TY - GEN A1 - Englberger, Julian A1 - Herrmann, Frank A1 - Manitz, Michael T1 - Berücksichtigung stochastischer Einflüsse bei der Hauptproduktionsprogrammplanung T2 - Im Mix der Methoden - Neue Perspektiven der Produktionswirtschaft. 1. Doktorandenworkshop der Wissenschaftlichen Kommission für Produktionswirtschaft, 19./20. März 2021, Berlin Y1 - 2021 PB - TU Berlin ER - 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 - TY - CHAP A1 - Herrmann, Frank A1 - Lange, Frederick A1 - Manitz, Michael T1 - Order release within hierarchical production planning and control using product specific clearing functions T2 - Proceedings of the International Annual Conference of the German Operations Research Society 2014 (OR 2014), Aachen, September 2 - 5, 2014, Germany KW - Hierarchische Produktionsplanung KW - Auftragsabwicklung KW - clearing functions Y1 - 2014 ER - TY - CHAP A1 - Herrmann, Frank A1 - Manitz, Michael T1 - Some remarks to the shape of Clearing Functions T2 - Proceedings of the International Annual Conference of the German Operations Research Society 2015 (OR 2015) Wien, September 1 - 4 KW - clearing functions KW - production planning Y1 - 2015 ER - TY - BOOK A1 - Herrmann, Frank A1 - Manitz, Michael T1 - Materialbedarfsplanung und Ressourcenbelegungsplanung BT - Durchführung in Produktionsplanungs- und -steuerungssystemen und ihre Analyse KW - Materialbedarf KW - Produktionsplanung KW - Produktionssteuerung KW - PPS Y1 - 2017 SN - 978-3-658-12543-1 U6 - https://doi.org/10.1007/978-3-658-12543-1 PB - Springer Gabler CY - Wiesbaden ER - TY - CHAP A1 - Fuchs, Michael A1 - Herrmann, Frank A1 - Manitz, Michael T1 - A Lagrangian relaxation based approach for the capacitated multi-level lot sizing problem with stochastic demands T2 - SMMSO 2019 - 12th Conference on Stochastic Models of Manufacturing and Service Operations, June 9th - 14th, 2019, Goslar, Germany KW - Kapazitätsbelegungsplanung KW - Nachfrageermittlung KW - Stochastischer Prozess KW - Lagrange-Relaxation Y1 - 2019 ER - TY - CHAP A1 - Manitz, Michael A1 - Herrmann, Frank A1 - Munninger, Maximilian T1 - Considering sequence-dependent stochasticity in production schedules T2 - Proceedings of the International Annual Conference of the German Operations Research Society 2017 (OR 2017), Berlin, September 6 - 8, 2017 KW - Produktionsplanung KW - Ablaufplanung KW - Reihenfolgeproblem KW - Stochastischer Prozess Y1 - 2017 ER - TY - CHAP A1 - Herrmann, Frank A1 - Manitz, Michael ED - Claus, Thorsten ED - Herrmann, Frank ED - Manitz, Michael T1 - Ein hierarchisches Planungskonzept zur operativen Produktionsplanung und -steuerung T2 - Produktionsplanung und -steuerung N2 - Ausgangspunkt für die operative Produktionsplanung und -steuerung ist eine Infrastruktur aus Produktionsressourcen mit definierten Produktionskapazitäten, auf denen jeweils die Produktion eines oder mehrerer verschiedener Produkte erfolgen kann. Y1 - 2021 SN - 978-3-662-64290-0 U6 - https://doi.org/10.1007/978-3-662-64291-7_2 SP - 9 EP - 25 PB - Springer Gabler CY - Berlin, Heidelberg ER - TY - CHAP A1 - Claus, Thorsten A1 - Herrmann, Frank A1 - Manitz, Michael ED - Claus, Thorsten ED - Herrmann, Frank ED - Manitz, Michael T1 - Knappe Kapazitäten und Unsicherheit - Analytische Ansätze und Simulation in der Produktionsplanung und -steuerung T2 - Produktionsplanung und -steuerung Y1 - 2021 SN - 978-3-662-64290-0 U6 - https://doi.org/10.1007/978-3-662-64291-7_1 SP - 3 EP - 7 PB - Springer Gabler CY - Berlin, Heidelberg ER - TY - JOUR A1 - Englberger, Julian A1 - Herrmann, Frank A1 - Manitz, Michael T1 - Master production scheduling with scenario-based capacity-load factors in a rolling planning environment JF - Logistics Research, Special Issue “Supply Chain Analytics in the 2020s” N2 - 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. Y1 - 2022 U6 - https://doi.org/10.23773/2022_12 VL - 15 SP - 1 EP - 16 PB - Bundesvereinigung Logistik (BVL) e.V. CY - Bremen ER -