 5, p. 113806, 2023, doi: https://doi.org/10.1016/j.jbusres.2023.113806.
S. G. Emami, V. Lorenzoni, and G. Turchetti, “Towards resilient healthcare systems: a framework for crisis management,” Int. J. Environ. Res. Public Health, vol. 21, no. 3, p. 286, 2024, doi: https://doi.org/10.3390/ijerph21030286.
S. Farahi and K. Salimifard, “A simulation–optimization approach for measuring emergency department resilience in times of crisis,” Oper. Res. Heal. Care, vol. 31, no. 12, p. 100326, 2021, doi: https://doi.org/10.1016/j.orhc.2021.100326.
M. A. Alubaie et al., “The Efficiency and Accuracy Gains of Real-Time Health Data Integration in Healthcare Management: A Comprehensive Review of Current Practices and Future Directions,” Egypt. J. Chem., vol. 67, no. 13, pp. 1725–1729, 2024, [Online]. Available: https://journals.ekb.eg/article_405749.html
G. Gopal, C. Suter-Crazzolara, L. Toldo, and W. Eberhardt, “Digital transformation in healthcare–architectures of present and future information technologies,” Clin. Chem. Lab. Med., vol. 57, no. 3, pp. 328–335, 2019, doi: https://doi.org/10.1515/cclm-2018-0658.
V. Suganthi and K. Kalaiselvi, “Decision Support System in Healthcare Monitoring,” in Artificial Intelligence‐Based System Models in Healthcare, Wiley Online Library, 2024, pp. 55–78. doi: https://doi.org/10.1002/9781394242528.ch3.
K. Govindan, H. Mina, and B. Alavi, “A decision support system for demand management in healthcare supply chains considering the epidemic outbreaks: A case study of coronavirus disease 2019 (COVID-19),” Transp. Res. Part E Logist. Transp. Rev., vol. 138, no. 6, p. 101967, 2020, doi: https://doi.org/10.1016/j.tre.2020.101967.
A. M. Caunhye, X. Nie, and S. Pokharel, “Optimization models in emergency logistics: A literature review,” Socioecon. Plann. Sci., vol. 46, no. 1, pp. 4–13, 2012, doi: https://doi.org/10.1016/j.seps.2011.04.004.
F. Salamian, A. Paksaz, B. Khalil Loo, M. Mousapour Mamoudan, M. Aghsami, and A. Aghsami, “Supply Chains Problem During Crises: A Data-Driven Approach,” Modelling, vol. 5, no. 4, pp. 2001–2039, 2024, doi: https://doi.org/10.3390/modelling5040104.
F. Wan, “Multi-criteria optimization for the management of intensive care beds in an epidemic context,” INSA de Lyon, 2024. [Online]. Available: https://theses.hal.science/tel-05009627/
J. Bai, “Three essays on optimization of the intensive care unit (ICU) management decisions,” University of Mannheim, 2022. [Online]. Available: https://madoc.bib.uni-mannheim.de/61639/
K. A. Wager, F. W. Lee, and J. P. Glaser, Health care information systems: a practical approach for health care management, 1st ed. John Wiley & Sons, 2021.
W. Bender, C. A. Hiddleson, and T. G. Buchman, “Intensive care unit telemedicine: innovations and limitations,” Crit. Care Clin., vol. 35, no. 3, pp. 497–509, 2019, [Online]. Available: https://www.criticalcare.theclinics.com/article/S0749-0704(19)30024-7/abstract
M. Dar, L. Swamy, D. Gavin, and A. Theodore, “Mechanical-ventilation supply and options for the COVID-19 pandemic. Leveraging all available resources for a limited resource in a crisis,” Ann. Am. Thorac. Soc., vol. 18, no. 3, pp. 408–416, 2021, doi: https://doi.org/10.1513/AnnalsATS.202004-317CME.
I. E. Essoussi, M. Masmoudi, and M. Z. Babai, “Multi-criteria decision-making for collaborative COVID-19 surge management and inter-hospital patients’ transfer optimisation,” Int. J. Prod. Res., vol. 61, no. 23, pp. 7992–8021, 2023, doi: https://doi.org/10.1080/00207543.2022.2162619.
