We solve the challenging problem of integrated planning, scheduling, and dynamic optimization for sequential batch processes with fixed batch sizes. The integrated problem is first formulated into a complicated mixed-integer nonlinear programing (MINLP) problem. There are a planning model, multiple scheduling models in planning periods, and a number of dynamic models describing task execution processes. To efficiently solve the complex MINLP problem, we develop an efficient method which separates the subproblems using metamodels to represent the linking functions. Compared to the direct solution approach, the proposed method significantly reduces the computational time.