Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/297204 
Year of Publication: 
2022
Citation: 
[Journal:] Logistics Research [ISSN:] 1865-0368 [Volume:] 15 [Issue:] 1 [Article No.:] 12 [Year:] 2022 [Pages:] 1-16
Publisher: 
Bundesvereinigung Logistik (BVL), Bremen
Abstract: 
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.
Subjects: 
Hierarchical Production Planning
Production Planning & Control
Master Production Scheduling
Stochastic Optimization
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article

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