The development of hybrid simulation-optimization methods has allowed to explore the complexities of planning and scheduling problems. In this work, a simulation-optimization approach is developed to provide decision-support to industrial processes, combining a mathematical formulation with a detailed discrete-event simulation model. To solve industrial planning and scheduling problems, the methodology iteratively estimates the modelling parameters to maximize the output in a multistage production facility, while guaranteeing allocation constraints and main process uncertainties to satisfy the demand. The production plan is evaluated at the MILP model and the capacity-feasible schedule is generated by the dispatching rules in the simulation model. Results highlight the advantages of the hybrid approach to guarantee optimized process performance metrics in a simulated operational planning.