Evaluating the Airline Service Quality by Fuzzy OWA Operators

  • Ching-Hsue Cheng
  • Jing-Rong Chang
  • Tien-Hwa Ho
  • An-Pin Chen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3558)


The OWA (Ordered Weighted Averaging) aggregation operators have been extensively adopted to assign the relative weights of numerous criteria. However, previous aggregation operators (including OWA) are independent of aggregation situations. To solve the problem, this study proposes a new aggregation model – dynamic fuzzy OWA operators based on situation model, which can modify the associated dynamic weight based on the aggregation situation and can work like a “magnifying lens” to enlarge the most important attribute dependent on minimal information, or can obtain equal attribute weights based on maximal information. We also apply proposed model to evaluate the service quality of airline.


Analytic Hierarchy Process Aggregation Operator Order Weighted Average Attribute Weight Order Weighted Average Operator 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Beliakov, G., Warren, J.: Appropriate Choice of Aggregation Operators in Fuzzy Decision Support Systems. IEEE Transactions on Fuzzy Systems 9(6), 773–784 (2001)CrossRefGoogle Scholar
  2. 2.
    Borcherding, K., Epple, T., Winterfeldt, D.V.: Comparison of weighting judgments in multi-attribute utility measurement. Management Science 37(12), 1603–1619 (1991)zbMATHCrossRefGoogle Scholar
  3. 3.
    Carbonell, M., Mas, M., Mayor, G.: On a class of Monotonic Extended OWA Operators. In: Proceedings of the Sixth IEEE International Conference on Fuzzy Systems (IEEEFUZZ 1997) Barcelona, Catalunya, Spain, pp. 1695–1699 (1997)Google Scholar
  4. 4.
    Chen, S.M.: Fuzzy group decision making for evaluating the rate of aggregative risk in software development. Fuzzy Sets and Systems 118, 75–88 (2001)CrossRefMathSciNetGoogle Scholar
  5. 5.
    Choi, D.Y.: A new aggregation method in a fuzzy environment. Decision Support Systems 25, 39–51 (1999)CrossRefGoogle Scholar
  6. 6.
    Filev, D., Yager, R.R.: On the issue of obtaining OWA operator weights. Fuzzy Sets and Systems 94, 157–169 (1998)CrossRefMathSciNetGoogle Scholar
  7. 7.
    Fuller, R., Majlender, P.: An analytic approach for obtaining maximal entropy OWA operator weights. Fuzzy Sets and Systems 124, 53–57 (2001)zbMATHCrossRefMathSciNetGoogle Scholar
  8. 8.
    Jaynes, E.T.: Cleaning up mysteries: The original goal, Maximum Entropy and Bayesian Methods. Kluwer, Dordrecht (1989)Google Scholar
  9. 9.
    Klir, G.J.: Fuzzy Sets, Uncertainly and information. Prentice Hall, Englewood Cliffs (1988)Google Scholar
  10. 10.
    Klir, G.J., Wierman, M.J.: Uncertainty-Based Information, 2nd edn. Physica-Verlag, Germany (1999)zbMATHGoogle Scholar
  11. 11.
    Lee, H.M.: Group decision making using fuzzy sets theory for evaluating the rate of aggregative risk in software development. Fuzzy Sets and Systems 80, 261–271 (1996)CrossRefGoogle Scholar
  12. 12.
    Mendel, J.M.: Uncertain Rule-Based Fuzzy Logic Systems: Introduction and New Directions. Prentice Hall PTR, Upper Saddle River (2000)Google Scholar
  13. 13.
    Mesiar, R., Saminger, S.: Domination of ordered weighted averaging operators over tnorms. Soft Computing 8, 562–570 (2004)zbMATHCrossRefGoogle Scholar
  14. 14.
    Moshkovich, H.M., Schellenberger, R.E., Olson, D.L.: Data influences the result more than preferences: Some lessons from implementation of multiattribute techniques in a real decision task. Decision Support Systems 22, 73–84 (1998)CrossRefGoogle Scholar
