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The International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery

ICNC-FSKD 2019: Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery pp 895–903Cite as

The Assessment of Airline Service Performance with Dependent Evaluation Criteria by Generalized QFD and SAW Under Interval-Valued Fuzzy Environment

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1074))

Abstract

For airlines, service is regarded as an essential item in their enterprises, and thus they emphasize service performance on management. Due to varied messages’ imprecision and vagueness, the assessment of airline service performance is a fuzzy multi-criteria decision-making (FMCDM) problem for management. In FMCDM problems, classical multi-criteria decision-making (MCDM) methods, including simple additive weighting (SAW), have been extended into FMCDM methods to encompass imprecise and vague messages. The generalizations were first used in FMCDM with independent evaluation criteria, and then FMCDM could be further associated with quality function deployment (QFD) to resolve the tie of the dependent evaluation criteria. Alternative ratings and criteria weights of FMCDM were commonly presented by general (i.e., triangular or trapezoidal) fuzzy numbers. Recently, FMCDM with independent evaluation criteria under an interval-valued fuzzy environment was proposed; however, FMCDM with dependent evaluation criteria under the environment has scarcely been mentioned for high computation difficulty. Moreover, QFD has been generalized under a general fuzzy environment but not an interval-valued fuzzy environment. However, interval-valued fuzzy numbers can present more messages than triangular or trapezoidal fuzzy numbers. Additionally, the assessment of airline service performance using several criteria is not only an FMCDM problem but also a problem with dependent evaluation criteria. In this paper, we generalize QFD and SAW under an interval-valued fuzzy environment for the assessment of airline service performance with dependent evaluation criteria for obtaining more messages. By the association of QFD and SAW, the computation tie of the dependent evaluation criteria corresponding to the interval-valued fuzzy numbers is resolved and more messages are gained for FMCDM.

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References

  1. Chen, S.J., Hwang, C.L.: Fuzzy multiple attribute decision making methods and application. Lecture Notes in Economics and Mathematical Systems. Springer, New York (1992)

    Google Scholar 

  2. Hwang, C.L., Yoon, K.: Multiple Attribute Decision Making: Methods and Application. Springer, New York (1981)

    MATH  Google Scholar 

  3. Bellman, R.E., Zadeh, L.A.: Decision-making in a fuzzy environment. Manag. Sci. 17(4), 141–164 (1970)

    MathSciNet  MATH  Google Scholar 

  4. Wang, Y.J.: Applying FQFD and utility representative functions under fuzzy environment for FMCDM. Test. Eval. 44(4), 1776–1790 (2016)

    Google Scholar 

  5. Chen, C.T.: Extensions to the TOPSIS for group decision-making under fuzzy environment. Fuzzy Sets Syst. 114(1), 1–9 (2000)

    MATH  Google Scholar 

  6. Delgado, M., Verdegay, J.L., Vila, M.A.: Linguistic decision-making models. Int. J. Intell. Syst. 7(5), 479–492 (1992)

    MATH  Google Scholar 

  7. Herrera, F., Herrera-Viedma, E., Verdegay, J.L.: A model of consensus in group decision making under linguistic assessments. Fuzzy Sets Syst. 78(1), 73–87 (1996)

    MathSciNet  MATH  Google Scholar 

  8. Zadeh, L.A.: Fuzzy sets. Inf. Control 8(3), 338–353 (1965)

    MATH  Google Scholar 

  9. Liang, G.S., Wang, M.J.: A fuzzy multi-criteria decision-making method for facility site selection. Int. J. Prod. Res. 29(11), 2313–2330 (1991)

    MATH  Google Scholar 

  10. Wang, Y.J.: A fuzzy multi-criteria decision-making model based on simple additive weighting method and relative preference relation. Appl. Soft Comput. 30, 412–420 (2015)

    Google Scholar 

  11. Wang, Y.J., Kao, C.S.: A fuzzy multi-criteria group decision-making model for the financial performance evaluation of airlines. In: The 6th International Conference on Fuzzy Systems and Knowledge Discovery (IEEE FSKD 2009), Tianjin, China (2009)

    Google Scholar 

  12. Chan, L.K., Wu, M.L.: Quality function deployment: a literature review. Eur. J. Oper. Res. 143(3), 463–497 (2002)

    MATH  Google Scholar 

  13. Lowe, A., Ridgway, K., Atkinson, H.: QFD in new production technology evaluation. Int. J. Prod. Econ. 67(2), 103–112 (2000)

    Google Scholar 

  14. Matook, S., Indulska, M.: Improving the quality of process reference models: a quality function deployment-based approach. Decis. Support Syst. 47(1), 60–71 (2009)

    Google Scholar 

  15. Partovi, F.Y., Corredoira, R.A.: Quality function deployment for the good of soccer. Eur. J. Oper. Res. 137(3), 642–656 (2002)

    MATH  Google Scholar 

  16. Wang, Y.J.: A criteria weighting approach by combining fuzzy quality function deployment with relative preference relation. Appl. Soft Comput. 14, 419–430 (2014)

    Google Scholar 

  17. Liang, G.S.: Applying fuzzy quality function deployment to identify service management requirements. Qual. Quan. 44(1), 47–57 (2010)

    MathSciNet  Google Scholar 

  18. Joshi, D., Kumar, S.: Interval-valued intuitionistic hesitant fuzzy Choquet integral based TOPSIS method for multi-criteria group decision making. Eur. J. Oper. Res. 248(1), 183–191 (2016)