M. Bagheri, M. Bagheritabar, S. Alizadeh, M. (Sam) S. Parizi, P. Matoufinia, and Y. Luo, “Machine-Learning-Powered Information Systems: A Systematic Literature Review for Developing Multi-Objective Healthcare Management,” Appl. Sci., vol. 15, no. 1, p. 296, 2024, doi: https://doi.org/10.3390/app15010296.
D. Carraminana, A. M. Bernardos, J. A. Besada, and J. R. Casar, “Towards resilient cities: A hybrid simulation framework for risk mitigation through data-driven decision making,” Simul. Model. Pract. Theory, vol. 133, no. 5, p. 102924, 2024, doi: https://doi.org/10.1016/j.simpat.2024.102924.
L. Luo et al., “Medical Resource Management in Emergency Hierarchical Diagnosis and Treatment Systems: A Research Framework,” in Healthcare, MDPI, 2024, p. 1358. doi: https://doi.org/10.3390/healthcare12131358.
E. Nartey, F. K. Aboagye-Otchere, and S. N. Y. Simpson, “Management control and supply chain operational performance of public health emergency to pandemic control,” Manag. Res. Rev., vol. 45, no. 3, pp. 398–435, 2022, doi: https://doi.org/10.1108/MRR-09-2020-0600.
R. Maleki, M. Taghizadeh-Yazdi, R. Ghasemi, and S. Rivandi, “A Hybrid Mathematical-Simulation Approach to Hospital Beds Capacity Optimization for COVID-19 Pandemic Conditions,” in Operations Research Forum, Springer, 2024, p. 107. doi: https://doi.org/10.1007/s43069-024-00389-7.
S. Jahangiri, M. Abolghasemian, P. Ghasemi, and A. P. Chobar, “Simulation-based optimisation: analysis of the emergency department resources under COVID-19 conditions,” Int. J. Ind. Syst. Eng., vol. 43, no. 1, pp. 1–19, 2023, doi: https://doi.org/10.1504/IJISE.2023.128399.
J. Zhang, M. Dridi, and A. El Moudni, “A Markov decision model with dead ends for operating room planning considering dynamic patient priority,” RAIRO-Operations Res., vol. 53, no. 5, pp. 1819–1841, 2019, doi: https://doi.org/10.1051/ro/2018110.
Y. Ni, K. Wang, and L. Zhao, “A Markov decision process model of allocating emergency medical resource among multi-priority injuries,” Int. J. Math. Oper. Res., vol. 10, no. 1, pp. 1–17, 2017, doi: https://doi.org/10.1504/IJMOR.2017.080738.
M. Liu and D. Zhang, “A dynamic logistics model for medical resources allocation in an epidemic control with demand forecast updating,” J. Oper. Res. Soc., vol. 67, no. 6, pp. 841–852, 2016, doi: https://doi.org/10.1057/jors.2015.105.
H. O. Mete and Z. B. Zabinsky, “Stochastic optimization of medical supply location and distribution in disaster management,” Int. J. Prod. Econ., vol. 126, no. 1, pp. 76–84, 2010, doi: https://doi.org/10.1016/j.ijpe.2009.10.004.
L. G. Shattuck and N. L. Miller, “Extending naturalistic decision making to complex organizations: A dynamic model of situated cognition,” Organ. Stud., vol. 27, no. 7, pp. 989–1009, 2006, doi: https://doi.org/10.1177/0170840606065706.
A. M. Bernardelli, L. Bonasera, D. Duma, and E. Vercesi, “Multi-objective stochastic scheduling of inpatient and outpatient surgeries,” Flexible Services and Manufacturing Journal.
R. Aringhieri, P. Landa, P. Soriano, E. Tanfani, and A. Testi, “A two level metaheuristic for the operating room scheduling and assignment problem,” Comput. Oper. Res., vol. 54, no. 2, pp. 21–34, 2015, doi: https://doi.org/10.1016/j.cor.2014.08.014.
M. H. Lerchbaumer et al., “Point-of-care lung ultrasound in COVID-19 patients: Inter-and intra-observer agreement in a prospective observational study,” Sci. Rep., vol. 11, no. 1, p. 10678, 2021, doi: https://doi.org/10.1038/s41598-021-90153-2.
