Production scheduling and linear MPC: Complete integration via complementarity conditions. (9th June 2019) - Simkoff, Jodie M.; Baldea, Michael
Highlights: We present a novel framework for completely integrated scheduling and model predictive control of chemical processes. The controller behavior is represented fully and explicitly by including the MPC KKT conditions directly in the scheduling formulation. Two case studies demonstrate that schedules obtained using knowledge of closed-loop dynamics lead to increased profit compared with "open-loop" scheduling approaches. Abstract: Competitive global market conditions and the availability of real-time pricing data call for agile and flexible operations in the chemical process industries. As the operating paradigm shifts, process scheduling decisions may consider a shorter time scale, and interactions with the process control layer become significant. The integration of these two decision-making layers is an active area of research. In this work, we propose a novel framework for the integration of production scheduling and linear model predictive control. Our approach explicitly represents the closed-loop response of the system by embedding the KKT conditions of the controller in a nonlinear programming formulation of the optimal scheduling problem. We present two case studies which demonstrate that control-informed scheduling leads to superior realized performance compared with a hierarchical, sequential decision-making structure.