The uncertainty present in any process environment and related not only to variable market demand but also to operational disturbances is usually unavoidable, and therefore, poor performance may be attained with the execution of deterministic optimal schedules. In this work, the short-term scheduling problem in chemical batch processes with variable processing times is addressed with the aim to identify robust schedules able to face the major effects driving the operation of batch processes with uncertain times, i.e., idle and wait times. The problem is modeled using a two-stage stochastic approach accounting for the minimization of a weighted combination of the expected makespan and the expected wait times. The formulation is extended to explicitly manage risk by optimizing three different robustness criteria. The application of the proposed formulation to academic and industrially based examples shows the efficiency of the proposed approach and highlights the importance of considering the uncertainty in the short-term scheduling level.