S. H. Jacobson, S. N. Hall, and J. R. Swisher, “Discrete-event simulation of health care systems,” in Patient flow: Reducing delay in healthcare delivery, Springer, 2013, pp. 273–309. doi: https://doi.org/10.1007/978-1-4614-9512-3_12.
S. Garg, “Hospitalization rates and characteristics of patients hospitalized with laboratory-confirmed coronavirus disease 2019—COVID-NET, 14 States, March 1–30, 2020,” MMWR. Morb. Mortal. Wkly. Rep., vol. 69, no. 15, pp. 458–464, 2020, doi: http://dx.doi.org/10.15585/mmwr.mm6915e3.
X. Yin, I. E. Buyuktahtakin, and B. P. Patel, “COVID-19: Optimal allocation of ventilator supply under uncertainty and risk,” arXiv preprint arXiv:2103.06813.
A. Shoukat, C. R. Wells, J. M. Langley, B. H. Singer, A. P. Galvani, and S. M. Moghadas, “Projecting demand for critical care beds during COVID-19 outbreaks in Canada,” Cmaj, vol. 192, no. 19, pp. E489–E496, 2020, doi: https://doi.org/10.1503/cmaj.200457.
J. I. Vázquez-Serrano, R. E. Peimbert-García, and L. E. Cárdenas-Barrón, “Discrete-event simulation modeling in healthcare: a comprehensive review,” Int. J. Environ. Res. Public Health, vol. 18, no. 22, p. 12262, 2021, doi: https://doi.org/10.3390/ijerph182212262.
X. Zhang, “Application of discrete event simulation in health care: a systematic review,” BMC Health Serv. Res., vol. 18, no. 687, pp. 1–11, 2018, doi: https://doi.org/10.1186/s12913-018-3456-4.
M. M. Günal and M. Pidd, “Discrete event simulation for performance modelling in health care: a review of the literature,” J. Simul., vol. 4, no. 1, pp. 42–51, 2010, doi: https://doi.org/10.1057/jos.2009.25.
D. Goldsman and P. Goldsman, “Discrete-event simulation,” in Modeling and Simulation in the Systems Engineering Life Cycle: Core Concepts and Accompanying Lectures, Springer, 2015, pp. 103–109. doi: https://doi.org/10.1007/978-1-4471-5634-5_10.
D. Fouskakis and D. Draper, “Stochastic optimization: a review,” Int. Stat. Rev., vol. 70, no. 3, pp. 315–349, 2002, doi: https://doi.org/10.1111/j.1751-5823.2002.tb00174.x.
K. Marti, Stochastic optimization methods, vol. 2. Springer, 2005. doi: https://doi.org/10.1007/978-3-031-40059-9.
G. C. Pflug, Optimization of stochastic models: the interface between simulation and optimization. Springer Science & Business Media, 2012.
I. Adan and J. Resing, Queueing theory. Eindhoven, 2002.
R. B. Cooper, “Queueing theory,” in Proceedings of the ACM’81 conference, 1981, pp. 119–122. doi: https://doi.org/10.1145/800175.809851.
J.-Y. Lefrant et al., “ICU bed capacity during COVID-19 pandemic in France: From ephemeral beds to continuous and permanent adaptation,” Anaesthesia, Crit. Care Pain Med., vol. 40, no. 3, p. 100873, 2021, doi: https://doi.org/10.1016/j.accpm.2021.100873.
T. Zhang, Y. Lu, Y. Guan, X. Zhong, and T. Hogan, “Data-driven modeling and analysis for COVID-19 pandemic hospital beds planning,” IEEE Trans. Autom. Sci. Eng., vol. 20, no. 3, pp. 1551–1564, 2022, doi: https://doi.org/10.1109/TASE.2022.3224171.
L. Green, Queueing analysis in healthcare. Springer, 2006. doi: https://doi.org/10.1007/978-0-387-33636-7_10.
E. Litvak and M. C. Long, “Cost and quality under managed care: Irreconcilable differences,” Am J Manag Care, vol. 6, no. 3, pp. 305–312, 2000.
