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TWO-LEVEL OPTIMIZATION OF PRODUCT FAMILIES: APPLICATION TO A PRODUCT FAMILY OF WATER HOSE BOXES

Published online by Cambridge University Press:  27 July 2021

Sebastian Rötzer ,
Martin Le Bourgeois ,
Dominik Thoma  and
Markus Zimmermann
Sebastian Rötzer*
Affiliation:
Technische Universität München, Germany
Martin Le Bourgeois
Affiliation:
ID-Consult GmbH, Germany
Dominik Thoma
Affiliation:
ID-Consult GmbH, Germany
Markus Zimmermann
Affiliation:
Technische Universität München, Germany
*
Rötzer, Sebastian Josef, Technische Universität München, Mechanical Engineering, Germany, roetzer@pl.mw.tum.de

Abstract

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Increasing product complexity and individual customer requirements make the design of optimal product families difficult. Numerical optimization supports optimal design but must deal with the following challenges: many design variables, non-linear or discrete dependencies, and many possibilities of assigning shared components to products. Existing approaches use simplifications to alleviate those challenges. However, for use in industrial practice, they often use irrelevant commonality metrics, do not rely on the actual design variables of the product, or are unable to treat discrete variables. We present a two-level approach: (1) a genetic algorithm (GA) to find the best commonality scheme (i.e., assignment scheme of shared components to products) and (2) a particle swarm optimization (PSO) to optimize the design variables for one specific commonality scheme. It measures total cost, comprising manufacturing costs, economies of scales and complexity costs. The approach was applied to a product family consisting of five water hose boxes, each of them being subject to individual technical requirements. The results are discussed in the context of the product family design process.

Keywords

Product familiesNumerical methodsProduct modelling / modelsComplexityOptimisation
Type
Article
Information
Proceedings of the Design Society , Volume 1: ICED21 , August 2021 , pp. 3259 - 3268
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), 2021. Published by Cambridge University Press

