A Survey of The Centroids of Fuzzy Numbers and Applications

Authors

https://doi.org/10.48314/jidcm.v1i2.66

Abstract

This paper presents a thorough review of the various methods for determining the centroids of fuzzy numbers, highlighting their significance in fuzzy set theory and decision-making processes. Starting with the foundational concepts of fuzzy sets and fuzzy numbers, including triangular and trapezoidal forms, the study critically examines existing centroid calculation formulas, addressing their advantages and limitations. The review identifies common errors in previous approaches, emphasizing the necessity for accurate and consistent centroid formulae. Furthermore, the paper explores multiple applications of centroid-based fuzzy number ranking, notably in decision-making and Multi-Criteria Decision Making (MCDM). It demonstrates that precise centroid computation is essential for effective fuzzy number comparison and ranking, ultimately enhancing the reliability of fuzzy logic applications in engineering, management, and applied sciences.  

Keywords:

Fuzzy numbers, Fuzzy set theory, Decision-making, Multi-criteria decision making, Fuzzy logic applications

References

  1. [1] Zadeh, L. A. (1965). Fuzzy sets. Information and control, 8(3), 338–353. https://doi.org/10.1016/S0019-9958(65)90241-X

  2. [2] Atanassov, K. (1986). Intuitionistic fuzzy sets. fuzzy sets and systems. Fuzzy sets and systems, 20(1), 87–96. http://dx.doi.org/10.1016/S0165-0114(86)80034-3

  3. [3] Chu, T. C., & Lin, Y. C. (2003). A fuzzy TOPSIS method for robot selection. The international journal of advanced manufacturing technology, 21, 284–290. https://doi.org/10.1007/s001700300033

  4. [4] Chou, S. Y., & Chang, Y. H. (2008). A decision support system for supplier selection based on a strategy-aligned fuzzy SMART approach. Expert systems with applications, 34(4), 2241–2253. https://doi.org/10.1016/j.eswa.2007.03.001

  5. [5] Chou, S. Y., Chang, Y. H., & Shen, C. Y. (2008). A fuzzy simple additive weighting system under group decision-making for facility location selection with objective/subjective attributes. European journal of operational research, 189(1), 132–145. https://doi.org/10.1016/j.ejor.2007.05.006

  6. [6] Tseng, M. L. (2010). An assessment of cause and effect decision-making model for firm environmental knowledge management capacities in uncertainty. Environmental monitoring and assessment, 161, 549–564. https://doi.org/10.1007/s10661-009-0767-2

  7. [7] Tseng, M. L. (2011). Using a hybrid MCDM model to evaluate firm environmental knowledge management in uncertainty. Applied soft computing, 11(1), 1340–1352. https://doi.org/10.1016/j.asoc.2010.04.006

  8. [8] Tseng, M. L. (2011). Green supply chain management with linguistic preferences and incomplete information. Applied soft computing, 11(8), 4894–4903. https://doi.org/10.1016/j.asoc.2011.06.010

  9. [9] Azadeh, A., Osanloo, M., & Ataei, M. (2010). A new approach to mining method selection based on modifying the Nicholas technique. Applied soft computing, 10(4), 1040–1061. https://doi.org/10.1016/j.asoc.2009.09.002

  10. [10] Dağdeviren, M., Yavuz, S., & Kılınç, N. (2009). Weapon selection using the AHP and TOPSIS methods under fuzzy environment. Expert systems with applications, 36(4), 8143–8151. https://doi.org/10.1016/j.eswa.2008.10.016

  11. [11] Filev, D. P., & Yager, R. R. (1991). A generalized defuzzification method via BAD distributions. International journal of intelligent systems, 6(7), 687–697. https://doi.org/10.1002/int.4550060702

  12. [12] Kandel, A. (1986). Fuzzy mathematical techniques with applications. Addison-Wesley Longman Publishing Co. https://doi.org/10.5555/6765

  13. [13] Yager, R. R., & Filev, D. P. (1993). Slide: A simple adaptive defuzzification method. IEEE transactions on fuzzy systems, 1(1), 69. https://doi.org/10.1109/TFUZZ.1993.390286

  14. [14] Abbasbandy, S., & Asady, B. (2004). The nearest trapezoidal fuzzy number to a fuzzy quantity. Applied mathematics and computation, 156(2), 381–386. https://doi.org/10.1016/j.amc.2003.07.025

