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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References
Abraham A., 2005, Adaptation of fuzzy inference system using neural learning, fuzzy system engineering: theory and practice. In: Nedjah, N. et al. (eds.), Studies in Fuzziness and Soft Computing, pp. 53-83, Springer Verlag, Germany.
Bhattacharya, A., Sarkar, B., and Mukherjee, S.K., 2005, Integrating AHP with QFD for robot selection under requirement perspective, International Journal of Production Research, 43(17): 3671-3685.
Chuang, P.T., 2001, Combining the analytic hierarchy process and quality function deployment for a location decision from a requirement perspective, International Journal of Advanced Manufacturing Technology, 18: 842-849.
Cohen, L., 1995, Quality Function Deployment - How to make QFD Work for You, Addison - Wesley, New York.
Franceśchini, F., and Rossetto, S., 1995, QFD: the problem of comparing technical/engineering design requirements, Research Engineering Design, 7: 270-278.
Govers, C.P.M., 2001, QFD not just a tool but a way of quality management, International Journal of Production Economics, 69(2): 151-159.
Hauser, J.R., and Clausing, D., 1988, The house of quality, Harvard Business Review, May - June: 63-73.
Jang, J.S.R., 1991, ANFIS: adaptive network based fuzzy inference systems, IEEE Transactions Systems, Man & Cybernetics, 23: 665−685.
Saaty, T.L., 1994, How to make a decision: the analytic hierarchy process, Interfaces, 24(6): 19-43.
Saaty, T.L., 1990, How to make a decision: the analytic hierarchy process, European Journal of Operational Research, 48(1): 9-26.
Saaty, T.L., 1988, The Analytic Hierarchy Process, Pergamon, New York.
Saaty, T.L., and Vargas, L.G., 1987, Uncertainty and rank order in the analytic hierarchy process, European Journal of Operational Research, 32: 107-117.
Saaty, T.L., 1980, The Analytical Hierarchy Process, McGraw-Hill, New Work.
Sugeno, M., 1985, Industrial Applications of Fuzzy Control, Elsevier Science Pub Co., New York.
Sullivan, L. P., 1986, Quality function deployment, Quality Progress, 19(6): 39-50.
Wasserman, G.S., 1993, On how to prioritize design requirements during the QFD planning process, IEEE Transactions, 25(3): 59-65.
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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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