Daniel Rivera, Arizona State University
A Process Control Perspective to Managing Production-Inventory Systems: Modeling, Forecasting, and Control
Production-inventory systems are iconic dynamical systems that are meaningful to applications in process settings and beyond. In this talk we focus on the classical single-node production-inventory system, and
survey some recent published work from our laboratory addressing modeling, control, and demand forecasting considerations. A combined feedback-feedforward, three degree-of-freedom (3 DoF) Internal Model Control (IMC) algorithm is presented as useful in understanding fundamental control requirements for this system, but in practice a Model Predictive Control (MPC) algorithm is required. A standard MPC
solution is contrasted with IMC, which ultimately leads to an improved MPC formulation that mimics the positive attributes of the 3 DoF IMC control system while addressing practical requirements such as
constraint handling. Feedforward compensation of demand forecasts is critical to the performance of the closed-loop system; borrowing from the field of control-relevant identification, an analysis procedure for estimating demand models is presented which relies on sensible prefiltering of the demand data to emphasize the goodness-of-fit in the regions of time and frequency most important for achieving desired
levels of closed-loop performance. The various points of the presentation are illustrated with meaningful examples.
Biography
Daniel E. Rivera is professor of chemical engineering in the School for Engineering of Matter, Transport, and Energy at Arizona State University in Tempe, Arizona. He received the B.S. degree in chemical engineering from the University of Rochester, New York in 1982, the M.S. degree in chemical engineering from the University of Wisconsin-Madison in 1984, and the Ph.D. in chemical engineering from the California Institute of Technology, Pasadena, California in 1987. Prior to joining ASU he was a member of the Control Systems Section of Shell Development Company in Houston, Texas. His research interests span the topics of dynamic modeling using system identification, robust process control, and applications of control engineering to problems in supply chain management and behavioral health.