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Product Architectures Generation Under Uncertainty: Comparison Between Two Methods

[+] Author Affiliations
Marie-Lise Moullec

Ecole Centrale Paris, Châtenay-Malabry, FranceThales Air Systems, Rungis, France

Marija Jankovic, Jean-Claude Bocquet

Ecole Centrale Paris, Châtenay-Malabry, France

Marc Bouissou

Ecole Centrale Paris, Châtenay-Malabry, FranceEDF R&D, Clamart, France

Paper No. DETC2012-70644, pp. 825-836; 12 pages
doi:10.1115/DETC2012-70644
From:
  • ASME 2012 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
  • Volume 7: 9th International Conference on Design Education; 24th International Conference on Design Theory and Methodology
  • Chicago, Illinois, USA, August 12–15, 2012
  • Conference Sponsors: Design Engineering Division, Computers and Information in Engineering Division
  • ISBN: 978-0-7918-4506-6
  • Copyright © 2012 by ASME

abstract

Complex product architecture definition involves technological and architectural choices in order to reach defined system performances. These choices form a wide combinatorial design space whose complete exploration requires a computational method. The latter is made difficult because of the lack and the fuzziness of data and knowledge in preliminary design. To introduce this type of uncertainty, we have proposed an approach based on Bayesian nets: a Bayesian net architecture generation and clustering method is proposed. However, in recent research, lots of conceptual design problems were addressed with Constraint Satisfaction Problem (CSP). The purpose of this paper is to compare these two methods and advantages and challenges in view to design situations under uncertainty. The comparison consists in modeling a sample problem with both methods. The modeling process of each method is described, providing preliminary highlights on advantages and disadvantages of both methods. Then, the methods are evaluated in terms of modeling capabilities and easiness. The number of generated architectures and the execution time of each simulation are also analyzed with regard to the influence of uncertainty introduction in the models. The final objective is to determine which method seems to be the more appropriate to help designers in finding new innovative designs.

Copyright © 2012 by ASME

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