Production planning with a two-stage stochastic programming model in a kitting facility under demand and yield uncertainties

The main objective of this study is to develop a two-stage stochastic programming framework for lot-sizing and scheduling the production activities at a kitting facility to support a manufacturing plant. The novelty of the study lies in modeling multiple uncertainties using two-stage stochastic programming to solve a kitting specific production planning problem in a manufacturing setting. The demand for the kits and the yield of the kitting workers are the two sources of uncertainties considered in this study. The first-stage decisions include the baseline production schedule and the workforce requirement, while the second stage makes recourse decisions on overtime production. The proposed decision-making framework is validated on a multi-period, multi-product case study involving a kitting facility supporting a manufacturing plant producing braking equipment. The main conclusion of the study is that uncertainties have significant impacts on kitting planning decisions and that the proposed two-stage stochastic programming model was robust in determining optimal production plans under uncertainty.