In practice, the uncertainty in processing time data frequently affects the feasibility of
optimal solution of the nominal production scheduling problem. Using the unit-specific
event-based continuous time model for scheduling, we develop a novel multi-stage robust
approach with corrective action to ensure robust feasibility of the worst case solution
while reducing the conservatism arising from traditional robust optimization approaches.
We quantify the probability of constraint satisfaction by using a priori and a posteriori
probabilistic bounds for known and unknown uncertainty distributions, consequently, improving
the objective value for a given risk scenario. Computational experiments on several
examples were carried out to measure the effectiveness of the proposed method. For
a given constraint satisfaction probability, the proposed method improves the objective
value compared to the traditional robust optimization approaches
