Advertisement

Building Multi-Attribute Decision Model Based on Kansei Information in Environment with Hybrid Uncertainty

  • Junzo Watada
  • Nureize Arbaiy
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 10)

Abstract

The objective of this paper is to build multi attribute decision model considering Kansei information in hybrid uncertain environment. First, fuzzy random variable is explained to deal with the models in hybrid uncertain environment. Second, using fuzzy random variables, linear regression model (FRRM) is formulated. Third, multi-attribute decision model (MADM) is built based on linear regression model. Finally, multi-attribute decision model is presented in presence of Kansei information given by experts in an environment with hybrid uncertainty involving both randomness and fuzziness.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Gil, M.A., Miguel, L.D., Ralescu, D.A.: Overview on the development of fuzzy random variables. Fuzzy sets and systems 157(19), 2546–2557 (2006)MathSciNetzbMATHCrossRefGoogle Scholar
  2. 2.
    Kwakernaak, H.: Fuzzy random variables–I. Definitions and theorems. Information Sciences 15(1), 1–29 (1978)MathSciNetzbMATHCrossRefGoogle Scholar
  3. 3.
    Kwakernaak, H.: Fuzzy random variables–II. Algorithm and examples. Information Sciences 17(3), 253–278 (1979)MathSciNetzbMATHCrossRefGoogle Scholar
  4. 4.
    Liu, Y.K., Liu, B.: Fuzzy random variable: A scalar expected value operator. Fuzzy Optimization and Decision Making 2(2), 143–160 (2003)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Liu, B., Liu, Y.K.: Expected value of fuzzy variable and fuzzy expected value models. IEEE Transaction on Fuzzy Systems 10(4), 445–450 (2002)CrossRefGoogle Scholar
  6. 6.
    Nahmias, S.: Fuzzy variable. Fuzzy Sets and Systems 1(2), 97–101 (1978)MathSciNetzbMATHCrossRefGoogle Scholar
  7. 7.
    Nureize, A., Watada, J.: A fuzzy regression approach to hierarchical evaluation model for oil palm grading. Fuzzy Optimization Decision Making 9(1), 105–122 (2010)zbMATHCrossRefGoogle Scholar
  8. 8.
    Arbaiy, N., Watada, J.: Approximation of goal constraint coefficients in fuzzy goal programming. In: Second International Conference on Computer Engineering and Applications (ICCEA 2010), vol. 1, pp. 161–165 (2010)Google Scholar
  9. 9.
    Nureize, A., Watada, J.: Building fuzzy random objective function for interval fuzzy goal programming. In: IEEE International Conference on Industrial Engineering and Engineering Management (IEEM 2010), pp. 980–984 (2010)Google Scholar
  10. 10.
    Tanaka, H., Uejima, S., Asai, K.: Linear regression analysis with fuzzy model. IEEE Transactions on Systems, Man and Cybernetics (SMC) 12(6), 903–907 (1982)zbMATHCrossRefGoogle Scholar
  11. 11.
    Tanaka, H., Shimomura, T., Watada, J., Asai, K.: Fuzzy linear regression analysis of the number of staff in local goverment. In: Proceedings of FIP 1984, Kauai, Hawaii, July 22-26 (1984)Google Scholar
  12. 12.
    Wang, S., Liu, Y.-K., Watada, J.: Fuzzy random renewal process with queueing applications. Computers & Mathematics with Applications 57(7), 1232–1248 (2009)MathSciNetzbMATHCrossRefGoogle Scholar
  13. 13.
    Wang, S., Watada, J.: Reliability optimization of a series-parallel system with fuzzy random lifetimes. International Journal of Innovative Computing. Information & Control 5(6), 1547–1558 (2009)Google Scholar
  14. 14.
    Wang, S., Watada, J.: Studying distribution functions of fuzzy random variables and its applications to critical value functions. International Journal of Innovative Computing, Information & Control 5(2), 279–292 (2009)Google Scholar
  15. 15.
    Wang, S., Watada, J.: T-independence condition for fuzzy random vector based on continuous triangular norms. Journal of Uncertain Systems 2(2), 155–160 (2008)Google Scholar
  16. 16.
    Watada, J., Tanaka, H., Asai, K.: Analysis of time-series data by posibilistic model. In: Proceedings of International Workshop on Fuzzy System Applications, Fukuoka, pp. 228–233 (1988)Google Scholar
  17. 17.
    Watada, J., Pedrycz, W.: A fuzzy regression approach to acquisition of linguistic rules. In: Pedrycz, W., Skowron, A., Kreinovich, V. (eds.) Handbook on Granular Comutation,  ch. 32, pp. 719–740. John Wiley & Sons, Chichester (2008)CrossRefGoogle Scholar
  18. 18.
    Watada, J., Wang, S.: Regression model based on fuzzy random variables. In: Rodulf, S. (ed.) Views on Fuzzy Sets and Systems from Different Perspectives, vol. ch. 26, Springer, Berlin (2009)Google Scholar
  19. 19.
    Watada, J., Wang, S., Pedrycz, W.: Building confidence-interval-based fuzzy random regression model. IEEE Transactions on Fuzzy Systems 11(6), 1273–1283 (2009)CrossRefGoogle Scholar
  20. 20.
    Zadeh, L.A.: Toward a generalized theory of uncertainty (GTU) - an outline. Inforamation Science 172(1-2), 1–40 (2005)MathSciNetzbMATHCrossRefGoogle Scholar
  21. 21.
    Zadeh, L.A.: Generalized theory of uncertainty (GTU)- principal concepts and ideas. Computational Statistics and Data Analysis 51(1), 15–46 (2006)MathSciNetzbMATHCrossRefGoogle Scholar
  22. 22.
    Zhao, R.Q., Tang, W.S.: Some properties of fuzzy random renewal processes. IEEE Transactions on Fuzzy Systems 14(2), 173–179 (2006)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Junzo Watada
    • 1
  • Nureize Arbaiy
    • 1
  1. 1.Graduate School of Informtion, Production and SystemWaseda UniversityKitakyushuJapan

Personalised recommendations