An adaptive model predictive control configuration for production-inventory systems

In this paper, an adaptive model predictive control (MPC) configuration is proposed for the identification and control of production-inventory systems. The time-varying dynamic behavior of the production process is approximated by an adaptive Finite Impulse Response (FIR) model. The well-known recursive least-squares (RLS) method is used for the online identification of the model coefficients. The adapted model along with a smoothed estimation of the future customer demand are used to predict inventory levels over the optimization horizon. The efficiency of the proposed scheme is evaluated regarding its capability to eliminate the inventory drift. The performance of the method with respect to the bullwhip effect is also considered and studied in the paper. Comparison with non-adaptive control approaches illustrates the advantages of the proposed method.