Master production scheduling with scenario-based capacity-load factors in a rolling planning environment - Julian Englberger; Frank Herrmann; Michael Manitz (Logistics Research, 2022)
This paper proposes two stochastic programming models for master production scheduling with capacity-load factor scenarios. In contrast to other work on production planning with load-dependent lead times or dynamic capacity loads, we iteratively build a set of realistic capacity-load factor scenarios by simulating the realization of the master production schedules in a rolling horizon environment. Therefore, we integrate the models into a hierarchical production planning and control system that is common in industrial practice and measure the effective capacity-load factors. With these factors, we resolve the master production scheduling problem. Toa evaluate the performance of the proposed models, we compare the stochastic models with the common approach to reduce the nominally available capacity for master production scheduling. In our experiments, the stochastic models significantly reduce the tardiness of production orders caused by capacity bottlenecks.
Keywords: 650; Hierarchical Production Planning; Production Planning & Control; Master Production Scheduling; Stochastic Optimization