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 MKT 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. (C) 2019 Elsevier Ltd. All rights reserved.