  15. [15] Wang, Y. M., Yang, J. B., Xu, D. L., & Chin, K. S. (2006). On the centroids of fuzzy numbers. Fuzzy sets and systems, 157(7), 919–926. https://doi.org/10.1016/j.fss.2005.11.006

  16. [16] Chanas, S., Delgado, M., Verdegay, J. L., & Vila, M. A. (1993). Ranking fuzzy interval numbers in the setting of random sets. Information sciences, 69(3), 201–217. https://doi.org/10.1016/0020-0255(93)90120-B

  17. [17] Orlovsky, S. (1993). Decision-making with a fuzzy preference relation. In Readings in fuzzy sets for intelligent systems (pp. 717–723). Elsevier. https://doi.org/10.1016/B978-1-4832-1450-4.50077-8

  18. [18] Cheng, C. H. (1998). A new approach for ranking fuzzy numbers by distance method. Fuzzy sets and systems, 95(3), 307–317. https://doi.org/10.1016/S0165-0114(96)00272-2

  19. [19] Shieh, B. S. (2007). An approach to centroids of fuzzy numbers. International journal of fuzzy systems, 9(1), 51–54. https://www.researchgate.net/publication/238068920_An_Approach_to_Centroids_of_Fuzzy_Numbers

  20. [20] Wang, Y. J., & Lee, H. S. (2008). The revised method of ranking fuzzy numbers with an area between the centroid and original points. Computers & mathematics with applications, 55(9), 2033–2042. https://doi.org/10.1016/j.camwa.2007.07.015

  21. [21] Chu, T. C., & Tsao, C. T. (2002). Ranking fuzzy numbers with an area between the centroid point and original point. Computers & mathematics with applications, 43(1–2), 111–117. https://doi.org/10.1016/S0898-1221(01)00277-2

  22. [22] [22] Ramli, N., & Mohamad, D. (2009). A comparative analysis of centroid methods in ranking fuzzy numbers. European journal of scientific research, 28(3), 492–501. https://doi.org/10.5923/j.ijcem.20160502.02

  23. [23] Pan, H., & Yeh, C. H. (2003). Fuzzy project scheduling. The 12th ieee international conference on fuzzy systems, 2003. fuzz’03. (Vol. 1, pp. 755–760). IEEE. https://doi.org/10.1109/FUZZ.2003.1209458

  24. [24] Pan, H., & Yeh, C. H. (2003). A metaheuristic approach to fuzzy project scheduling. International conference on knowledge-based and intelligent information and engineering systems (pp. 1081–1087). Springer. http://dx.doi.org/10.1007/978-3-540-45224-9_145

  25. [25] Yager, R. R. (1979). Ranking fuzzy subsets over the unit interval. 1978 IEEE conference on decision and control including the 17th symposium on adaptive processes (pp. 1435–1437). IEEE. https://ieeexplore.ieee.org/abstract/document/4046341/

  26. [26] Yager, R. R. (1981). A procedure for ordering fuzzy subsets of the unit interval. Information sciences, 24(2), 143–161. https://doi.org/10.1016/0020-0255(81)90017-7

  27. [27] Murakami, S., & Maeda, M. (1984). Fuzzy decision analysis in the development of centralized regional energy control systems. Energy development, (United States), 6(4). https://www.osti.gov/biblio/5035693

  28. [28] Abbasbandy, S., & Hajjari, T. (2011). An improvement in centroid point method for ranking of fuzzy numbers, 20(78), 109–117. https://www.sid.ir/FileServer/JE/959201178-201

  29. [29] Varghese, A., & Kuriakose, S. (2012). Centroid of an intuitionistic fuzzy number. Notes on intuitionistic fuzzy sets, 18(1), 19–24. https://ifigenia.org/images/archive/1/1b/20120721115607!NIFS-18-1-19-24.pdf

  30. [30] Asady, B., & Zendehnam, A. (2007). Ranking fuzzy numbers by distance minimization. Applied mathematical modelling, 31(11), 2589–2598. https://doi.org/10.1016/j.apm.2006.10.018

  31. [31] Parandin, N., & Fariborzi, A. M. A. (2008). Ranking of fuzzy numbers by distance method, 5(19), 47–55. https://www.sid.ir/FileServer/JE/134520081905

