A novel capacity sharing mechanism to collaborative activities in the blood collection process during the COVID-19 outbreak

Because of government intervention, such as quarantine and cancellation of public events at the peak of the COVID-19 outbreak and donors' health scare of exposure to the virus in medical centers, the number of blood donors has considerably decreased. In some countries, the rate of blood donation has reached lower than 30%. Accordingly, in this study, to fill the lack of blood product during COVID-19, especially at the outbreak's peak, we propose a novel mechanism by providing a two-stage optimization tool for coordinating activities to mitigate the shortage in this urgent situation. In the first stage, a blood collection plan considering disruption risk in supply to minimize the unmet demand will be solved. Afterward, in the second stage, the collected units will be shared between regions by applying the capacity sharing concept to avoid the blood shortage in health centers. Moreover, to tackle the uncertainty and disruption risk, a novel stochastic model combining the mixed uncertainty approach is tailored. A rolling horizon planning method is implemented under an iterative procedure to provide and share the limited blood resources to solve the proposed model. A real-world case study of Iran is investigated to examine the applicability and performance of the proposed model; it should be noted that the designed mechanism is not confined just to this case. Obtained computational results indicate the applicability of the model, the superior performance of the capacity sharing concept, and the effectiveness of the designed mechanism for mitigating the shortage and wastage during the COVID-19 outbreak. (C) 2021 Elsevier B.V. All rights reserved.

Automated summary

This study proposes a novel mechanism to address blood product shortage during the COVID-19 outbreak, utilizing a two-stage optimization tool to coordinate activities. The solution includes blood collection considerations, capacity sharing, and a stochastic model to tackle uncertainty and disruption risks. The approach provides a real-world case study in Iran to demonstrate applicability and effectiveness.