A model of supply‐chain decisions for resource sharing with an application to ventilator allocation to combat COVID‐19
We present a stochastic optimization model for allocating and sharing a critical resource in the case of a pandemic. The demand for different entities peaks at different times, and an initial inventory for a central agency are to be allocated. The entities (states) may share the critical resource wi...
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| Veröffentlicht in: | Naval research logistics Jg. 67; H. 5; S. 303 - 320 |
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Hoboken, USA
John Wiley & Sons, Inc
01.08.2020
Wiley Subscription Services, Inc |
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| ISSN: | 0894-069X, 1520-6750, 1520-6750 |
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| Abstract | We present a stochastic optimization model for allocating and sharing a critical resource in the case of a pandemic. The demand for different entities peaks at different times, and an initial inventory for a central agency are to be allocated. The entities (states) may share the critical resource with a different state under a risk‐averse condition. The model is applied to study the allocation of ventilator inventory in the COVID‐19 pandemic by FEMA to different U.S. states. Findings suggest that if less than 60% of the ventilator inventory is available for non‐COVID‐19 patients, FEMA's stockpile of 20 000 ventilators (as of March 23, 2020) would be nearly adequate to meet the projected needs in slightly above average demand scenarios. However, when more than 75% of the available ventilator inventory must be reserved for non‐COVID‐19 patients, various degrees of shortfall are expected. In a severe case, where the demand is concentrated in the top‐most quartile of the forecast confidence interval and states are not willing to share their stockpile of ventilators, the total shortfall over the planning horizon (until May 31, 2020) is about 232 000 ventilator days, with a peak shortfall of 17 200 ventilators on April 19, 2020. Results are also reported for a worst‐case where the demand is at the upper limit of the 95% confidence interval. An important finding of this study is that a central agency (FEMA) can act as a coordinator for sharing critical resources that are in short supply over time to add efficiency in the system. Moreover, through properly managing risk‐aversion of different entities (states) additional efficiency can be gained. An additional implication is that ramping up production early in the planning cycle allows to reduce shortfall significantly. An optimal timing of this production ramp‐up consideration can be based on a cost‐benefit analysis. |
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| AbstractList | We present a stochastic optimization model for allocating and sharing a critical resource in the case of a pandemic. The demand for different entities peaks at different times, and an initial inventory for a central agency are to be allocated. The entities (states) may share the critical resource with a different state under a risk‐averse condition. The model is applied to study the allocation of ventilator inventory in the COVID‐19 pandemic by FEMA to different U.S. states. Findings suggest that if less than 60% of the ventilator inventory is available for non‐COVID‐19 patients, FEMA's stockpile of 20 000 ventilators (as of March 23, 2020) would be nearly adequate to meet the projected needs in slightly above average demand scenarios. However, when more than 75% of the available ventilator inventory must be reserved for non‐COVID‐19 patients, various degrees of shortfall are expected. In a severe case, where the demand is concentrated in the top‐most quartile of the forecast confidence interval and states are not willing to share their stockpile of ventilators, the total shortfall over the planning horizon (until May 31, 2020) is about 232 000 ventilator days, with a peak shortfall of 17 200 ventilators on April 19, 2020. Results are also reported for a worst‐case where the demand is at the upper limit of the 95% confidence interval. An important finding of this study is that a central agency (FEMA) can act as a coordinator for sharing critical resources that are in short supply over time to add efficiency in the system. Moreover, through properly managing risk‐aversion of different entities (states) additional efficiency can be gained. An additional implication is that ramping up production early in the planning cycle allows to reduce shortfall significantly. An optimal timing of this production ramp‐up consideration can be based on a cost‐benefit analysis. We present a stochastic optimization model for allocating and sharing a critical resource in the case of a pandemic. The demand for different entities peaks at different times, and an initial inventory for a central agency are to be allocated. The entities (states) may share the critical resource with a different state under a risk-averse condition. The model is applied to study the allocation of ventilator inventory in the COVID-19 pandemic by FEMA to different U.S. states. Findings suggest that if less than 60% of the ventilator inventory is available for non-COVID-19 patients, FEMA's stockpile of 20 000 ventilators (as of March 23, 2020) would be nearly adequate to meet