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Simulation-based conjoint ranking for optimal decision support process under aleatory uncertainty

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Abstract

Design chain management requires many decision makers throughout the product development process. It is critical to reduce complexity and uncertainty of the design process by correctly modeling subjective data associated with decision makers’ preferences. This paper aims at using decision support to find optimal designs by modeling respondent preferences and trade-offs with consideration of uncertainty. Specifically, a simulation-based ranking methodology is implemented and incorporated with traditional conjoint analysis. This process facilitates a schematic decision support process by alleviating user fatigue. In addition, incorporation of uncertainty in the ranking process provides the capability of producing robust and reliable products. The efficacy and applicability of simulation-based conjoint ranking is demonstrated with a case study of a power-generating shock absorber design.

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Authors and Affiliations

  1. G.W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA, USA

    Alex M. Ruderman, Seung-Kyum Choi & Roger J. Jiao

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  1. Alex M. Ruderman
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  2. Seung-Kyum Choi
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  3. Roger J. Jiao
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Correspondence to Seung-Kyum Choi.

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Ruderman, A.M., Choi, SK. & Jiao, R.J. Simulation-based conjoint ranking for optimal decision support process under aleatory uncertainty. J Intell Manuf 24, 641–652 (2013). https://doi.org/10.1007/s10845-011-0610-9

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  • DOI: https://doi.org/10.1007/s10845-011-0610-9

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