S. Mirsadraee et al., “Prevalence of thrombotic complications in ICU-treated patients with coronavirus disease 2019 detected with systematic CT scanning,” Crit. Care Med., vol. 49, no. 5, pp. 804–815, 2021, doi: 10.1097/CCM.0000000000004890.
H. Chu et al., “SARS-CoV-2 induces a more robust innate immune response and replicates less efficiently than SARS-CoV in the human intestines: an ex vivo study with implications on pathogenesis of COVID-19,” Cell. Mol. Gastroenterol. Hepatol., vol. 11, no. 3, pp. 771–781, 2021, doi: https://doi.org/10.1016/j.jcmgh.2020.09.017.
D. Bertsimas et al., “COVID-19 mortality risk assessment: An international multi-center study,” PLoS One, vol. 15, no. 12, p. e0243262, 2020, doi: https://doi.org/10.1371/journal.pone.0243262.
S. A. Paul, M. C. Reddy, and C. J. DeFlitch, “A systematic review of simulation studies investigating emergency department overcrowding,” Simulation, vol. 86, no. 8–9, pp. 559–571, 2010, doi: https://doi.org/10.1177/0037549709360912.
S. Yoon and S. H. Kim, “Assessing the Effectiveness of Simulation-Based Education in Emerging Infectious Disease Management: A Systematic Review and Meta-analysis,” Simul. Healthc., vol. 20, no. 2, pp. 118–128, 2023, doi: 10.1097/SIH.0000000000000812.
O. H. Olayinka, “Big data integration and real-time analytics for enhancing operational efficiency and market responsiveness,” Int J Sci Res Arch, vol. 4, no. 1, pp. 280–296, 2021, doi: https://doi.org/10.30574/ijsra.2021.4.1.0179.
B. Avinash and G. Joseph, “Reimagining healthcare supply chains: a systematic review on digital transformation with specific focus on efficiency, transparency and responsiveness,” J. Health Organ. Manag., vol. 38, no. 8, pp. 1255–1279, 2024, doi: https://doi.org/10.1108/JHOM-03-2024-0076.
H. Kashani, S. Valaei Sharif, S. Hosseini, and M. A. Hekmatian, “Dynamical modeling of outbreak and control of pandemics: Assessing the resilience of healthcare infrastructure under mitigation policies,” in The Science behind the COVID Pandemic and Healthcare Technology Solutions, Springer, 2022, pp. 329–351. doi: https://doi.org/10.1007/978-3-031-10031-4_16.
M. Ortiz-Barrios, S. Arias-Fonseca, A. Ishizaka, M. Barbati, B. Avendaño-Collante, and E. Navarro-Jiménez, “Artificial intelligence and discrete-event simulation for capacity management of intensive care units during the Covid-19 pandemic: A case study,” J. Bus. Res., vol. 160, no. 5, p. 113806, 2023, doi: https://doi.org/10.1016/j.jbusres.2023.113806.
S. G. Emami, V. Lorenzoni, and G. Turchetti, “Towards resilient healthcare systems: a framework for crisis management,” Int. J. Environ. Res. Public Health, vol. 21, no. 3, p. 286, 2024, doi: https://doi.org/10.3390/ijerph21030286.
S. Farahi and K. Salimifard, “A simulation–optimization approach for measuring emergency department resilience in times of crisis,” Oper. Res. Heal. Care, vol. 31, no. 12, p. 100326, 2021, doi: https://doi.org/10.1016/j.orhc.2021.100326.
M. A. Alubaie et al., “The Efficiency and Accuracy Gains of Real-Time Health Data Integration in Healthcare Management: A Comprehensive Review of Current Practices and Future Directions,” Egypt. J. Chem., vol. 67, no. 13, pp. 1725–1729, 2024, [Online]. Available: https://journals.ekb.eg/article_405749.html
G. Gopal, C. Suter-Crazzolara, L. Toldo, and W. Eberhardt, “Digital transformation in healthcare–architectures of present and future information technologies,” Clin. Chem. Lab. Med., vol. 57, no. 3, pp. 328–335, 2019, doi: https://doi.org/10.1515/cclm-2018-0658.
