Due to economic pressure industries, when planning, tend to focus on optimizing the expected profit or the yield. The consequence of highly optimized solutions is an increased sensitivity to uncertainty. This generates additional "operational" costs, incurred by possible modifications of the original plan to be performed when reality does not reflect what was expected in the planning phase. The modern research trend focuses on "robustness" of solutions instead of yield or profit. Although robust solutions have a lower expected profit, they are less sensitive to noisy data and hence generate less operational costs. In this talk, we focus on the robustness of airline schedules. We compare different existing methods for "robust scheduling" on simulated data in order to analyze their performance. In particular, we analyze the consequences of erroneous prediction models on the performance of robust solutions. Simulations are based on the public data of the ROADEF Challenge 2009 (