PPE Supply Optimization Under Risks of Disruption from the COVID-19 Pandemic

Cecil Ash (), Uday Venkatadri (), Claver Diallo (), Peter Vanberkel () and Ahmed Saif ()
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Cecil Ash: Dalhousie University
Uday Venkatadri: Dalhousie University
Claver Diallo: Dalhousie University
Peter Vanberkel: Dalhousie University
Ahmed Saif: Dalhousie University

SN Operations Research Forum, 2023, vol. 4, issue 2, 1-29

Abstract: Abstract The COVID-19 pandemic has struck health service providers around the world with dire shortages, inflated prices, and volatile demand of personal protective equipment (PPE). This paper discusses supply chain resilience in the context of a Canadian provincial healthcare provider during the COVID-19 pandemic. A multi-period multi-objective mixed-integer programming model is presented for PPE supply planning under disruption risk. The deterministic formulation is extended to consider both two-stage and multi-stage uncertainty in the supply, price, and demand of PPE using stochastic programming (SP) and chance-constrained programming (CCP). The first objective is to minimize a risk measure of the stochastic total cost, either its Expected Value (EV) or its Value-at-Risk (VaR), and the second objective is to minimize the maximum shortage of any product in any time period. The $$\epsilon$$ ϵ -constraint method is used to generate sets of Pareto-optimal solutions and analyze the trade-off between these two competing objectives. Numerical experiments are conducted to analyze the efficacy of emergency inventory and increased inventory levels as risk mitigation strategies. We consider uncertainty scenarios based on plausible and actual pandemic trajectories seen around the world during the COVID-19 pandemic including single-wave, two-wave, and exponential growth.

Keywords: Supply chain resilience; Stochastic programming; Chance-constrained optimization; COVID-19 PPE; Canadian healthcare (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s43069-023-00209-4

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