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JACIII Vol.22 No.5 pp. 767-776
doi: 10.20965/jaciii.2018.p0767
(2018)

Paper:

A Three-Dimensional Fuzzy Linguistic Evaluation Model

Sirin Suprasongsin*,**, Van-Nam Huynh*, and Pisal Yenradee**

*School of Knowledge Science, Japan Advanced Institute of Science and Technology
1-1 Asahidai, Nomi, Ishikawa 923-1292, Japan

**School of Manufacturing Systems and Mechanical Engineering, Sirindhorn International Institute of Technology, Thammasat University
99 Moo 18, Km. 41 on Paholyothin Highway Khlong Luang, Pathum Thani 12120, Thailand

Received:
March 20, 2018
Accepted:
July 31, 2018
Published:
September 20, 2018
Keywords:
fuzzy linguistic term set, probability, multi-criteria group decision making
Abstract

A probabilistic linguistic-based model is an effective tool to express preferences with different weights for different linguistic terms. This paper aims at introducing a new model for determining criteria weights in group decision-making problems, which is based on the concept of probabilistic linguistic terms. Different linguistic weights of respondents are also incorporated into the proposed model. Fuzzy numbers are used to quantify the linguistic terms. Using this model, first, a new concept called three-dimensional fuzzy linguistic representation is proposed to serve as an extension of the existing models. Then, a normalization process, an aggregation process, and a defuzzifying process for three-dimensional fuzzy linguistic representation are investigated. Next, a model for determining criteria weights is formulated. A case study of a beverage product in Thailand is provided to demonstrate the applicability of the proposed model. Finally, the results are compared with the existing models.

