Blood supply chain design in disaster management: a comparison of risk-averse measures in robust optimization approaches
Résumé
Disasters, both natural and man-made, pose significant challenges to healthcare systems, particularly in managing blood supply chains effectively. Ensuring an adequate and timely supply of blood products becomes crucial during these crises to save lives and mitigate the impact of catastrophic events. This paper develops two-stage and multi-period risk aversion models for the blood supply chain in disaster management, focusing on mean-CVaR and worst-case criterion measures. We leverage advanced algorithms based on Benders decomposition and column-and-row generation techniques to produce practical solution methods which enable solving large-scale problem instances effectively. Numerical results demonstrate that these methods not only reduce the computational burden but also significantly enhance the solver’s ability to explore feasible solutions efficiently. Our computational experiments show that a coarse approximation of uncertainty is more effective than a fine approximation and that good preparation enables a more effective response to an emergency. Our findings provide insights for policymakers, healthcare practitioners, and logistics professionals to improve blood supply chain management strategies in disaster response and preparedness efforts.
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