Integration of scheduling and control in process manufacturing systems has traditionally resulted in greater coordination between the different layers of hierarchy in optimization and control. However, the effectiveness of this coordination as well as the overall responsiveness of the manufacturing system Could be greatly improved if such integration explicitly accounts for parametric variations at each of the layers. This paper proposes an approach to the problem of robust integration of the scheduling and control layers in an uncertainty analysis framework so as to yield robust manufacturing systems that can perform well in the presence of the parametric variations. The uncertainty analysis is performed here using different uncertainty approaches such as the chance constrained programming, fuzzy mathematical programming, and robust optimization. In this paper, we propose to analyze the impact of the uncertainty on the different manufacturing objectives in a multiobjective Pareto sense. The deterministic integrated scheduling and control model taken from published work [Flores-Tlacuahuac, A.; Grossmann, I. E. Ind. Eng. Chem. Res. 2006, 45, 6698] forms the basis Of Our work toward addressing the uncertainty issues. We demonstrate the development of improved and robust integration of the scheduling and control tasks using the proposed frameworks.