Robust optimization for lot-sizing problems under yield uncertainty

Production yield can be highly volatile and uncertain, especially in industries where exogenous environmental factors such as the climate or raw material quality impact manufacturing process. Thus, for production planning, it is necessary to take into account uncertainty obtain robust efficient plans. In this paper, we consider lot-sizing problems under uncertainty. We propose a multi-period, single-item problem with backorder via optimization methodology. First, formulate model budgeted set, which optimized worst case perspective ensure feasibility of proposed plan any realization described by set. Second, analyze structure optimal solution, derive policy special inventory management box These results help us develop dynamic program polynomial complexity stationary rate. Finally, extensive computational experiments show robustness effectiveness through an average analyses. The demonstrate that approach immunizes system against Moreover, comparison nominal model, deterministic safety stock, stochastic shows balances costs better reducing backorders at expense more often producing larger amount goods.