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A Bayesian approach for multiple criteria decision making with applications in Design for Six Sigma

Author

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  • R Rajagopal

    (The Pennsylvania State University)

  • E del Castillo

    (The Pennsylvania State University)

Abstract

Linking end-customer preferences with variables controlled at a manufacturing plant is a main idea behind popular Design for Six Sigma management techniques. Multiple criteria decision making (MCDM) approaches can be used for such purposes, but in these techniques the decision-maker's (DM) utility function, if modelled explicitly, is considered known with certainty once assessed. Here, a new algorithm is proposed to solve a MCDM problem with applications to Design for Six Sigma based on a Bayesian methodology. At a first stage, it is assumed that there are process responses that are functions of certain controllable factors or regressors. This relation is modelled based on experimental data. At a second stage, the utility function of one or more DMs or customers is described in a statistical model as a function of the process responses, based on surveys. This step considers the uncertainty in the utility function(s) explicitly. The methodology presented then maximizes the probability that the DM's or customer's utility is greater than some given lower bound with respect to the controllable factors of the first stage. Both stages are modelled with Bayesian regression techniques. The advantages of using the Bayesian approach as opposed to traditional methods are highlighted.

Suggested Citation

  • R Rajagopal & E del Castillo, 2007. "A Bayesian approach for multiple criteria decision making with applications in Design for Six Sigma," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 58(6), pages 779-790, June.
    • Handle: RePEc:pal:jorsoc:v:58:y:2007:i:6:d:10.1057_palgrave.jors.2602184
    DOI: 10.1057/palgrave.jors.2602184
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    References listed on IDEAS

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    1. Guillermo Miro-Quesada & Enrique Del Castillo & John Peterson, 2004. "A Bayesian Approach for Multiple Response Surface Optimization in the Presence of Noise Variables," Journal of Applied Statistics, Taylor & Francis Journals, vol. 31(3), pages 251-270.
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    Cited by:

    1. R J Ormerod, 2010. "Rational inference: deductive, inductive and probabilistic thinking," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 61(8), pages 1207-1223, August.
    2. Yu-Sheng Kao & Kazumitsu Nawata & Chi-Yo Huang, 2019. "Evaluating the Performance of Systemic Innovation Problems of the IoT in Manufacturing Industries by Novel MCDM Methods," Sustainability, MDPI, vol. 11(18), pages 1-33, September.

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