TY - JOUR A1 - Englberger, Julian A1 - Herrmann, Frank A1 - Manitz, Michael T1 - Two-stage stochastic master production scheduling under demand uncertainty in a rolling planning environment JF - International Journal of Production Research N2 - This paper proposes a scenario-based two-stage stochastic programming model with recourse for master production scheduling under demand uncertainty. We integrate the model into a hierarchical production planning and control system that is common in industrial practice. To reduce the problem of the disaggregation of the master production schedule, we use a relatively low aggregation level (compared to other work on stochastic programming for production planning). Consequently, we must consider many more scenarios to model demand uncertainty. Additionally, we modify standard modelling approaches for stochastic programming because they lead to the occurrence of many infeasible problems due to rolling planning horizons and interdependencies between master production scheduling and successive planning levels. To evaluate the performance of the proposed models, we generate a customer order arrival process, execute production planning in a rolling horizon environment and simulate the realisation of the planning results. In our experiments, the tardiness of customer orders can be nearly eliminated by the use of the proposed stochastic programming model at the cost of increasing inventory levels and using additional capacity. KW - Hierarchische Produktionsplanung KW - Fertigungsprogrammplanung KW - Nachfrageverhalten KW - Stochastische Optimierung KW - Master production scheduling KW - Demand uncertainty KW - Two-stage stochastic programming KW - Scenario-based stochastic programming with recourse KW - Hierarchical production planning Y1 - 2016 U6 - https://doi.org/10.1080/00207543.2016.1162917 VL - 54 IS - 20 SP - 6192 EP - 6215 PB - Taylor & Francis ER -