Two Stage Distributionally Robust Optimization for Post-Disaster Material Supply Network

Material supply network for emergency rescue of natural disasters is of vital importance in reducing casualties and protecting people's lives and property. This paper introduces a post-disaster rescue material supply network. Due to the strong uncertainty of material demand in disaster-stricken areas and the difficulty in obtaining probability distributions with sufficient accuracy, we propose a two-stage distribution robust optimization method. We construct the ambiguity set of material demand in the supply network based on statistics such as the first and second moments from historical data. Then, the optimization problem is formulated as a two-stage stochastic robust optimization model. Linear decision-making rules and duality theory are jointly applied to facilitate solving process of the proposed optimization model. Via a numerical experiment, the effectiveness of the proposed approach is validated.