Robust optimization can be effectively used to protect production plans against uncertainties. This is particularly important in sectors where variability is inherent the process that must be optimally planned. The drawback is that, in real situations, some information can be added in order to better control the extra-cost resulting from considering the parameter variability. This work investigates how demand forecasting can be used in conjunction with robust optimization in order to achieve an optimal planning considering demand uncertainties. In the proposed procedure forecast is used to update uncertain parameters of the robust model. Moreover the robustness budget is optimized at each planned stage in a rolling planning horizon. In this way the parameters of the robust model can be dynamically updated tacking information from the data. The study is applied to a reverse logistics case, where the planning of sorting for material recycling is affected by uncertainties in the demand, consisting of the waste material to be sorted and recycled. Results are compared with the standard robust optimization approach, using real case instances, showing potentialities of the proposed method.