Presenting a Mathematical Model for a Sustainable Blood Supply Chain Considering Demand Uncertainty in Disasters

Abstract

A blood supply chain is one of the most important parts of a healthcare system. Any improvement in the performance of the blood supply chain can significantly increase its efficiency. In the present study, a multi-objective optimization model was used to design a blood supply chain network that minimizes the blood delivery time and the total supply chain cost. This model considered the penalty for CO₂ emission considering the environmental aspect of the blood supply chain. Due to uncertainty in supply and demand, the uncertainty model was changed into a deterministic model using a robust possibilistic programming method. Then, the multi-objective model was converted into a single-objective model using the ε-constraint method and was solved in GAMS software. The SA metaheuristic algorithm was used to validate the model. Comparing the results on a small scale showed that the SA algorithm performed better in the first objective function and the product delivery time was less. However, in the second objective function, the performance of the epsilon constraint method was better.