A new modeling framework for multiperiod supply chain planning problems is proposed in this paper. The problem is transformed into a set of two optimization problems. At first, demands in distinctive geographical locations over multiple time periods are aggregated and distributed to multiple entities using an operation policy that was employed by Ryu and Pistikopoulos (Ind. Eng. Chem. Res. 2005, 44, 2174). Individual single-site multiperiod planning problems are then constructed based on their allocated demands. These optimization problems are mathematically formulated as mixed-integer linear programming (MILP) problems and numerically demonstrated with two examples. From the results of the examples, it is seen that the key issue in the multiperiod supply chain planning problem is the distribution of multiple demands to multiple entities.