In most multiproduct batch plants, the short-term planning activity starts by considering the set of product orders to be filled during the scheduling period. Each order specifies the product and the amount to be manufactured as well as the promised due date and the release time. Several orders can be related to the same product, though featuring different quantities and due-dates. The initial task to be accomplished by the scheduler is the so-called batching process that transforms the product orders to fill into equivalent sets of batches to be scheduled and subsequently assigns a due date to each one. To execute the batching procedure for a particular product, the scheduler should not only account for the preferred unit sizes but also for all the orders related to such a product and their corresponding deadlines. Frequently, a batch is shared by several orders with the earliest one determining the batch due-date. In this paper, a new two-step systematic methodology for the scheduling of single-stage multiproduct batch plants is presented. In the first phase, the product batching process is accomplished to minimize the work-in-process inventory while meeting the orders' due-dates. The set of batches so attained is then optimally scheduled to meet the product orders as close to their due dates as possible. New MILP continuous-time models for both the batching and the scheduling problems were developed. In addition, widely known heuristic rules can be easily embedded in the scheduling problem formulation to gel a faster convergence to near-optimal schedules for 'real-world' industrial problems. Three example problems involving up to 29 production orders have been successfully solved in low computational time.