V. Suganthi and K. Kalaiselvi, “Decision Support System in Healthcare Monitoring,” in Artificial Intelligence‐Based System Models in Healthcare, Wiley Online Library, 2024, pp. 55–78. doi: https://doi.org/10.1002/9781394242528.ch3.
K. Govindan, H. Mina, and B. Alavi, “A decision support system for demand management in healthcare supply chains considering the epidemic outbreaks: A case study of coronavirus disease 2019 (COVID-19),” Transp. Res. Part E Logist. Transp. Rev., vol. 138, no. 6, p. 101967, 2020, doi: https://doi.org/10.1016/j.tre.2020.101967.
A. M. Caunhye, X. Nie, and S. Pokharel, “Optimization models in emergency logistics: A literature review,” Socioecon. Plann. Sci., vol. 46, no. 1, pp. 4–13, 2012, doi: https://doi.org/10.1016/j.seps.2011.04.004.
F. Salamian, A. Paksaz, B. Khalil Loo, M. Mousapour Mamoudan, M. Aghsami, and A. Aghsami, “Supply Chains Problem During Crises: A Data-Driven Approach,” Modelling, vol. 5, no. 4, pp. 2001–2039, 2024, doi: https://doi.org/10.3390/modelling5040104.
F. Wan, “Multi-criteria optimization for the management of intensive care beds in an epidemic context,” INSA de Lyon, 2024. [Online]. Available: https://theses.hal.science/tel-05009627/
J. Bai, “Three essays on optimization of the intensive care unit (ICU) management decisions,” University of Mannheim, 2022. [Online]. Available: https://madoc.bib.uni-mannheim.de/61639/
K. A. Wager, F. W. Lee, and J. P. Glaser, Health care information systems: a practical approach for health care management, 1st ed. John Wiley & Sons, 2021.
W. Bender, C. A. Hiddleson, and T. G. Buchman, “Intensive care unit telemedicine: innovations and limitations,” Crit. Care Clin., vol. 35, no. 3, pp. 497–509, 2019, [Online]. Available: https://www.criticalcare.theclinics.com/article/S0749-0704(19)30024-7/abstract
M. Dar, L. Swamy, D. Gavin, and A. Theodore, “Mechanical-ventilation supply and options for the COVID-19 pandemic. Leveraging all available resources for a limited resource in a crisis,” Ann. Am. Thorac. Soc., vol. 18, no. 3, pp. 408–416, 2021, doi: https://doi.org/10.1513/AnnalsATS.202004-317CME.
I. E. Essoussi, M. Masmoudi, and M. Z. Babai, “Multi-criteria decision-making for collaborative COVID-19 surge management and inter-hospital patients’ transfer optimisation,” Int. J. Prod. Res., vol. 61, no. 23, pp. 7992–8021, 2023, doi: https://doi.org/10.1080/00207543.2022.2162619.
M. Bagheri, M. Bagheritabar, S. Alizadeh, M. (Sam) S. Parizi, P. Matoufinia, and Y. Luo, “Machine-Learning-Powered Information Systems: A Systematic Literature Review for Developing Multi-Objective Healthcare Management,” Appl. Sci., vol. 15, no. 1, p. 296, 2024, doi: https://doi.org/10.3390/app15010296.
D. Carraminana, A. M. Bernardos, J. A. Besada, and J. R. Casar, “Towards resilient cities: A hybrid simulation framework for risk mitigation through data-driven decision making,” Simul. Model. Pract. Theory, vol. 133, no. 5, p. 102924, 2024, doi: https://doi.org/10.1016/j.simpat.2024.102924.
L. Luo et al., “Medical Resource Management in Emergency Hierarchical Diagnosis and Treatment Systems: A Research Framework,” in Healthcare, MDPI, 2024, p. 1358. doi: https://doi.org/10.3390/healthcare12131358.
E. Nartey, F. K. Aboagye-Otchere, and S. N. Y. Simpson, “Management control and supply chain operational performance of public health emergency to pandemic control,” Manag. Res. Rev., vol. 45, no. 3, pp. 398–435, 2022, doi: https://doi.org/10.1108/MRR-09-2020-0600.