References

Chen, C. and Wang, L. (2008), “A modified genetic algorithm for product family optimization with platform specified by information theoretical approach”, Journal of Shanghai Jiaotong University (Science), Vol. 13 No. 3, pp. 304–311.CrossRefGoogle Scholar
Chowdhury, S., Messac, A. and Khire, R. (2013), “Investigating the commonality attributes for scaling product families using comprehensive product platform planning (CP3)”, Structural and Multidisciplinary Optimization, Vol. 48 No. 6, pp. 1089–1107.CrossRefGoogle Scholar
Chowdhury, S., Messac, A. and Khire, R.A. (2011), “Comprehensive Product Platform Planning (CP3) Framework”, Journal of Mechanical Design, Vol. 133 No. 10.CrossRefGoogle Scholar
Fellini, R., Kokkolaras, M. and Papalambros, P.Y. (2006), “Quantitative platform selection in optimal design of product families, with application to automotive engine design”, Journal of Engineering Design, Vol. 17 No. 5, pp. 429–446.CrossRefGoogle Scholar
Franke, H.-J. (1998), “Variantenvielfalt und Resultierende Komplexität. Ursachen und Methoden zu Ihrer Bewältigung”, paper presented at 9th Symposium on Design for Manufacturing, 15.-16.10.1998, Schnaittach/Erlangen.Google Scholar
Fujita, K. and Yoshida, H. (2004), “Product Variety Optimization Simultaneously Designing Module Combination and Module Attributes”, Concurrent Engineering, Vol. 12 No. 2, pp. 105–118.CrossRefGoogle Scholar
Johannesson, H., Landahl, J., Levandowski, C. and Raudberget, D. (2017), “Development of product platforms: Theory and methodology”, Concurrent Engineering, Vol. 25 No. 3, pp. 195–211.CrossRefGoogle Scholar
Khajavirad, A., Michalek, J.J. and Simpson, T.W. (2009), “An efficient decomposed multiobjective genetic algorithm for solving the joint product platform selection and product family design problem with generalized commonality”, Structural and Multidisciplinary Optimization, Vol. 39 No. 2, pp. 187–201.CrossRefGoogle Scholar
Li, L. and Huang, G.Q. (2009), “Multiobjective evolutionary optimisation for adaptive product family design”, International Journal of Computer Integrated Manufacturing, Vol. 22 No. 4, pp. 299–314.CrossRefGoogle Scholar
Liu, Z., Wong, Y.S. and Lee, K.S. (2010), “Modularity analysis and commonality design: a framework for the top-down platform and product family design”, International Journal of Production Research, Vol. 48 No. 12, pp. 3657–3680.CrossRefGoogle Scholar
Liu, Z., Wong, Y.S. and Lee, K.S. (2011), “A manufacturing-oriented approach for multi-platforming product family design with modified genetic algorithm”, Journal of Intelligent Manufacturing, Vol. 22 No. 6, pp. 891–907.CrossRefGoogle Scholar
Long, D., Seering, W. and Rebentisch, E. (2009), “Finding Opportunities for Commonality in Complex Systems”, in Norell Bergendahl, M. (Ed.), Product and systems design, Design Society, Glasgow.Google Scholar
Park, J. and Simpson, T.W. (2008), “Toward an activity-based costing system for product families and product platforms in the early stages of development”, International Journal of Production Research, Vol. 46 No. 1, pp. 99–130.CrossRefGoogle Scholar
Robertson, D. and Ulrich, K. (1998), “Planning for Product Platforms”, Sloan Management Review, Vol. 39 No. 4, pp. 19–31.Google Scholar
Rötzer, S., Rostan, N., Steger, H.C., Vogel-Heuser, B. and Zimmermann, M. (2020a), “Sequencing of Information in Modular Model-based Systems Design”, Proceedings of the 22nd International DSM Conference. CAMBRIDGE, MASSACHUSETTS, USA, October, 2020 13th-15th 2020. in press.CrossRefGoogle Scholar
Rötzer, S., Thoma, D. and Zimmermann, M. (2020b), “COST OPTIMIZATION OF PRODUCT FAMILIES USING SOLUTION SPACES”, Proceedings of the Design Society: DESIGN Conference, Vol. 1, pp. 1087–1094.CrossRefGoogle Scholar
Simpson, T.W. (2006), “Methods for Optimizing Product Platforms and Product Families. Overview and Classification”, in Simpson, T.W., Siddique, Z. and Jiao, J. (Eds.), Product platform and product family design: Methods and applications, Springer, New York, London, pp. 133–156.CrossRefGoogle Scholar
Simpson, T.W. and D'Souza, B.S. (2004), “Assessing Variable Levels of Platform Commonality Within a Product Family Using a Multiobjective Genetic Algorithm”, Concurrent Engineering, Vol. 12 No. 2, pp. 119–129.CrossRefGoogle Scholar
Simpson, T.W., Seepersad, C.C. and Mistree, F. (2001), “Balancing Commonality and Performance within the Concurrent Design of Multiple Products in a Product Family”, Concurrent Engineering, Vol. 9 No. 3, pp. 177–190.CrossRefGoogle Scholar
Simpson, T.W., Siddique, Z. and Jiao, J. (2006a), “Platform-Based Product Family Development. Introduction and Overview”, in Simpson, T.W., Siddique, Z. and Jiao, J. (Eds.), Product platform and product family design: Methods and applications, Springer, New York, London, pp. 1–15.CrossRefGoogle Scholar
Simpson, T.W., Siddique, Z. and Jiao, J. (Eds.) (2006b), Product platform and product family design: Methods and applications, Springer, New York, London.CrossRefGoogle Scholar
Wang, W. (2011), “Scalable platform design optimization using hybrid co-evolutionary algorithms”, in Staff, I. (Ed.), 2011 IEEE 2nd International Conference on Software Engineering and Service Science, 7/15/2011 - 7/17/2011, Beijing, China, IEEE, [Place of publication not identified], pp. 270–273.CrossRefGoogle Scholar
Wei, W., Tian, Z., Peng, C., Liu, A. and Zhang, Z. (2019), “Product family flexibility design method based on hybrid adaptive ant colony algorithm”, Soft Computing, Vol. 23 No. 20, pp. 10509–10520.CrossRefGoogle Scholar