Supply Chains (SC) are increasingly affected by internal and external factors such as floods, earthquakes, war, and sanctions, creating significant uncertainty. The wheat and flour SC directly impacts household diets, and any disruption can lead to severe social, political, and economic consequences. This study presents a Mixed-Integer Linear Programming (MILP) model to minimize total costs. The model addresses a multi-product, multi-period problem that includes seven levels: farms, import points, silos (if construction), flour production plants, warehouses, and both essential and non-essential customers. To account for uncertainty, a two-stage stochastic programming approach is applied. In the first stage, the model identifies the optimal locations for silo construction. In the second stage, it optimizes the flow of goods across the network based on different scenarios. The results show the ideal locations for silo construction, the required import volume for each scenario, and flour shortages for non-essential customers, ultimately helping reduce dependency on imports for essential goods.