  32. [32] Saneifard, R., & Nahid, S. (2013). A new parametric method for ranking fuzzy numbers based on positive and negative ideal solutions, 5(2), 119–128. https://sid.ir/paper/231733/en

  33. [33] Saneifard, R. (2009). Ranking LR fuzzy numbers with weighted averaging based on levels, 1(2), 163–173. https://doi.org/1016220090206.pdf

  34. [34] Nasibov, E. N., & Mert, A. (2007). On methods of defuzzification of parametrically represented fuzzy numbers. Automatic control and computer sciences, 41, 265–273. https://doi.org/10.3103/S0146411607050057

  35. [35] Kumar, A., Singh, P., & Kaur, A. (2010). Ranking of generalized exponential fuzzy numbers using integral value approach. International journal of advances in soft computing and its applications, 2(2), 1–10. https://www.academia.edu/download/77445202/vol.2.2.5.July.10.pdf

  36. [36] Liou, T. S., & Wang, M. J. J. (1992). Ranking fuzzy numbers with integral value. Fuzzy sets and systems, 50(3), 247–255. https://doi.org/10.1016/0165-0114(92)90223-Q

  37. [37] Rao, P. P. B., & Shankar, N. R. (2011). Ranking fuzzy numbers with a distance method using circumcenter of centroids and an index of modality. Advances in fuzzy systems, 2011(1), 178308. https://doi.org/10.1155/2011/178308

  38. [38] Ezzati, R., Allahviranloo, T., Khezerloo, S., & Khezerloo, M. (2012). An approach for ranking of fuzzy numbers. Expert systems with applications, 39(1), 690–695. https://doi.org/10.1016/j.eswa.2011.07.060

  39. [39] Abbasbandy, S., & Hajjari, T. (2009). A new approach for ranking of trapezoidal fuzzy numbers. Computers & mathematics with applications, 57(3), 413–419. https://doi.org/10.1016/j.camwa.2008.10.090

  40. [40] Yong, D., & Qi, L. (2005). A TOPSIS-based centroid--index ranking method of fuzzy numbers and its application in decision-making. Cybernetics and systems: An international journal, 36(6), 581–595. https://doi.org/10.1080/01969720590961727

  41. [41] Ganesh, A. H., & Jayakumar, S. (2014). Ranking of fuzzy numbers using radius of gyration of centroids. International journal of basic and applied sciences, 3(1), 17. http://dx.doi.org/10.14419/ijbas.v3i1.1477

  42. [42] Dhanasekar, S., Hariharan, S., & Sekar, P. (2014). Ranking of generalized trapezoidal fuzzy numbers using Haar wavelet. Applied mathematical sciences, 8(160), 7951–7958. http://dx.doi.org/10.12988/ams.2014.410798

  43. [43] Barkhordari, A. M. (2014). Ranking of fuzzy numbers based on angle measure, 3(1), 259–265. http://dx.doi.org/1046820140106.pdf

  44. [44] Ezzati, R., Khezerloo, S., & Ziari, S. (2015). Application of parametric form for ranking of fuzzy numbers. Iranian journal of fuzzy systems, 12(1), 59–74. https://ijfs.usb.ac.ir/article_1842_27cb3eea8770ed442d2b473b328b2217.pdf

  45. [45] Lotfi, M., Salahshour, S., Esfahani, F. N., & Jafarnejad, A. (2016). A new approach for ranking fuzzy numbers. Journal of fuzzy set valued analysis, 2016, 102–109. http://dx.doi.org/10.5899/2016/jfsva-00277

  46. [46] Hadi-Vencheh, A., & Mokhtarian, M. N. (2011). A new fuzzy MCDM approach based on centroid of fuzzy numbers. Expert systems with applications, 38(5), 5226–5230. https://doi.org/10.1016/j.eswa.2010.10.036

  47. [47] Hadi-Vencheh, A., & Allame, M. (2010). On the relation between a fuzzy number and its centroid. Computers & mathematics with applications, 59(11), 3578–3582. https://doi.org/10.1016/j.camwa.2010.03.051

Published

2025-06-07

How to Cite

Ashoori, M. . (2025). A Survey of The Centroids of Fuzzy Numbers and Applications. Journal of Intelligent Decision and Computational Modelling, 1(2), 77-86. https://doi.org/10.48314/jidcm.v1i2.66

Similar Articles

You may also start an advanced similarity search for this article.