Vol 19 No 4 (2025): BAREKENG: Journal of Mathematics and Its Application
Articles

THE LINEARITY OF THE EXPECTED VALUE OF A FUZZY VARIABLE

Uvi Dwian Kencono
Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Gadjah Mada, Indonesia
Indarsih - Indarsih
Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Gadjah Mada, Indonesia
Published September 1, 2025
Keywords
  • Credibility,
  • Expected Value,
  • Fuzzy Variable,
  • Membership Function
How to Cite
[1]
U. D. Kencono and I. Indarsih, “THE LINEARITY OF THE EXPECTED VALUE OF A FUZZY VARIABLE”, BAREKENG: J. Math. & App., vol. 19, no. 4, pp. 2621-2632, Sep. 2025.

Abstract

In this research, we introduce a novel credibility measure defined as a non-empty set satisfying the axioms of normality, monotonicity, self-duality, and maximality. Based on this credibility measure, a credibility space is constructed, upon which a fuzzy variable can be defined. Similar to fuzzy numbers, fuzzy variables are characterized by membership functions. The membership function of this fuzzy variable is directly derived from the credibility measure. Subsequently, by integrating the credibility measure, the expected value of the fuzzy variable is obtained. The linearity property of fuzzy expected value on fuzzy variables will be proven. This linearity property is highly useful in solving various problems involving fuzzy variables. Therefore, the proposed credibility measure provides a new framework in fuzzy variable theory. This credibility measure not only offers a more formal approach to measuring uncertainty but also opens up possibilities for the development of more complex and applicable fuzzy models.

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References

  1. L. A. Zadeh, “FUZZY SETS,” Inf. Control, vol. 8, pp. 338–353, 1965, doi: https://doi.org/10.1016/S0019-9958(65)90241-X
  2. L. A. Zadeh, C. S. Division, and C. Sciences, “POSSIBILITY THEORY VS PROBABILITY THEORY IN DECISION ANALYSIS,” IEEE Xplore, vol. 94720, no. 4, pp. 4–6, 1978.
  3. S. Nahmias, “FUZZY VARIABLES,” Fuzzy Sets Syst., vol. 1, pp. 97–110, 1978.doi: https://doi.org/10.1016/0165-0114(78)90011-8
  4. H. J. Zimmermann, FUZZY SET THEORY AND ITS APPLICATIONS, SECOND, REVISED EDITION. New York: Springer Science Business Media New York, 1991.
  5. B. Liu and K. Iwamura, “CHANCE CONSTRAINED PROGRAMMING WITH FUZZY PARAMETERS,” Fuzzy Sets Syst., vol. 94, pp. 227–237, 1998.doi: https://doi.org/10.1016/S0165-0114(96)00236-9
  6. P. F. Halsey Royden, REAL ANALYSIS (4TH EDITION). China: Pearson Education Asia Limited and China Machine Press, 2010.
  7. X. Li and B. Liu, “A SUFFICIENT AND NECESSARY CONDITION FOR CREDIBILITY MEASURES,” Int. J. Uncertain., vol. 14, no. 5, pp. 527–535, 2006.doi: https://doi.org/10.1142/S0218488506004175
  8. B. Liu, THEORY AND PRACTICE OF UNCERTAIN PROGRAMMING, Second Edi. Moscow: Springer-Verlag Company, 2008.
  9. B. Liu, “A SURVEY OF CREDIBILITY THEORY,” Fuzzy Optim. Decis. Mak., vol. 5, no. 4, pp. 387–408, 2006, doi: https://doi.org/10.1007/s10700-006-0016-x.
  10. X. Li and B. Liu, “NEW INDEPENDENCE DEFINITION OF FUZZY RANDOM VARIABLE AND RANDOM FUZZY VARIABLE,” World J. Model. Simul., vol. 2, no. 5, pp. 338-342 New, 2006.
  11. B. Liu, S. Member, and Y. Liu, “EXPECTED VALUE OF FUZZY VARIABLE AND FUZZY EXPECTED VALUE MODELS,” IEEE Trans. Fuzzy Syst., vol. 10, no. 4, pp. 445–450, 2002.doi: https://doi.org/10.1109/TFUZZ.2002.800692
  12. B. Liu, UNCERTAINTY THEORY, Second Edi. Springer, 2005.
  13. F. Xue, W. Tang, and R. Zhao, “THE EXPECTED VALUE OF A FUNCTION OF A FUZZY VARIABLE WITH A CONTINUOUS MEMBERSHIP FUNCTION,” Comput. Math. with Appl., vol. 55, pp. 1215–1224, 2008.doi: https://doi.org/10.1016/j.camwa.2007.04.042
  14. B. Liu, UNCERTAINTY THEORY (THIRD EDITION). New York: Springer, 2009.
  15. E. Susanti et al., “OPTIMIZATION OF RICE INVENTORY USING FUZZY INVENTORY MODEL AND LAGRANGE INTERPOLATION METHOD,” BAREKENG J. Ilmu Mat. and Terap., vol. 17, no. 3, pp. 1215–1220, 2023, doi: 1 https://doi.org/10.30598/barekengvol17iss3pp1215-1220.
  16. E. F. Ma’rif and A. M. Abadi, “FUZZY APPLICATION (MAMANDI METHOD) IN DECISION-MAKING ON LED TV SELECTION,” BAREKENG J. Ilmu Mat. and Terap., vol. 18, no. 2, pp. 1117–1128, 2024, doi: https://doi.org/10.30598/barekengvol18iss2pp1117-1128
  17. Indarsih, “A SINGLE ITEM PRODUCTION INVENTORY MODEL WITH THE FUZZY DEMAND,” IAENG Int. J. Appl. Math., vol. 54, no. 8, pp. 1673–1677, 2024.
  18. G. Beliakov, “KNAPSACK PROBLEMS WITH DEPENDENCIES THROUGH NON-ADDITIVE MEASURES AND CHOQUET INTEGRAL,” Eur. J. Oper. Res., vol. 301, no. 1, pp. 277–286, 2022.doi: https://doi.org/10.1016/j.ejor.2021.11.004
  19. H. Doukas and A. Nikas, “DECISION SUPPORT MODELS IN CLIMATE POLICY,” Eur. J. Oper. Res., vol. 280, no. 1, pp. 1–24, 2020.doi: https://doi.org/10.1016/j.ejor.2019.01.017
  20. S. Y. Chi and L. H. Chien, “WHY DEFUZZIFICATION MATTERS: AN EMPIRICAL STUDY OF FRESH FRUIT SUPPLY CHAIN MANAGEMENT,” Eur. J. Oper. Res., vol. 311, no. 2, pp. 648–659, 2023.doi: https://doi.org/10.1016/j.ejor.2023.05.037
  21. X. Li and D. A. Ralescu, “CREDIBILITY MEASURE OF FUZZY SETS AND APPLICATIONS,” Int. J. Adv. Intell. Paradig., vol. 1, pp. 241–250, 2009.doi: https://doi.org/10.1504/IJAIP.2009.026567