This thesis introduces a theory for model-driven objective functions in Model Predictive Control (MPC) algorithms. For scheduling supply chains, such model-driven objective functions allow the MPC algorithm to make optimal scheduling decisions by anticipating future changes in product flow and transfer price dynamics. Including such dynamics introduces new insights in the decision-making process for supply chains, as current supply-chain management relies on professional expertise and modelling techniques with static product flows and transfer prices. In this thesis, a dynamic model for product flows and transfer prices at a storage depot in the supply chain is developed with Economic Engineering Systems Theory. We develop a model-driven objective function for profit-maximization in an MPC scheduling algorithm using the Economic Engineering storage depot model. The advantage of the model-driven objective function is the ability to assess the product flow and transfer price dynamics that affect the revenues and costs for various decisions. The MPC algorithm for scheduling shipments towards storage depots includes the constraints in the supply chain and offers the potential to control processes in the supply chain in a dynamic and automated way. This thesis applies the modelling technique and scheduling algorithm to the refined oil product supply chain of Shell for DACH. The algorithm automates processes that form the bridge between the yearly tactical planning and the day-to-day scheduling operations. Supply-chain companies like Shell benefit from the scheduling algorithm by optimal decision-making, additional time for strategic activities and less room for human error.