A Robust Optimization Approach for Sustainable humanitarian supply chain management of blood products

Blood as a vital element of the health system has an important role in this system, because any blood deficient results in death. Blood supply chain management aims to bridge the gap between blood donors and consumers. This results in no blood deficient, and minimization of, the corruption of blood products and lower costs. Humanitarian supply chain includes planning, managing activities related to materials, information, and finances when providing relief to affected people

The purpose of this study is to optimize the sustainable humanitarian supply chain of blood products in Tehran. In this study, a five-echelon blood supply chain network is presented that includes donors, mobile, fixed and regional blood collection centers, and hospitals. This study proposed a multi objective mixed integer linear programming optimization model considering economic, environment and social objectives. Due to the uncertain and random nature of blood supply and demand and cost parameters, the model develops into a robust optimization model. The ε-constraint method is used to transform the multi-objective problem into a single-objective mode. The model is coded in GAMS and solved by CPLEX solver.

The model outputs include allocate blood donor to mobile and fixed blood collection centers, allocate fixed and mobile blood centers to regional blood centers, and allocate regional blood centers to hospitals. The application of the proposed model is investigated in a case problem in Tehran where real data is utilized to design a network for emergency supply of blood during potential disasters. The results indicate robust model is more efficient in controlling demand, supply and costs uncertainties. At ω = 100 the supply chain cost reaches to $51746 and unmet blood demand is zero under any earthquake scenario and, the supply chain is robust in all scenarios.

The proposed robust optimization model is useful for managers, as well as researchers, who have studied location–allocation of facilities in the blood supply chain network in post-disaster periods. To provide some managerial perspectives for the aforementioned problem, a sensitivity analysis has been performed. Important practical implications were drawn from the case study results. We showed how solution robustness (supply chain cost) can be balanced against model robustness (unmet demand). As the value of risk aversion weight, increases, the total cost indicating solution robustness increases, while the blood unmet demand indicating model robustness decreases. Finally results analysis, concluding remarks and directions for further research are presented.