We propose a model-based optimization approach for the integration of production scheduling and dynamic process operation for general continuous/batch processes. The method introduces a discrete time formulation for simultaneous optimization of scheduling arid operating decisions. The process is described by the resource task network (RTN) representation coupled with detailed first-principles process dynamic models. General cbmplications in Scheduling and control can be fully represented in this modeling framework, such as customer orders, transfer policies, and requirements on product quality and process safety. The scheduling and operation layers are linked with the task history state variables in the state space RTN model: A tailored generalized Benders decomposition (GBD) algorithm is applied to efficiently solve the resulting large nonconvex mixed-integer nonlinear program by exploring the particular model structure. We apply the integrated optimization approach to a polymerization process with two parallel semibatch reactors and continuous storage and purification units. The two polymerization reactors share cooling utility from the same source, and the utility price is dependent on the consumption rate. The optimization objective is to design the process schedule and reactor control policies simultaneously to maximize the overall process profit. The case study results suggest improvements in plant profitability for the integrated approach, in contrast to the typical sequential approach, where recipes of the polymerization tasks are individually optimized but the interactions among process units are overlooked.