Skip to main content

A Novel MGDM Method Based on Information Granularity under Linguistic Setting

  • Conference paper
  • 1196 Accesses

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8170))

Abstract

The aim of this paper is to investigate the multiple attribute group decision making(MGDM) problems under linguistic information, in which attribute weights and the expert weights are completely unknown, and the attribute values take the form of linguistic variables. Firstly, an objective method based on information granularity and entropy is proposed for acquiring attribute weights. The expert weights by use of attribute weights and the relative entropy are obtained. Secondly, we utilize the numerical weighting linguistic average operator to aggregate the linguistic variables corresponding to each alternative, and rank the alternatives according to the linguistic information. Finally, an illustrative example is given to verify practicality and effectiveness of the developed approach.

This is a preview of subscription content, log in via an institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Pawlak, Z., Slowinski, R.: Rough set approach to multi-attribute decision analysis. European Journal of Operational Research 72, 443–459 (1994)

    Article  MATH  Google Scholar 

  2. Kacprzyk, J.: Group decision making with a fuzzy linguistic majority. Fuzzy Sets and Systems 18, 105–118 (1986)

    Article  MathSciNet  MATH  Google Scholar 

  3. Parreiras, R.O., Ekel, P.Y., et al.: A flexible consensus scheme for multicriteria group decision making under linguistic assessments. Information Sciences 180, 1075–1089 (2010)

    Article  Google Scholar 

  4. Bordogna, G., Fedrizzi, M., Passi, G.: A linguistic modelling of consensus in group decision making based on OWA operators. IEEE Transactions on Systems, Man and Cybernetics 27, 126–132 (1997)

    Article  Google Scholar 

  5. Cabrerizo, F.J., Pérez, I.J., Herrera-Viedma, E.: Managing the consensus in group decision making in an unbalanced fuzzy linguistic context with incomplete information. Knowledge-Based Systems 23, 169–181 (2010)

    Article  Google Scholar 

  6. Wei, G.W.: A method for multiple attribute group decision making based on the ET-WG and ET-OWG operators with 2-tuple linguistic information. Expert Systems with Applications 37, 7895–7900 (2010)

    Article  Google Scholar 

  7. Xu, Z.S.: Deviation Measures of Linguistic Preference Relations in Group Decision Making. Omega-Int. J. Manage. S. 33, 249–254 (2005)

    Article  Google Scholar 

  8. Wu, Z.B., Chen, Y.H.: The maximization deviation method for multiple attribute group decision making under linguistic environment. Fuzzy Sets and Systems 158, 1608–1617 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  9. Boran, F.E., Genc, S., et al.: A multi-criteria intuitionistic fuzzy group decision making for supplier selection with TOPSIS method. Expert Systems with Applications 36, 11363–11368 (2009)

    Article  Google Scholar 

  10. Xu, Z.S.: A note on linguistic hybrid arithmetic averaging operator in multiple attribute group decision making with linguistic information. Group Decision and Negotiation 15, 593–604 (2006)

    Article  Google Scholar 

  11. Xu, Z.S.: A multi-attribute group decision making method based on term indices in linguistic evaluation scales. Journal of System Engineering 20, 84–88 (2005)

    Google Scholar 

  12. Liu, P.D.: A weighted aggregation operators multi-attribute group decision making method based on interval-valued trapezoidal fuzzy numbers. Expert Systems with Applications 38, 1053–1060 (2011)

    Article  Google Scholar 

  13. Li, D.F.: Compromise ratio method for fuzzy multi-attribute group decision making. Applied Soft Computing 7, 807–817 (2007)

    Article  Google Scholar 

  14. Fan, Z.P., Liu, Y.: A method for group decision-making based on multi-granularity uncertain linguistic information. Expert Systems with Applications 37, 4000–4008 (2010)

    Article  Google Scholar 

  15. Pang, J.F., Liang, J.Y.: Evaluation of the results of multi-attribute group decision-making with linguistic information. Omega-Int. J. Science. S. 40, 294–301 (2012)

    Article  Google Scholar 

  16. Herrera-Viedma, E., Mata, F.S., Martínez, L., Chiclana, F., Pérez, L.G.: Measurements of Consensus in Multi-granular Linguistic Group Decision-Making. In: Torra, V., Narukawa, Y. (eds.) MDAI 2004. LNCS (LNAI), vol. 3131, pp. 194–204. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  17. de Andr\(\acute{i}\)s, R., Garc\(\acute{i}\)a-Lapresta, J.L., Mart\(\acute{i}\)nez, L.: A Multi-granular Linguistic Model for Management Decision-making in Performance Appraisal. Soft Comput. 14, 21–34 (2010)

    Google Scholar 

  18. Xu, Z.S.: A method for multiple attribute decision making with incomplete weight information in linguistic setting. Knowledge-Based Systems 20, 719–725 (2007)

    Article  Google Scholar 

  19. Guiasu, S.: Information theory with application. McGraw-Hill, New York (1977)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Fu, Y., Xu, D., Mao, J. (2013). A Novel MGDM Method Based on Information Granularity under Linguistic Setting. In: Ciucci, D., Inuiguchi, M., Yao, Y., Ślęzak, D., Wang, G. (eds) Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing. RSFDGrC 2013. Lecture Notes in Computer Science(), vol 8170. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41218-9_28

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-41218-9_28

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-41217-2

  • Online ISBN: 978-3-642-41218-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics