A Model of Supply-Chain Decisions for Resource Sharing with an Application to Ventilator Allocation to Combat COVID-19
Sanjay Mehrotra (mehrotranorthwestern.edu)
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 is 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 US 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 03/23/2020) 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 (till 05/31/20) is about 232,000 ventilator days, with a peak shortfall of 17,200 ventilators on 04/19/2020. Results are also reported for a worst-case where the demand is at the upper limit of the 95% confidence interval.
Keywords: Resource sharing, Ventilator allocation, COVID-19, Emergency management, Stochastic programming, Mixed-integer programming
Category 1: Applications -- OR and Management Sciences
Category 2: Stochastic Programming
Category 3: Integer Programming ((Mixed) Integer Linear Programming )
Citation: Mehrotra S., Rahimian H., Barah M., Luo F., and Schantz, K (2020). A Model of Supply-Chain Decisions for Resource Sharing with an Application to Ventilator Allocation to Combat COVID-19. Technical report, Northwestern University.
Entry Submitted: 04/02/2020
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