R. Maleki, M. Taghizadeh-Yazdi, R. Ghasemi, and S. Rivandi, “A Hybrid Mathematical-Simulation Approach to Hospital Beds Capacity Optimization for COVID-19 Pandemic Conditions,” in Operations Research Forum, Springer, 2024, p. 107. doi: https://doi.org/10.1007/s43069-024-00389-7.
S. Jahangiri, M. Abolghasemian, P. Ghasemi, and A. P. Chobar, “Simulation-based optimisation: analysis of the emergency department resources under COVID-19 conditions,” Int. J. Ind. Syst. Eng., vol. 43, no. 1, pp. 1–19, 2023, doi: https://doi.org/10.1504/IJISE.2023.128399.
J. Zhang, M. Dridi, and A. El Moudni, “A Markov decision model with dead ends for operating room planning considering dynamic patient priority,” RAIRO-Operations Res., vol. 53, no. 5, pp. 1819–1841, 2019, doi: https://doi.org/10.1051/ro/2018110.
Y. Ni, K. Wang, and L. Zhao, “A Markov decision process model of allocating emergency medical resource among multi-priority injuries,” Int. J. Math. Oper. Res., vol. 10, no. 1, pp. 1–17, 2017, doi: https://doi.org/10.1504/IJMOR.2017.080738.
M. Liu and D. Zhang, “A dynamic logistics model for medical resources allocation in an epidemic control with demand forecast updating,” J. Oper. Res. Soc., vol. 67, no. 6, pp. 841–852, 2016, doi: https://doi.org/10.1057/jors.2015.105.
H. O. Mete and Z. B. Zabinsky, “Stochastic optimization of medical supply location and distribution in disaster management,” Int. J. Prod. Econ., vol. 126, no. 1, pp. 76–84, 2010, doi: https://doi.org/10.1016/j.ijpe.2009.10.004.
L. G. Shattuck and N. L. Miller, “Extending naturalistic decision making to complex organizations: A dynamic model of situated cognition,” Organ. Stud., vol. 27, no. 7, pp. 989–1009, 2006, doi: https://doi.org/10.1177/0170840606065706.
A. M. Bernardelli, L. Bonasera, D. Duma, and E. Vercesi, “Multi-objective stochastic scheduling of inpatient and outpatient surgeries,” Flexible Services and Manufacturing Journal.
R. Aringhieri, P. Landa, P. Soriano, E. Tanfani, and A. Testi, “A two level metaheuristic for the operating room scheduling and assignment problem,” Comput. Oper. Res., vol. 54, no. 2, pp. 21–34, 2015, doi: https://doi.org/10.1016/j.cor.2014.08.014.
M. H. Lerchbaumer et al., “Point-of-care lung ultrasound in COVID-19 patients: Inter-and intra-observer agreement in a prospective observational study,” Sci. Rep., vol. 11, no. 1, p. 10678, 2021, doi: https://doi.org/10.1038/s41598-021-90153-2.
S. H. Jacobson, S. N. Hall, and J. R. Swisher, “Discrete-event simulation of health care systems,” in Patient flow: Reducing delay in healthcare delivery, Springer, 2013, pp. 273–309. doi: https://doi.org/10.1007/978-1-4614-9512-3_12.
S. Garg, “Hospitalization rates and characteristics of patients hospitalized with laboratory-confirmed coronavirus disease 2019—COVID-NET, 14 States, March 1–30, 2020,” MMWR. Morb. Mortal. Wkly. Rep., vol. 69, no. 15, pp. 458–464, 2020, doi: http://dx.doi.org/10.15585/mmwr.mm6915e3.
X. Yin, I. E. Buyuktahtakin, and B. P. Patel, “COVID-19: Optimal allocation of ventilator supply under uncertainty and risk,” arXiv preprint arXiv:2103.06813.
A. Shoukat, C. R. Wells, J. M. Langley, B. H. Singer, A. P. Galvani, and S. M. Moghadas, “Projecting demand for critical care beds during COVID-19 outbreaks in Canada,” Cmaj, vol. 192, no. 19, pp. E489–E496, 2020, doi: https://doi.org/10.1503/cmaj.200457.
J. I. Vázquez-Serrano, R. E. Peimbert-García, and L. E. Cárdenas-Barrón, “Discrete-event simulation modeling in healthcare: a comprehensive review,” Int. J. Environ. Res. Public Health, vol. 18, no. 22, p. 12262, 2021, doi: https://doi.org/10.3390/ijerph182212262.