    MathSciNet  MATH  Google Scholar 

  19. Mokhtarian, M.N.: A note on “Extension of fuzzy TOPSIS method based on interval-valued fuzzy sets”. Appl. Soft Comput. 26, 513–514 (2015)

    Google Scholar 

  20. Figueroa-García, J.C., Mehra, A., Chandra, S.: Optimal solutions for group matrix games involving interval-valued Fuzzy Numbers. Fuzzy Sets Syst. 362(1), 55–70 (2019)

    MathSciNet  MATH  Google Scholar 

  21. Lee, C.S., Chung, C.C., Lee, H.S., Gan, G.Y., Chou, M.T.: An interval-valued fuzzy number approach for supplier selection. Marine Sci. Technol. 24(3), 384–389 (2016)

    Google Scholar 

  22. Sen, S., Patra, K., Mondal, S.K.: Fuzzy risk analysis in familial breast cancer using a similarity measure of interval-valued fuzzy numbers. Pac. Sci. Rev. A: Nat. Sci. Eng. 18(3), 203–221 (2016)

    Google Scholar 

  23. Churchman, C.W., Ackoff, R.J., Amoff, E.L.: Introduction to Operation Research. Wiley, New York (1957)

    Google Scholar 

  24. Liang, G.S.: Fuzzy MCDM based on ideal and anti-ideal concepts. Eur. J. Oper. Res. 112(3), 682–691 (1999)

    MATH  Google Scholar 

  25. Raj, P.A., Kumar, D.N.: Ranking alternatives with fuzzy weights using maximizing set and minimizing set. Fuzzy Sets Syst. 105(3), 365–375 (1999)

    MATH  Google Scholar 

  26. Gorzalczany, M.B.: A method of inference in approximate reasoning based on interval-valued fuzzy sets. Fuzzy Sets Syst. 21(1), 1–17 (1987)

    MathSciNet  MATH  Google Scholar 

  27. Yao, J.S., Lin, F.T.: Constructing a fuzzy flow-shop sequencing model based on statistical data. Int. J. Approximate Reason. 29(3), 215–234 (2002)

    MathSciNet  MATH  Google Scholar 

  28. Lee, H.S.: A fuzzy multi-criteria decision making model for the selection of the distribution center. Lecture Notes in Artificial Intelligence, vol. 3612, pp. 1290–1299 (2005)

    Google Scholar 

  29. Lee, H.S.: On fuzzy preference relation in group decision making. Int. J. Comput. Math. 82, 133–140 (2005)

    MathSciNet  MATH  Google Scholar 

  30. Kacprzyk, J., Fedrizzi, M., Nurmi, H.: Group decision making and consensus under fuzzy preferences and fuzzy majority. Fuzzy Sets Syst. 49(1), 21–31 (1992)

    MathSciNet  MATH  Google Scholar 

  31. Tanino, T.: Fuzzy preference in group decision making. Fuzzy Sets Syst. 12(2), 117–131 (1984)

    MathSciNet  MATH  Google Scholar 

  32. Epp, S.S.: Discrete Mathematics with Applications, Wadsworth, California (1990)

    Google Scholar 

  33. Kuo, M.S., Liang, G.S.: A soft computing method of performance evaluation with MCDM based on interval-valued fuzzy numbers. Appl. Soft Comput. 12, 476–485 (2012)

    Google Scholar 

  34. Sanayei, A., Farid Mousavi, S., Yazdankhah, A.: Group decision making process for supplier selection with VIKOR under fuzzy environment. Expert Syst. Appl. 37(1), 24–30 (2010)

    Google Scholar 

  35. Saaty, T.L.: The Analytic Hierarchy Process. McGraw-Hill, New York (1980)

    MATH  Google Scholar 

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Acknowledgements

This research work was partially supported by the Ministry of Science and Technology of the Republic of China under Grant No. MOST 106-2410-H-346-002-.

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Authors and Affiliations

  1. Department of Shipping and Transportation Management, National Penghu University of Science and Technology, Penghu, 880, Taiwan, Republic of China

    Yu-Jie Wang & Tzeu-Chen Han

  2. Dinos International Corporation, Taipei, 104, Taiwan, Republic of China

    Li-Jen Liu

Authors
  1. Yu-Jie Wang
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  2. Li-Jen Liu
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  3. Tzeu-Chen Han
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Corresponding author

Correspondence to Tzeu-Chen Han .

Editor information

Editors and Affiliations

  1. School of Computer Science and Engineering, The University of Aizu, Aizu-Wakamatsu, Fukushima, Japan

    Yong Liu

  2. School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore

    Lipo Wang

  3. Computer Science and Mathematics, University of Sao Paulo, Ribeirao Preto, Brazil

    Liang Zhao

  4. School of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, China

    Zhengtao Yu

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Wang, YJ., Liu, LJ., Han, TC. (2020). The Assessment of Airline Service Performance with Dependent Evaluation Criteria by Generalized QFD and SAW Under Interval-Valued Fuzzy Environment. In: Liu, Y., Wang, L., Zhao, L., Yu, Z. (eds) Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery. ICNC-FSKD 2019. Advances in Intelligent Systems and Computing, vol 1074. Springer, Cham. https://doi.org/10.1007/978-3-030-32456-8_96

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