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 dynamic optimization (MLDO) problem that is then discretized into a large-scale 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 two efficient methods that separate the subproblems using surrogate models to represent the linking functions. The first method decomposes the dynamic optimization problems from the integrated planning and scheduling problem where the surrogate models represent task processing costs dependent on the processing times. The second method further decomposes the scheduling problems from the planning problem where the surrogate models represent production costs dependent on production quantities. Compared to the direct solution approach, the proposed methods reduce the computational time by more than 4 orders of magnitude in the case studies.