Model predictive control with an on-line identification model of a supply chain unit

A model predictive controller was designed in this study for a single supply chain unit. A demand model was described using an autoregressive integrated moving average (ARIMA) model, one that is identified on-line to forecast the future demand. Feedback was used to modify the demand prediction, and profit was chosen as the control objective. To imitate reality, the purchase price was assumed to be a piecewise linear form, whereby the control objective became a nonlinear problem. In addition, a genetic algorithm was introduced to solve the problem. Constraints were put on the predictive inventory to control the inventory fluctuation, that is, the bullwhip effect was controllable. The model predictive control (MPC) method was compared with the order-up-to-level (OUL) method in simulations. The results revealed that using the MPC method can result in more profit and make the bullwhip effect controllable.