Emergency blood supply plays an important role in disaster relief. However, large fluctuations in blood demand make precise blood forecasting a challenge. The demand for blood and information about transportation disruption are dynamically revealed over time. A discrete-time Mixed Integer Linear Programming (MILP) mathematical formulation will be developed to cope with the underlying uncertainty through a rolling horizon approach with the purpose to optimally manage the blood supply chain system in disaster relief (i.e., determining the amount of blood collection, location of the blood collection stations, transport and storage needed to meet the demand in the worst-case scenario and minimize the total response time and total operational costs). The model also takes blood characteristics and blood emergency supply constraints into consideration. The performance of the proposed model and solution method is then investigated in a real case study from the 2008 Wenchuan earthquake and the results are discussed in detail.
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