In order to achieve optimal operational conditions, the integration of decision making across different layers of a company and the consideration of uncertain parameters in view of dynamic market conditions are essential. In this study, we propose a framework for the integration of scheduling and control for nonlinear systems under process uncertainties. The proposed approach includes the use of a tube-based robust model predictive control to handle disturbances affecting the control layer of the problem, the use of classification methodologies to determine the feasible space of operation of the process, and the use of surrogate models to derive the closed-loop input-output behavior of the dynamic system. Case studies are utilized to illustrate the performance of the proposed framework and evaluate the impact of control-level disturbances in scheduling solutions.