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A MULTI-DIMENSIONAL CONFIGURATION ALGORITHM FOR MODULAR PRODUCT ARCHITECTURES

Published online by Cambridge University Press:  11 June 2020

F. M. Seiler*
Affiliation:
Hamburg University of Technology, Germany
D. Krause
Affiliation:
Hamburg University of Technology, Germany

Abstract

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With an increasing demand for product individualisation leading to increased product architecture complexity and -costs, modular kits are one common measure to cope with this issue. The management of such a modular kit as well as the methodical determination of a specific product variant is key to the manufacturer's success. As multiple influence factors need to be taken into account when configuring product variants, we propose a multi-dimensional geometric optimisation algorithm, allowing for prioritising varying customer demands and thereby determining the ideally balanced product variant.

Type
Article
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
The Author(s), 2020. Published by Cambridge University Press

References

Broumi, S. et al. (2019), “A new algorithm for finding minimum spanning trees with undirected neutrosophic graphs”, Granular Computing, Vol. 4 No. 1, 6369.Google Scholar
Bursac, N. (2016), Model Based Systems Engineering zur Unterstützung der Baukastenentwicklung imKontext der Frühen Phase der Produktgenerationsentwicklung, Dissertation. Stolzenberger, Leimen.Google Scholar
Cicconi, P. et al. (2018), “A model-based simulation approach to support the product configuration and optimization of gas turbine ducts”, In: Computer-Aided Design & Applications, Vol. 15, pp. 807818. https://doi.org/10.1080/16864360.2018.1462564Google Scholar
Diestel, R. (2000), Graphentheorie: Mathematical Physics and Mathematiks. Springer, HeidelbergGoogle Scholar
Dijkstra, E.W. (1959), “A Note on Two Problems in Connexion with Graphs”, In: Numerische Mathematik 1, pp. 269271.CrossRefGoogle Scholar
Erixon, G. (1998), “Modular Function Deployment: A Method for Product Modularisation”, The Royal Insitute of Technology, Department of Manufacturing Systems, Stockholm.Google Scholar
Hadzic, T. and Andersen, H.R. (2006), “A BDD-Based Polytime Algorithm for Cost-Bounded Interactive Configuration”. In: Proceedings of the 21st National Conference on Artificial Intelligence (AAAI).Google Scholar
Hermann, M.-O., Michler, J. and Schönthaler, F. (2013), “Wo Kundenwünsche auf technische und wirtschaftliche Notwendigkeiten treffenBusiness News, Vol. 3, pp. 1316.Google Scholar
Krause, D. and Gebhardt, N. (2018), “Methodische Entwicklung modularer Produktfamilien: Hohe Produktvielfalt beherrschbar entwickeln”, Springer, Hamburg. https://doi.org/10.1007/978-3-662-53040-5CrossRefGoogle Scholar
Krieter, S. et al. (2018), “Propagating Configuration Decisions with modal Implication Graphs”, In: proceedings of 40th International Conference on Software Engineering. ICSE ‘18. ACM, pp. 898909. https://doi.org/10.1145/3180155.3180159CrossRefGoogle Scholar
Liebisch, M. (2014), Aspektorientierte Datenhaltung in Produktkonfiguratoren - Anforderungen, Konzepteund Realisierung. Dissertation, Jena.Google Scholar
Seiler, F., Greve, E. and Krause, D. (2019a), “Development of a Configure-to-Order-Based Process for the Implementation of Modular Product Architectures: A Case Study”. In Proceedings of the 22nd International Conference on Engineering Design (ICED19), Delft, The Netherlands, 5-8 August 2019. https://doi.org/10.1017/dsi.2019.304CrossRefGoogle Scholar
Seiler, F., Schwede, L.-N. and Krause, D. (2019b), MBSE-basierte Produktkonfiguratoren zur Analyse der Modularisierung bei der Entwicklung modularer Baukastensysteme. Entwerfen Entwickeln Erleben in Produktenwicklung und Design, Band 2, Dresden.Google Scholar
Simpson, T.W., Siddique, Z. and Jiao, R.J. (eds.) (2006), “Product platform and product family design: methods and applications.” Springer Science & Business Media. https://doi.org/10.1007/0-387-29197-0_1CrossRefGoogle Scholar
Stormer, H. (2007), “Kundenbasierte Produktkonfiguration”, In Informatik Spektrum, Vol. 30. pp. 322326. https://doi.org/10.1007/s00287-007-0177-1CrossRefGoogle Scholar
Weisz, G., Györgi, A. and Szepesvári, C. (2018), “LeapsAndBounds: A Method for approximately optimal algorithm configuration”, Proceedings of the 35th International Conference on Machine Learning, Stockholm, Sweden, PMLR 80.Google Scholar
Zennaro, I. et al. (2019), “Big size highly customised product manufacturing systems: a literature review and future research agenda”, In International Journal of Production Research, Vol. 57 No. 15-16, pp. 53625385, https://doi.org/10.1080/00207543.2019.1582819CrossRefGoogle Scholar