A novel decomposition algorithm for the integration of scheduling and control of multiproduct, multiunit batch processes under stochastic parameter uncertainty is presented. This iterative algorithm solves a scheduling and dynamic optimization problem around a nominal point while approximating uncertainty through back-off terms, embedded in the operational process constraints. Monte Carlo simulations are performed to propagate uncertainty and to evaluate dynamic feasibility; statistical information is drawn from these simulations to update the back-off terms. Convergence of the algorithm results in a set of scheduling and control decisions that aim to keep the plant dynamically feasible under the effect of uncertainty up to a user-defined tolerance criterion. The proposed algorithm is shown to be successfully applied to a multiproduct, multiunit batch plant under the effects of different probability density functions in the uncertain parameters. The algorithm's performance is gauged against a fully integrated, mixed logic dynamic optimization problem with multiscenario-based uncertainty. The solution to the integrated algorithm is obtained with mixed integer nonlinear programming solvers. Results show that the proposed decomposition algorithm remains computationally attractive, without compromising the quality of the solution.