Management of Reconfigurable Production Networks in Order-Based Production
| Autor: | Helge Epha, Stefan Braunreuther, Johannes be Isa, Gunther Reinhart |
|---|---|
| Přispěvatelé: | Fraunhofer IGCV, Technische Universität Berlin (TU), Augsburg University of Applied Sciences, Technische Universität Munchen - Université Technique de Munich [Munich, Allemagne] (TUM), Ilkyeong Moon, Gyu M. Lee, Jinwoo Park, Dimitris Kiritsis, Gregor von Cieminski, TC 5, WG 5.7 |
| Rok vydání: | 2018 |
| Předmět: |
Mass-customization
0209 industrial biotechnology 021103 operations research business.industry Mass customization 0211 other engineering and technologies Production planning 02 engineering and technology Capital expenditure 020901 industrial engineering & automation Information and Communications Technology Reconfigurable production networks [INFO]Computer Science [cs] Volatility (finance) business Industrial organization Agile software development |
| Zdroj: | Advances in Production Management Systems. Smart Manufacturing for Industry 4.0 ISBN: 9783319997063 APMS (2) IFIP Advances in Information and Communication Technology IFIP International Conference on Advances in Production Management Systems (APMS) IFIP International Conference on Advances in Production Management Systems (APMS), Aug 2018, Seoul, South Korea. pp.490-497, ⟨10.1007/978-3-319-99707-0_61⟩ |
| DOI: | 10.1007/978-3-319-99707-0_61 |
| Popis: | Part 7: Industry 4.0 – Collaborative Cyber-physical Production and Human Systems; International audience; High market volatility as well as increasing global competition in manufacturing lead to a growing demand for flexible and agile production networks. Advanced production systems in turn conduct high capital expenditure along with high investment risks. However, the latest developments of information and communication technology in production environments carry promising optimization opportunities.The approach of this paper is to apply reconfigurable production networks for scalable capacity and low capital expenditure by adapting “Production planning as a service”. Therefore, a genetic algorithm was applied to solve a complex optimization problem. At the end of this work, a prototypical application of the discussed subject is shown on a world-leading household appliance manufacturer. |
| Databáze: | OpenAIRE |
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