10.71932/ijm.2024.1081317

Evaluating MBTs Using Fuzzy Measure and Fuzzy Integral

  1. Department of Mathematics, Imam Hossein University, Tehran, Iran.

Received: 31-07-2024

Accepted: 30-11-2024

Published in Issue 04-12-2024

How to Cite

Sarabadan, S. (2024). Evaluating MBTs Using Fuzzy Measure and Fuzzy Integral. International Journal of Mathematical Modelling & Computations, 14(4), 335-345. https://doi.org/10.71932/ijm.2024.1081317

Abstract

This paper presents an evaluation model based on the fuzzy analytic hierarchy process and fuzzy integral where the vagueness and subjectivity are handled with linguistic values parameterized by trapezoidal fuzzy numbers. We adopt fuzzy measure and fuzzy integral, one of the multiple attribute decision-making approaches, to rank the evaluated objects. Evaluating MBTs is a multi-criteria decision-making (MCDM) problem. The performance of 29 MBTs were evaluated and ranked to serve as a case study to illustrate the procedure and effectiveness of the proposed approach.

Keywords

  • Fuzzy measure,
  • Choquet fuzzy integral,
  • Fuzzy Analytic hierarchy process,
  • MCDM

References

  1. L. Abdullah, N. Azzah Awang, M. Othman, Application of Choquet Integral-Fuzzy Measures for Aggregating
  2. Customers’ Satisfaction, Commun. Advances in Fuzzy Systems., 2 (2021).
  3. F. Ahmed, K. Kilic, Fuzzy Analytic Hierarchy Process: A performance analysis of various algorithms, Fuzzy sets and system. Appl., 369 (2019) 110-128.
  4. C.H. Cheng, Y. Lin, Evaluating the best main battle tank using fuzzy decision theory with linguistic criteria
  5. evaluation, Eur. J. Oper. Res., 142 (2002) 174-186.
  6. C.H. Cheng, M.L. Mon, Evaluating weapon system by analytic hierarchy process based on fuzzy scales,
  7. FuzzySets Syst., 63 (1994) 1-10.
  8. A. Flores-Franuli_c, H. Rom_an-Flores, A Chebyshev type inequality for fuzzy integrals, Appl. Math. Comput.
  9. (2007) 1178-1184.
  10. I. Emovon , T.T. Ow, Application of MCDM method in material selection for optimal design: A review,
  11. , Results in material., 7 (2020) 100-115.
  12. H. Liu, X. Wang, A. Kadir, Color image encryption using Choquet fuzzy integral and hyper chaotic systemOptik,
  13. (2013) 3527-3533.
  14. W.-S. Lee, Evaluating and ranking energy performance of office buildings using fuzzy measure and fuzzy
  15. integral, Energ. Conver. Manag., 51 (2010) 197-203.
  16. Z. Liao, H. Liao, M. Tang, A. Al-Barakati, and C. Llopis-Albert, “A Choquet integral-based hesitant fuzzy gained
  17. and lost dominance score method for multi-criteria group decision making considering the risk preferences of
  18. experts: casestudy of higher business education evaluation,” Information Fusion, 62,( 2020). 121–133.
  19. I. Mergias, K. Moustakas, A. Papadopoulos, M. Loizidou, Multi-criteria decision aid approach for the selection
  20. of the best compromise management scheme for ELVs: The case of Cyprus, J. Hazard. Mater.,47 (2007).
  21. Y. Liu, W. Jiang, A new distance measure of interval-valued intuitionistic fuzzy sets and its application in
  22. decision making, Methodologies and Application. 24 (2020) 6987–7003.
  23. H. Garg, K. Kumar, Distance measures for connection number sets based on set pair analysis and its
  24. applications to decision-making process, Applied Intelligence. Appl. 48 (2018) 3346–3359.
  25. D. Ralescu, G. Adams, The fuzzy integral, J. Math. Anal. Appl. 75 (1980) 562-570.
  26. H. Rom_an-Flores, Y. Chalco-Cano, H-continuity of fuzzy measures and set defuzzifincation, Fuzzy Sets Syst.
  27. (2006) 230-242.
  28. H. Rom_an-Flores, Y. Chalco-Cano, Sugeno integral and geometric inequalities, Int. J. Uncertain. Fuzz.
  29. Knowledge-Based Syst. 15 (2007) 1-11. 124 (2013) 3527-3533.
  30. H. Rom_an-Flores, A. Flores-Franuli_c, Y. Chalco-Cano, A Jensen type inequality for fuzzy integrals, Inform.
  31. Sci-ences 177 (2007) 3192-3201.
  32. H. Rom_an-Flores, A. Flores-Franuli_c, Y. Chalco-Cano, The fuzzy integral for monotone functions, Appl.
  33. Math. Comput. 185 (2007) 492-498.
  34. T.L. Saaty, Decision making with dependence and feedback: the analytic network process, Pittsburgh: RWS
  35. Publications; 1996.
  36. T.L. Saaty, The analytic hierarchy process, McGraw-Hill, New York, 1980.
  37. S.M. Seyedzadeh, B. Norouzi, S. Mirzakuchaki, RGB color image encryption based on Choquet fuzzy integral,
  38. J.Syst. Softw. 97 (2014) 128-139.
  39. P. Soda, G. Iannello, Aggregation of classifiers for staining pattern recognition in antinuclear autoantibodies
  40. analysis, IEEE Trans. Inf. Technol. Biomed. 13 (2009) 322-329.
  41. A. Soria-Frisch, A new paradigm for fuzzy aggregation in multisensorial image processing, in: Computational
  42. Intelligence. Theory and Applications, Springer, 2001, pp. 59-67.
  43. M. Sugeno, Theory of Fuzzy Integrals and its Applications, Ph.D. Dissertation, Tokyo Institute of Technology,
  44. T.C. Wang,T.H. Chang, Application of TOPSIS in evaluating initial training aircraft under a fuzzy environment,
  45. Expert Syst. Appl., 33 (2007) 870-880.
  46. Z. Wang, G. Klir, Fuzzy Measure Theory, Plenum, New York, 1992.
  47. R.R. Yager, A procedure for ordering fuzzy subsets of the unit interval, Inform. Sci., 24 (1981) 143-61.
  48. D. Yong, S. Cheng, Evaluating the Main Battle Tank Using Fuzzy Number Arithmetic Operations, Def. Sci. J.,
  49. (2006) 251-257.
  50. L.A. Zadeh, Fuzzy set, Inform. Control, 8 (1965) 338-353.