Cite this article as:
S. Suprasongsin, V. Huynh, and P. Yenradee, “A Three-Dimensional Fuzzy Linguistic Evaluation Model,” J. Adv. Comput. Intell. Intell. Inform., Vol.22 No.5, pp. 767-776, 2018.
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References
  1. [1] F. Herrera, S. Alonso, F. Chiclana, and E. Herrera-Viedma, “Computing with words in decision making: foundations, trends and prospects,” Fuzzy Optimization and Decision Making, Vol.8, No.4, pp. 337-364, 2009.
  2. [2] L. A. Zadeh, “Fuzzy logic = computing with words,” IEEE trans. on Fuzzy Systems, Vol.4, No.2, pp. 103-111, 1996.
  3. [3] Y. Zhang, Z. Xu, H. Wang, and H. Liao, “Consistency-based risk assessment with probabilistic linguistic preference relation,” Applied Soft Computing, Vol.49, pp. 817-833, 2016.
  4. [4] L. A. Zadeh, “Soft computing and fuzzy logic,” IEEE Software, Vol.11, No.6, pp. 48-56, 1994.
  5. [5] L. A. Zadeh, “Fuzzy sets,” Information and Control, Vol.8, No.3, pp. 338-353, 1965.
  6. [6] F. J. Cabrerizo, J. M. Moreno, I. J. Pérez, and E. Herrera-Viedma, “Analyzing consensus approaches in fuzzy group decision making: advantages and drawbacks,” Soft Computing, Vol.14, No.5, pp. 451-463, 2010.
  7. [7] H. Tian, J. Li, F. Zhang, Y. Xu, C. Cui, Y. Deng, and S. Xiao, “Entropy Analysis on Intuitionistic Fuzzy Sets and Interval-Valued Intuitionistic Fuzzy Sets and its Applications in Mode Assessment on Open Communities,” J. Adv. Comput. Intell. Intell Inform., Vol.22, No.1, pp. 147-155, 2018.
  8. [8] T. Hasuike and H. Katagiri, “An objective formulation of membership function based on fuzzy entropy and pairwise comparison,” J. of Intelligent & Fuzzy Systems, Vol.32, No.6, pp. 4443-4452, 2017.
  9. [9] T. Takeda, Y. Sakai, S. Kobashi, K. Kuramoto, and Y. Hata, “Foot Age Estimation System from Walking Dynamics Based on Fuzzy Logic,” J. Adv. Comput. Intell. Intell. Inform., Vol.18, No.4, pp. 489-498, 2014.
  10. [10] M. Delgado, J. L. Verdegay, and M. A. Vila, “On aggregation operations of linguistic labels,” Int. J. of Intelligent Systems, Vol.8, No.3, pp. 351-370, 1993.
  11. [11] H.-B. Yan, V.-N. Huynh, and Y. Nakamori, “A probabilistic model for linguistic multi-expert decision making involving semantic overlapping,” Expert Systems with Applications, Vol.38, No.7, pp. 8901-8912, 2011.
  12. [12] F. Herrera and L. Martínez, “A 2-tuple fuzzy linguistic representation model for computing with words,” IEEE Trans. on Fuzzy Systems, Vol.8, No.6, pp. 746-752, 2000.
  13. [13] L. Martínez and F. Herrera, “An overview on the 2-tuple linguistic model for computing with words in decision making: Extensions, applications and challenges,” Information Sciences, Vol.207, pp. 1-18, 2012.
  14. [14] V.-N. Huynh, C. H. Nguyen, and Y. Nakamori, “MEDM in general multi-granular hierarchical linguistic contexts based on the 2-tuples linguistic model,” 2005 IEEE Int. Conf. on Granular Computing, Vol.2, pp. 482-487, 2005.
  15. [15] G. Wei and X. Zhao, “Some dependent aggregation operators with 2-tuple linguistic information and their application to multiple attribute group decision making,” Expert Systems with Applications, Vol.39, No.5, pp. 5881-5886, 2012.
  16. [16] Q. Pang, H. Wang, and Z. Xu, “Probabilistic linguistic term sets in multi-attribute group decision making,” Information Sciences, Vol.369, pp. 128-143, 2016.
  17. [17] M. Lin, Z. Xu, Y. Zhai, and Z. Yao, “Multi-attribute group decision-making under probabilistic uncertain linguistic environment,” J. of the Operational Research Society, pp. 1-15, 2017.
  18. [18] P. Liu and X. You, “Probabilistic linguistic TODIM approach for multiple attribute decision-making,” Granular Computing, Vol.2, No.4, pp. 333-342, 2017.
  19. [19] Y. Zhai, Z. Xu, and H. Liao, “Probabilistic linguistic vector-term set and its application in group decision making with multi-granular linguistic information,” Applied Soft Computing, Vol.49, pp. 801-816, 2016.
  20. [20] X. Zhang and X. Xing, “Probabilistic Linguistic VIKOR Method to Evaluate Green Supply Chain Initiatives,” Sustainability, Vol.9, No.7, 1231, 2017.
  21. [21] J. M. Merigó and G. Wei, “Probabilistic aggregation operators and their application in uncertain multi-person decision-making,” Technological and Economic Development of Economy, Vol.17, No.2, pp. 335-351, 2011.
  22. [22] F. Herrera and L. Martínez, “A model based on linguistic 2-tuples for dealing with multigranular hierarchical linguistic contexts in multi-expert decision-making,” IEEE Trans. on Systems, Man, and Cybernetics, Part B (Cybernetics), Vol.31, No.2, pp. 227-234, 2001.
  23. [23] L. A. Zadeh, “The concept of a linguistic variable and its application to approximate reasoning – I,” Information Sciences, Vol.8, No.3, pp. 199-249, 1975.
  24. [24] S.-H. Chen, “Operations on fuzzy numbers with function principal,” J. of Management Science, Vol.6, No.1, pp. 13-26, 1985.
  25. [25] S. Suprasongsin, V.-N. Huynh, and P. Yenradee, “An Alternative Fuzzy Linguistic Approach for Determining Criteria Weights and Segmenting Consumers for New Product Development: A Case Study,” Int. Symp. on Knowledge and Systems Sciences, pp. 23-37, 2017.
  26. [26] T. J. Ross, “Fuzzy logic with engineering applications,” John Wiley & Sons, 2009.
  27. [27] C. H. Hsieh, “Optimization of fuzzy production inventory models,” Information Sciences, Vol.146, No.1-4, pp. 29-40, 2002.
  28. [28] S.-H. Chen and C. H. Hsieh, “Graded mean representation of generalized fuzzy numbers,” Proc. of 6th Conf. on Fuzzy Theory and its Applications, pp. 1-5, 1998.
  29. [29] S. H. Chen and S. M. Chang, “Optimization of fuzzy production inventory model with unrepairable defective products,” Int. J. of Production Economics, Vol.113, No.2, pp. 887-894, 2008.
  30. [30] S. Suprasongsin, V.-N. Huynh, and P. Yenradee, “Optimization of supplier selection and order allocation under fuzzy demand in fuzzy lead time,” Int. Symp. on Knowledge and Systems Sciences, pp. 182-195, 2016.
  31. [31] S. K. Babu, R. Anand B., M. Reddy K., M. V. Ramanaiah, and Karthikeyan K, “Statistical optimization for generalised fuzzy number,” Int. J. of Modern Engineering Research, Vol.3, No.2, pp. 647-651, 2013.

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Last updated on Dec. 06, 2024