An integration of fuzzy TOPSIS and fuzzy logic for multi-criteria decision making problems

  • Authors

    • Ratih Fitria Jumarni
    • Nurnadiah Zamri
    2018-04-06
    https://doi.org/10.14419/ijet.v7i2.15.11362
  • Fuzzy logic, Fuzzy set, Fuzzy TOPSIS, Multi-criteria decision-making, .
  • Multi-Criteria Decision Making (MCDM) is a decision-making methods, which it is able to find a unique agreement from number of experts by evaluating the uncertain judgment among them. Several fuzzy logic based approaches have been employed in MCDM to handle the linguistic uncertainties and hesitancy. However, there is still a need to handle high level of uncertainties that exists in decision making problems. Thus, the purpose of this paper is to introduce the new concept namely fuzzy TOPSIS and fuzzy logic based MCDM. The proposed concepts aims to handle the high levels of uncertainties which exists due to the varying experts’ judgments and the vagueness of the appraisal. The proposed method utilized fuzzy logic rule-base in determining the alternatives and criteria for decision matrix. Then, in the aggregation phase, the min operator is used to compute the firing strength for each rule. The feasibility and applicability of the proposed methods are illustrated with an example. This new concept is seen be able to handle intangibles and less cumbersome in mathematical calculations.

     

     

  • References

    1. [1] Syibrah N, & Hani H (2005), A general type-2 fuzzy logic based approach for multi-criteria group decision making. Proceedings of the IEEE International Conference on Fuzzy Systems, pp. 353–358.

      [2] https://www.slideshare.net/nish_d1?utm_campaign=profiletracking&utm_medium=sssite&utm_source=ssslideview">nish_d1 (2013), Conflicts in decision making. https://www.slideshare.net/nish_d1/conflict-in-decision-making.

      [3] Lopez L (2005), Conflict resolution and group decision-making-exploring the dynamics of conflict resolution at the group level. Korean System Dynamics Review 6, 37–52.

      [4] Mannix E (2003), Editor's comments: Conflict and conflict resolution-A return to theorizing. Academy of Management Review 28, 543–546.

      [5] Raju KS, Duckstein L & Arondel C (2000), Multicriterion analysis for sustainable water resources planning: A case study in Spain. Water Resources Management 14, 435–456.

      [6] Xu L & Yang JB (2001), Introduction to multi-criteria decision making and the evidential reasoning approach. Working Paper No. 0106, Manchester School of Management.

      [7] Kuo MS, Liang GS & Huang WC (2006), Extensions of the multicriteria analysis with pairwise comparison under a fuzzy environment. International Journal of Approximate Reasoning 43, 268–285.

      [8] Chen CT (2000), Extensions of the TOPSIS for group decision-making under fuzzy environment. Fuzzy Sets and Systems 114, 1–9.

      [9] Ishizaka A & Nemery P (2013), Multi-criteria decision analysis: Methods and software, John Wiley and Sons.

      [10] Huang JJ (2011), Multiple attribute decision making: Methods and applications, Chapman and Hall/CRC.

      [11] Wang YM & Elhag TM (2006), Fuzzy TOPSIS method based on alpha level sets with an application to bridge risk assessment. Expert Systems with Applications 31, 309-319.

      [12] Chen SJ, Hwang CL & Hwang FP (1992), Fuzzy multiple attribute decision making (methods and applications), Springer-Verlag.

      [13] Chen MF & Tzeng GH (2004), Combining grey relation and TOPSIS concepts for selecting an expatriate host country. Mathematical and Computer Modelling 40, 1473–1490.

      [14] Wu X, Zhang C & Zhang S (2004), Efficient mining of both positive and negative association rules. ACM Transactions on Information Systems 22, 381–405.

      [15] Mendel JM (2000), Uncertainty, fuzzy logic, and signal processing. Signal Processing 80, 913–933.

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  • How to Cite

    Fitria Jumarni, R., & Zamri, N. (2018). An integration of fuzzy TOPSIS and fuzzy logic for multi-criteria decision making problems. International Journal of Engineering & Technology, 7(2.15), 102-106. https://doi.org/10.14419/ijet.v7i2.15.11362