Abstract
Order appears in all areas related to mathematics and computer science. Given a set in many cases it is desirable to establish a precedence relation between the elements of the set either total or partial. In this paper, we explore an ordering operator for fuzzy numbers that is based on the Zadeh extension principle. This proposal takes into account the intuition of users of Database Management Systems. Our analysis includes a formal proof of these operator’s properties and examples of the applicability of the operator in representative cases that show its suitability for intuition management. Also, an operator implementation in the Haskell and SQL languages is presented. This allows its evaluation in different contexts.








Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.References
Akyar H (2018) A new approach for ordering fuzzy numbers and its application to fuzzy matrix games. J Sci Arts 18(3):547–564
Alizadeh HM, Khamseh AA, Ghomi SF (2013) Fuzzy hypothesis testing with vague data using likelihood ratio test. Soft Comput 17(9):1629–1641
Azman FN, Abdullah L (2012) Ranking fuzzy numbers by centroid method. Malays J Fundam Appl Sci 8(3)
Basar F (2012) Summability theory and its applications. Bentham Science Publishers
Biswas R (2016) Is fuzzy theory an appropriate tool for large size decision problems? Springer International Publishing, Cham, pp 93–118. https://doi.org/10.1007/978-3-319-26302-1_8
Canfora G, Troiano L (2004) Fuzzy ordering of fuzzy numbers. IEEE. https://doi.org/10.1109/FUZZY.2004.1375478
Chi HTX, Yu VF (2018) Ranking generalized fuzzy numbers based on centroid and rank index. Appl Soft Comput 68:283–292. https://doi.org/10.1016/j.asoc.2018.03.050
Dubois D, Prade H (1987) The mean value of a fuzzy number. Fuzzy Sets Syst 24(3):279–300. https://doi.org/10.1016/0165-0114(87)90028-5
Figueroa-García JC, Chalco-Cano Y, Romón-Flores H (2018) Yager index and ranking for interval type-2 fuzzy numbers. IEEE Trans Fuzzy Sys 26(5):2709–2718. https://doi.org/10.1109/TFUZZ.2017.2788884
Galindo J, Urrutia A, Piattini M (2006) Fuzzy database modeling. Idea Group Publishing, Design and Implementation. https://doi.org/10.4018/978-1-59140-324-1
Ganesh AH, Suresh M (2017) Ordering of generalised trapezoidal fuzzy numbers based on area method using euler line of centroids. Adv Fuzzy Math 12(4):783–791
Kerre E (1982) The use of fuzzy set theory in electrocardiological diagnostics. In: M. Gupta and E. Sanchez (Eds) Aproximate reasoning in decision analysis 20: pp. 277–282
Kerre EE, Mareš M, Mesiar R (2000) Generated fuzzy quantities and their orderings. Springer, US, Boston, MA, pp 119–129. https://doi.org/10.1007/978-1-4615-5209-3_9
Kumar A, Singh P, Kaur P, Kaur A (2011) RM approach for ranking of L–R type generalized fuzzy numbers. Soft Comput 15(7):1373–1381
Mitchell HB, Schaefer PA (2000) On ordering fuzzy numbers. Int J Intel Sys 15(11):981–993. https://doi.org/10.1002/1098-111X(200011)15:11<981::AID-INT1>3.0.CO;2-Z
Nasseri SH, Taleshian F, Alizadeh Z, Vahidi J (2012) A new method for ordering LR fuzzy number. J Math Comput Sci 4(3):283–294. https://doi.org/10.22436/jmcs.04.03.01
Prade H, Testemale C (1989) The possibilistic approach to the handling of imprecison in database systems. IEEE Data Eng Bull 12(2):4–10
Rao PPB, Shankar NR (2011) Ranking fuzzy numbers with a distance method using circumcenter of centroids and an index of modality. Adv Fuzzy Syst 2011:3. https://doi.org/10.1155/2011/178308
Rosen KH (2012) Discrete Mathematics and its Applications, 7th edn. Mc Graw Hill, https://doi.org/10.1093/teamat/hrq007
Rudnik K, Kacprzak D (2017) Fuzzy topsis method with ordered fuzzy numbers for flow control in a manufacturing system. Appl Soft Comput 52:1020–1041
Seiti H, Hafezalkotob A, Martínez L (2019) R-numbers, a new risk modeling associated with fuzzy numbers and its application to decision making. Inform Sci 483:206–231. https://doi.org/10.1016/j.ins.2019.01.006
Singh P (2015) A novel method for ranking generalized fuzzy numbers. JISE J Inf Sci Eng 31(4):1373–1385. https://doi.org/10.6688/JISE.2015.31.4.13
Thomas GB, Weir MD (2010) Cálculo: una variable, 12th edn. Pearson Educación
Vincent FY, Van LH, Dat LQ, Chi HTX, Chou SY, Duong TTT (2017) Analyzing the ranking method for fuzzy numbers in fuzzy decision making based on the magnitude concepts. Int J Fuzzy Syst 19(5):1279–1289
Wang Y (2020) Combining technique for order preference by similarity to ideal solution with relative preference relation for interval-valued fuzzy multi-criteria decision-making. Soft Comput 24:11347–11364. https://doi.org/10.1007/s00500-019-04599-8
Wang Y (2020) Utilization of trapezoidal intuitionistic fuzzy numbers and extended fuzzy preference relation for multi-criteria group decision-making based on individual differentiation of decision-makers. Soft Comput 24:397–407. https://doi.org/10.1007/s00500-019-03921-8
Wang YM, Yang JB, Xu DL, Chin KS (2006) On the centroids of fuzzy numbers. Fuzzy Sets Syst 157(7):919–926
Yager R (1980) On choosing between fuzzy subsets. Kybernetes 9(2):151–154. https://doi.org/10.1108/eb005552
Yuan Y (1991) Criteria for evaluating fuzzy ranking methods. Fuzzy Sets Syst 43(2):139–157. https://doi.org/10.1016/0165-0114(91)90073-Y
Zadeh LA (1965) Fuzzy sets. Inf Control 8:338–353. https://doi.org/10.1016/S0019-9958(65)90241-X
Zadeh LA (1975) The concept of a linguistic variable and its application to approximate reasoning-I. Inf Sci 8(3):199–249. https://doi.org/10.1016/0020-0255(75)90036-5
Zadeh LA (1975) The concept of a linguistic variable and its application to approximate reasoning-III. Inf Sci 9:43–64. https://doi.org/10.1016/0020-0255(75)90017-1
Ziemba P (2018) NEAT F-PROMETHEE-A new fuzzy multiple criteria decision making method based on the adjustment of mapping trapezoidal fuzzy numbers. Expert Syst Appl 110:363–380. https://doi.org/10.1016/j.eswa.2018.06.008
Acknowledgements
This work is part of the project “Challenges of the Fuzzy Relational Model,” with the support of UNEXPO “Antonio José de Sucre,” Barquisimeto, Venezuela. We thank Professor Carlos Lameda, associate coordinator of this project, and Professor Ralph Grove, a friend in Norfolk, VA, who helped us with the editing of this paper. We also want to thank Him who can order everything, even the fuzziest lives, to whom, together with the Psalmist, we express: “Oh that my ways were ordered to keep thy statutes!” (Psalm 119:5).
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interest.
Ethical approval
This article does not contain any studies with human participants performed by any of the authors.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Aguilera, A., Carrasquel, S., Coronado, D. et al. An ordering of fuzzy numbers based on the Zadeh extension principle. Soft Comput 26, 3091–3106 (2022). https://doi.org/10.1007/s00500-021-06470-1
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00500-021-06470-1