Dynamic Hospital Resource Scheduling During Pandemics with Stochastic Optimization
The COVID-19 pandemic has highlighted the need to effectively manage hospital resources: ICU beds and ventilators. These resources are significant for sustaining life, especially in severe cases. Traditional deterministic models often fall short in addressing the uncertainties associated with patie...
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| Format: | Article |
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College of Engineering of Afe Babalola University, Ado-Ekiti (ABUAD), Ekiti State, Nigeria
2025-05-01
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| Series: | ABUAD Journal of Engineering Research and Development |
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| Online Access: | https://journals.abuad.edu.ng/index.php/ajerd/article/view/1058 |
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| author | Yewande Ojo John Ogbemhe Oluwabukunmi Victor Babatunde Subomi Okeowo Olubayo Babatunde John Adebisi |
| author_facet | Yewande Ojo John Ogbemhe Oluwabukunmi Victor Babatunde Subomi Okeowo Olubayo Babatunde John Adebisi |
| author_sort | Yewande Ojo |
| collection | DOAJ |
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The COVID-19 pandemic has highlighted the need to effectively manage hospital resources: ICU beds and ventilators. These resources are significant for sustaining life, especially in severe cases. Traditional deterministic models often fall short in addressing the uncertainties associated with patient inflows and resource availability. This paper develops a novel two-stage stochastic programming model which aims to dynamically allocate resources to deal with the variability of inpatient admissions. To this end, the scenarios are developed using Monte Carlo simulation based on the probabilities estimated from the historical data. The model is created in Python language and solved using the Gurobi optimizer in 0.05s, a large-scale scenario optimization analysis problem with 42 variables and 35 constraints. The KPIs show the highest utilization of ventilators at 66. 67% and the average reduction of 53.5 in the number of offers an ICU practical shortfall leading to better patient care and shorter wait times. This research presents a data-driven tool to enhance the decision-making process and the healthcare system's overall readiness to maintain its strategic reserves by implementing flexible staffing models to improve preparation for disasters such as the pandemic. Its stochastic optimization framework makes hospital resource allocation more efficient, offering a scalable, resilient solution for tackling future pandemic challenges.
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| format | Article |
| id | doaj-art-e9f4530299404d68ad0014e1d0330451 |
| institution | DOAJ |
| issn | 2756-6811 2645-2685 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | College of Engineering of Afe Babalola University, Ado-Ekiti (ABUAD), Ekiti State, Nigeria |
| record_format | Article |
| series | ABUAD Journal of Engineering Research and Development |
| spelling | doaj-art-e9f4530299404d68ad0014e1d03304512025-08-20T02:58:55ZengCollege of Engineering of Afe Babalola University, Ado-Ekiti (ABUAD), Ekiti State, NigeriaABUAD Journal of Engineering Research and Development2756-68112645-26852025-05-018210.53982/ajerd.2025.0802.04-jDynamic Hospital Resource Scheduling During Pandemics with Stochastic OptimizationYewande Ojo0John Ogbemhe1Oluwabukunmi Victor Babatunde2Subomi Okeowo3Olubayo Babatunde4John Adebisi5Department of Communications Management, University of DenverDepartment of Systems Engineering, University of Lagos, NigeriaDepartment of Industrial Design, Federal University of Technology, Akure, NigeriaCollege of Arts, Media and Design, Northeastern University, USADepartment of Electrical Engineering, University of Lagos, NigeriaDivision of Engineering & Technology, The University of West Alabama, Livingston, AL 35470, USA The COVID-19 pandemic has highlighted the need to effectively manage hospital resources: ICU beds and ventilators. These resources are significant for sustaining life, especially in severe cases. Traditional deterministic models often fall short in addressing the uncertainties associated with patient inflows and resource availability. This paper develops a novel two-stage stochastic programming model which aims to dynamically allocate resources to deal with the variability of inpatient admissions. To this end, the scenarios are developed using Monte Carlo simulation based on the probabilities estimated from the historical data. The model is created in Python language and solved using the Gurobi optimizer in 0.05s, a large-scale scenario optimization analysis problem with 42 variables and 35 constraints. The KPIs show the highest utilization of ventilators at 66. 67% and the average reduction of 53.5 in the number of offers an ICU practical shortfall leading to better patient care and shorter wait times. This research presents a data-driven tool to enhance the decision-making process and the healthcare system's overall readiness to maintain its strategic reserves by implementing flexible staffing models to improve preparation for disasters such as the pandemic. Its stochastic optimization framework makes hospital resource allocation more efficient, offering a scalable, resilient solution for tackling future pandemic challenges. https://journals.abuad.edu.ng/index.php/ajerd/article/view/1058Stochastic Programming, Hospital Resource AllocationPandemic PreparednessMonte Carlo SimulationGurobi OptimizerResource Utilization |
| spellingShingle | Yewande Ojo John Ogbemhe Oluwabukunmi Victor Babatunde Subomi Okeowo Olubayo Babatunde John Adebisi Dynamic Hospital Resource Scheduling During Pandemics with Stochastic Optimization ABUAD Journal of Engineering Research and Development Stochastic Programming , Hospital Resource Allocation Pandemic Preparedness Monte Carlo Simulation Gurobi Optimizer Resource Utilization |
| title | Dynamic Hospital Resource Scheduling During Pandemics with Stochastic Optimization |
| title_full | Dynamic Hospital Resource Scheduling During Pandemics with Stochastic Optimization |
| title_fullStr | Dynamic Hospital Resource Scheduling During Pandemics with Stochastic Optimization |
| title_full_unstemmed | Dynamic Hospital Resource Scheduling During Pandemics with Stochastic Optimization |
| title_short | Dynamic Hospital Resource Scheduling During Pandemics with Stochastic Optimization |
| title_sort | dynamic hospital resource scheduling during pandemics with stochastic optimization |
| topic | Stochastic Programming , Hospital Resource Allocation Pandemic Preparedness Monte Carlo Simulation Gurobi Optimizer Resource Utilization |
| url | https://journals.abuad.edu.ng/index.php/ajerd/article/view/1058 |
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