Design of a scenario-based multi-level and multi-objective mathematical model with the aim of reducing the risk of the blood supply chain in the conditions of the COVID-19 pandemic

Supply chain risk management is a preventive approach to risk management in the supply chain to avoid possible unexpected consequences and to manage the blood supply chain (bsc) and achieve the maximum effectiveness and efficiency of this chain, risk management of the bsc is inevitable. this research aims to propose a mathematical model to reduce the risk of the bsc in the conditions of the covid-19 pandemic. one of the most important contributions of this research is to consider the uncertainty in the demand parameter in the conditions of the covid-19 pandemic and to provide a robust planning model to overcome it in order to properly manage and control its risks. for this purpose, in this research a scenario-based multi-objective model is proposed with the aim of reducing the risk of the bsc in the conditions of the covid-19 pandemic. in order to test the model, the problem is investigated in different sizes and using actual data and the results are presented, and sensitivity analysis is carried out on the changes in the parameters. baron solver in gams 24.9 software is used to solve the proposed mathematical model. the proposed model determines the product sent from the blood center to the hospital, the amount of product produced in the blood center, the amount of blood collected from donors, the number of collection centers, the amount of blood stock in the blood center and hospital with the aim of reducing cost and risk and increasing reliability. in this research, a scenario-based non-linear integer multi-objective model is proposed considering the level of supply and with the aim of reducing the risk of the bsc by reducing the cost and increasing the reliability of the bsc in the conditions of the covid-19 pandemic, which can be used for risk management of the bsc in critical conditions of blood supply, such as the covid-19 pandemic. finally, to measure the sensitivity of the presented model performance to the change in the parameters, the sensitivity analysis on the behavior of the model in terms of the change in the shortage cost, the number of blood collection facilities and the objective functions is presented. the sensitivity analysis on the shortage cost parameter showed that with the increase in the shortage cost, the shortage rate decreased and this leads to an increase in the total cost.