the projected needs in slightly above average demand scenarios. However, when more than 75% of the available ventilator inventory must be reserved for non-COVID-19 patients, various degrees of shortfall are expected. In a severe case, where the demand is concentrated in the top-most quartile of the forecast confidence interval and states are not willing to share their stockpile of ventilators, the total shortfall over the planning horizon (until May 31, 2020) is about 232 000 ventilator days, with a peak shortfall of 17 200 ventilators on April 19, 2020. Results are also reported for a worst-case where the demand is at the upper limit of the 95% confidence interval. An important finding of this study is that a central agency (FEMA) can act as a coordinator for sharing critical resources that are in short supply over time to add efficiency in the system. Moreover, through properly managing risk-aversion of different entities (states) additional efficiency can be gained. An additional implication is that ramping up production early in the planning cycle allows to reduce shortfall significantly. An optimal timing of this production ramp-up consideration can be based on a cost-benefit analysis.We present a stochastic optimization model for allocating and sharing a critical resource in the case of a pandemic. The demand for different entities peaks at different times, and an initial inventory for a central agency are to be allocated. The entities (states) may share the critical resource with a different state under a risk-averse condition. The model is applied to study the allocation of ventilator inventory in the COVID-19 pandemic by FEMA to different U.S. states. Findings suggest that if less than 60% of the ventilator inventory is available for non-COVID-19 patients, FEMA's stockpile of 20 000 ventilators (as of March 23, 2020) would be nearly adequate to meet the projected needs in slightly above average demand scenarios. However, when more than 75% of the available ventilator inventory must be reserved for non-COVID-19 patients, various degrees of shortfall are expected. In a severe case, where the demand is concentrated in the top-most quartile of the forecast confidence interval and states are not willing to share their stockpile of ventilators, the total shortfall over the planning horizon (until May 31, 2020) is about 232 000 ventilator days, with a peak shortfall of 17 200 ventilators on April 19, 2020. Results are also reported for a worst-case where the demand is at the upper limit of the 95% confidence interval. An important finding of this study is that a central agency (FEMA) can act as a coordinator for sharing critical resources that are in short supply over time to add efficiency in the system. Moreover, through properly managing risk-aversion of different entities (states) additional efficiency can be gained. An additional implication is that ramping up production early in the planning cycle allows to reduce shortfall significantly. An optimal timing of this production ramp-up consideration can be based on a cost-benefit analysis. |
| Author | Barah, Masoud Schantz, Karolina Mehrotra, Sanjay Luo, Fengqiao Rahimian, Hamed |
| AuthorAffiliation | 1 Department of Industrial Engineering and Management Sciences Northwestern University Evanston Illinois USA |
| AuthorAffiliation_xml | – name: 1 Department of Industrial Engineering and Management Sciences Northwestern University Evanston Illinois USA |
| Author_xml | – sequence: 1 givenname: Sanjay orcidid: 0000-0003-1106-1901 surname: Mehrotra fullname: Mehrotra, Sanjay email: mehrotra@northwestern.edu organization: Northwestern University – sequence: 2 givenname: Hamed surname: Rahimian fullname: Rahimian, Hamed organization: Northwestern University – sequence: 3 givenname: Masoud surname: Barah fullname: Barah, Masoud organization: Northwestern University – sequence: 4 givenname: Fengqiao surname: Luo fullname: Luo, Fengqiao organization: Northwestern University – sequence: 5 givenname: Karolina surname: Schantz fullname: Schantz, Karolina organization: Northwestern University |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/38607793$$D View this record in MEDLINE/PubMed |
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| Keywords | mixed‐integer programming COVID‐19 ventilator allocation resource sharing stochastic programming emergency management |
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| Snippet | We present a stochastic optimization model for allocating and sharing a critical resource in the case of a pandemic. The demand for different entities peaks at... |
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| SubjectTerms | Confidence intervals Cost benefit analysis COVID-19 Demand Economic forecasting emergency management mixed‐integer programming Optimization Pandemics resource sharing Risk management stochastic programming Stockpiling ventilator allocation Ventilators |
| Title | A model of supply‐chain decisions for resource sharing with an application to ventilator allocation to combat COVID‐19 |
| URI | https://onlinelibrary.wiley.com/doi/abs/10.1002%2Fnav.21905 https://www.ncbi.nlm.nih.gov/pubmed/38607793 https://www.proquest.com/docview/2419325059 https://www.proquest.com/docview/3038438262 https://pubmed.ncbi.nlm.nih.gov/PMC7267382 |
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