X. Zhang, “Application of discrete event simulation in health care: a systematic review,” BMC Health Serv. Res., vol. 18, no. 687, pp. 1–11, 2018, doi: https://doi.org/10.1186/s12913-018-3456-4.
M. M. Günal and M. Pidd, “Discrete event simulation for performance modelling in health care: a review of the literature,” J. Simul., vol. 4, no. 1, pp. 42–51, 2010, doi: https://doi.org/10.1057/jos.2009.25.
D. Goldsman and P. Goldsman, “Discrete-event simulation,” in Modeling and Simulation in the Systems Engineering Life Cycle: Core Concepts and Accompanying Lectures, Springer, 2015, pp. 103–109. doi: https://doi.org/10.1007/978-1-4471-5634-5_10.
D. Fouskakis and D. Draper, “Stochastic optimization: a review,” Int. Stat. Rev., vol. 70, no. 3, pp. 315–349, 2002, doi: https://doi.org/10.1111/j.1751-5823.2002.tb00174.x.
K. Marti, Stochastic optimization methods, vol. 2. Springer, 2005. doi: https://doi.org/10.1007/978-3-031-40059-9.
G. C. Pflug, Optimization of stochastic models: the interface between simulation and optimization. Springer Science & Business Media, 2012.
I. Adan and J. Resing, Queueing theory. Eindhoven, 2002.
R. B. Cooper, “Queueing theory,” in Proceedings of the ACM’81 conference, 1981, pp. 119–122. doi: https://doi.org/10.1145/800175.809851.
J.-Y. Lefrant et al., “ICU bed capacity during COVID-19 pandemic in France: From ephemeral beds to continuous and permanent adaptation,” Anaesthesia, Crit. Care Pain Med., vol. 40, no. 3, p. 100873, 2021, doi: https://doi.org/10.1016/j.accpm.2021.100873.
T. Zhang, Y. Lu, Y. Guan, X. Zhong, and T. Hogan, “Data-driven modeling and analysis for COVID-19 pandemic hospital beds planning,” IEEE Trans. Autom. Sci. Eng., vol. 20, no. 3, pp. 1551–1564, 2022, doi: https://doi.org/10.1109/TASE.2022.3224171.
L. Green, Queueing analysis in healthcare. Springer, 2006. doi: https://doi.org/10.1007/978-0-387-33636-7_10.
E. Litvak and M. C. Long, “Cost and quality under managed care: Irreconcilable differences,” Am J Manag Care, vol. 6, no. 3, pp. 305–312, 2000.
S. Mirsadraee et al., “Prevalence of thrombotic complications in ICU-treated patients with coronavirus disease 2019 detected with systematic CT scanning,” Crit. Care Med., vol. 49, no. 5, pp. 804–815, 2021, doi: 10.1097/CCM.0000000000004890.
H. Chu et al., “SARS-CoV-2 induces a more robust innate immune response and replicates less efficiently than SARS-CoV in the human intestines: an ex vivo study with implications on pathogenesis of COVID-19,” Cell. Mol. Gastroenterol. Hepatol., vol. 11, no. 3, pp. 771–781, 2021, doi: https://doi.org/10.1016/j.jcmgh.2020.09.017.
D. Bertsimas et al., “COVID-19 mortality risk assessment: An international multi-center study,” PLoS One, vol. 15, no. 12, p. e0243262, 2020, doi: https://doi.org/10.1371/journal.pone.0243262.
S. A. Paul, M. C. Reddy, and C. J. DeFlitch, “A systematic review of simulation studies investigating emergency department overcrowding,” Simulation, vol. 86, no. 8–9, pp. 559–571, 2010, doi: https://doi.org/10.1177/0037549709360912.
S. Yoon and S. H. Kim, “Assessing the Effectiveness of Simulation-Based Education in Emerging Infectious Disease Management: A Systematic Review and Meta-analysis,” Simul. Healthc., vol. 20, no. 2, pp. 118–128, 2023, doi: 10.1097/SIH.0000000000000812.
Copyright and Licensing
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.