Abstract
This chapter demonstrates how a neuro-fuzzy approach could produce outputs of a further-modified multi-criteria decision-making (MCDM) quality function deployment (QFD) model within the required error rate. The improved fuzzified MCDM model uses the modified S-curve membership function (MF) as stated in an earlier chapter. The smooth and flexible logistic membership function (MF) finds out fuzziness patterns in disparate level-of-satisfaction for the integrated analytic hierarchy process (AHP-QFD model. The key objective of this chapter is to guide decision makers in finding out the best candidate-alternative robot with a higher degree of satisfaction and with a lesser degree of fuzziness.
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Abraham, A., Vasant, P., Bhattacharya, A. (2008). Neuro-Fuzzy Approximation of Multi-Criteria Decision-Making QFD Methodology. In: Kahraman, C. (eds) Fuzzy Multi-Criteria Decision Making. Springer Optimization and Its Applications, vol 16. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-76813-7_12
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DOI: https://doi.org/10.1007/978-0-387-76813-7_12
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