TY - GEN A1 - Englberger, Julian A1 - Herrmann, Frank A1 - Claus, Thorsten T1 - A simulation analysis of Independent Requirements Consumption in Master Production Scheduling T2 - ASIM Workshop, Trends in Computational Science and Engineering (ASIM TCSE 2012), February 13-14, 2012, Vienna University of Technology, Austria N2 - This poster presents purpose and function of the Independent Requirements Consumption in Master Production Scheduling and gives recommendations for its application. The approach of hierarchical production planning and its planning problems are explained. The problem of new customer requirements information between planning periods and solving options are shown. The solving option of Independent Requirements Consumption is focus of this poster. The algorithm of the Independent Requirements Consumption that is implemented in commercial Enterprise Resource Planning (ERP) systems like the SAP system is analyzed. The methods for analyzing the effects of the Independent Requirements Consumption are outlined. The scenarios for simulation studies are stated and compared to similar investigations. The simulation studies were performed on a simulation system at IPF that contains both a production planning and control system and a production. The extent of simulation analyses outreaches similar investigations, especially because of the variety of scenarios. For scenarios in which capacity is available to a sufficient extent, advice for setting the Independent Requirements Consumption parameters is given. It is pointed out that with production constrained scenarios, the necessity to level out production leads to problems. An algorithm to determine necessary inventory levels is presented. It is stated that straight advice for Independent Requirements Consumption parameter settings might not be possible with production constrained scenarios. Further analyses with production constrained scenarios as well as investigations concerning safety stocks in combination with Independent Requirements Consumption are current and future research topics at the Innovation Center for Production Logistics and Factory Planning. KW - Produktonsplanung KW - Simulation analysis KW - Independent Requirements Consumption KW - Master Production Scheduling KW - Enterprise-Resource-Planning KW - Kundenorientierung Y1 - 2012 ER - TY - GEN A1 - Terbrack, Hajo A1 - Claus, Thorsten A1 - Herrmann, Frank T1 - An Optimization Model for Production Scheduling with Energy Consumption and Energy-related Emissions T2 - OR 2022, International Conference on Operations Research 2022, September 6-9, 2022, Karlsruhe N2 - Due to the scarcity of resources, rising costs as well as the further need for ecological improvement in industrial production, firms are increasingly motivated to take into account energy consumption in production planning. Associated with this, the consideration of energy-related emissions is becoming an additional point of interest for decisionmakers in both, economic and ecological terms. Based on an extensive systematic literature review, the study at hand presents a multicriterial scheduling problem that addresses tardiness, energy consumption and energy-related emissions in production scheduling. A model is introduced that takes into account job release dates, multiple machine states, a power demand threshold as well as time-varying emission conversion factors. Multiple case studies are analysed and the effects of different technological implementations as energy onsite generation and energy storage systems are evaluated. Furthermore, several price mechanisms regarding energy procurement and emission trading are discussed. Moreover, by coupling the optimization model to a discrete event simulation model, we are able to compare the model results to real case scenarios. First computational experiments have demonstrated that the model and its extensions can effectively reduce the environmental impact associated with production while simultaneously considering corresponding economic objectives. Based on the proposed approach, our research aims on answering the question with which economic trade-offs an ecological improvement in terms of energy consumption and emission output can be achieved in production. KW - Optimization KW - Production Scheduling KW - Energy Consumption Y1 - 2022 ER - TY - GEN A1 - Herrmann, Frank A1 - Schoen, Maximilian A1 - Claus, Thorsten A1 - Dai, Chenghao T1 - Aggregate Production Planning under Risk of Disruption T2 - OR 2024, International Conference on Operations Research 2024, September 3-6, 2024, Munich N2 - In recent years, large scale disruptions to global supply chains, like the Covid pandemic, a ship blocking the Suez canal or sanctions against Russia, have caused production to slow down or even to stand still, causing shortages and massive losses for affected businesses. Even if only specific companies were originally affected, shortages and delays rippled along the supply network. The established approach to deal with disruptions is to utilize safety stock and capacity to compensate for fluctuations in uncertain quantities like customer demand. This approach is tried and tested for small fluctuations. To address larger disruptions, like the above given, very high safety stock and capacity would be needed, which would lead to unnecessarily high costs. Resilience has often been viewed as an expensive capability that drives costs. Recent studies however advocate for the development of lean resilience concepts, creating new capabilities, which enable resilience and can deal with large fluctuations, reimagining resilience from the perspectives of efficiency and value creation. This contribution identifies gaps in current research and establishes structural deficits of approaches discussed in the literature regarding supply chains. Firstly, the need for rigorous, quantitative definitions of resilience and relevant disruptions is justified. Then, a stochastic model for aggregate production planning that includes capabilities to compensate for such large fluctuations along several dimensions is proposed. Lastly, this model is then applied to a case study pertaining to a realistic supply chain under the risk of large scale disruptions and the results of this approach are evaluated. KW - Supply Chain Resiliance KW - Aggregate Production Planning KW - Supply Chain Disruption Y1 - 2024 ER -