  15. 15.
    O’Hagan, M.: Aggregating template or rule antecedents in real-time expert systems with fuzzy set logic. In: Proc. 22nd Annu. IEEE Asilomar Conf. On Signals, Systems, Computers, Pacific Grove, CA, pp. 681–689 (1988)Google Scholar
  16. 16.
    Ribeiro, R.A., Pereira, R.A.M.: Generalized Mixture Operators using weighting functions: A comparative study with WA and OWA. European Journal of Operational Research 145, 329–342 (2003)zbMATHCrossRefGoogle Scholar
  17. 17.
    Shannon, C.E.: A Mathematical Theory of Communication. Bell Systems Technical Journal 27, 379–423 (1948)zbMATHMathSciNetGoogle Scholar
  18. 18.
    Shoemaker, P.J.H., Carter, W.C.: An Experimental Comparison of different approaches to determining Weights in Additive Utility Models. Management Science 28, 182–196 (1982)CrossRefGoogle Scholar
  19. 19.
    Smolikova, R., Wachowiak, M.P.: Aggregation operators for selection problems. Fuzzy Sets and Systems 131, 23–34 (2002)zbMATHCrossRefMathSciNetGoogle Scholar
  20. 20.
    Solymosi, T., Dombi, J.: A method for determining the weights of criteria: the centralized weights. European Journal of Operational Research 26, 35–41 (1986)CrossRefMathSciNetGoogle Scholar
  21. 21.
    Torra, V.: Learning weights for the Quasi-Weighted Mean. IEEE Transactions on Fuzzy Systems 10(5), 653–666 (2002)CrossRefMathSciNetGoogle Scholar
  22. 22.
    Torra, V.: On the learning of weights in some aggregation operators. Mathware and Soft Computing 6, 249–265 (1999)MathSciNetGoogle Scholar
  23. 23.
    Torra, V.: OWA operators in data modeling and re-identification. IEEE Transactions on Fuzzy Systems 12(5), 652–660 (2004)CrossRefGoogle Scholar
  24. 24.
    Tsaur, S.-H., Chang, T.Y., Yen, C.-H.: The evaluation of airline service quality by fuzzy MCDM. Tourism management 23, 107–115 (2002)CrossRefGoogle Scholar
  25. 25.
    Weber, M., Eisenfhr, F., von Winterfeldt, D.: The effects of splitting attributes on weights in multiattribute utility measurement. Management Science 34, 431–445 (1988)CrossRefGoogle Scholar
  26. 26.
    Yager, R.R.: Connectives and quantifiers in fuzzy sets. Fuzzy Sets and Systems 40, 39–75 (1991)zbMATHCrossRefMathSciNetGoogle Scholar
  27. 27.
    Yager, R.R.: On a general class of fuzzy connectives. Fuzzy Sets and Systems 4, 235–242 (1980)zbMATHCrossRefMathSciNetGoogle Scholar
  28. 28.
    Yager, R.R.: Ordered weighted averaging aggregation operators in multi-criteria decision making. IEEE Trans. Systems Man. and Cybernetics 18, 183–190 (1988)zbMATHCrossRefMathSciNetGoogle Scholar
  29. 29.
    Yager, R.R., Kacprzyk, J.: The Ordered Weighted Averaging Operators. Kluwer Academic Publishers, Boston (1997)zbMATHGoogle Scholar
  30. 30.
    Zadeh, L.A.: Fuzzy Sets. Information and Control 8, 338–353 (1965)zbMATHCrossRefMathSciNetGoogle Scholar
  31. 31.
    Zhang, D., Yu, P.L., Wang, P.Z.: State-dependent weights in multicriteria value functions. Journal of Optimization Theory and Applications 74(1), 1–21 (1992)zbMATHCrossRefMathSciNetGoogle Scholar
  32. 32.
    Zimmermann, H.J., Zysno, P.: Latent connectives in human decision making. Fuzzy Sets and Systems 4, 37–51 (1980)zbMATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Ching-Hsue Cheng
    • 1
  • Jing-Rong Chang
    • 1
  • Tien-Hwa Ho
    • 2
  • An-Pin Chen
    • 2
  1. 1.Department of Information ManagementNational Yunlin University of Science and TechnologyTouliu, YunlinTaiwan
  2. 2.Graduate School of Information ManagementNational Chiao Tung UniversityHsinchuTaiwan

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