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Enhanced Adaptive Choice-Based Conjoint Analysis Incorporating Engineering Knowledge

[+] Author Affiliations
Yi Ren, Panos Y. Papalambros

University of Michigan, Ann Arbor, MI

Paper No. DETC2014-34790, pp. V02AT03A033; 9 pages
  • ASME 2014 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
  • Volume 2A: 40th Design Automation Conference
  • Buffalo, New York, USA, August 17–20, 2014
  • Conference Sponsors: Design Engineering Division, Computers and Information in Engineering Division
  • ISBN: 978-0-7918-4631-5
  • Copyright © 2014 by ASME


Conjoint analysis from marketing has been successfully integrated with engineering analysis in design for market systems. The long questionnaires needed for conjoint analysis in relatively complex design decisions can become cumbersome to the human respondents. This paper presents an adaptive questionnaire generation strategy that uses active learning and allows incorporation of engineering knowledge in order to identify efficiently designs with high probability to be optimal. The strategy is based on viewing optimal design as a group identification problem. A running example demonstrates that a good estimation of consumer preference is not always necessary for finding the optimal design and that conjoint analysis could be configured more effectively for the specific purpose of design optimization. Extending the proposed method beyond a homogeneous preference model and noiseless user responses is also discussed.

Copyright © 2014 by ASME



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