One of the key tasks of operational management in companies within the information technology sector is the selection of an adequate cloud platform to improve work efficiency. This research proposes two fuzzy multicriteria models for precisely determining the priority of cloud platforms. The selection of attributes for evaluating cloud platforms is based on experience and best practices in this economic sector. In the first model, the ranking of the considered cloud platforms is performed using the proposed TOPSIS method extended with interval type-2 triangular fuzzy numbers. The second model is based on the singular values of the corresponding matrix. This method defines both the energy and entropy of a fuzzy soft set, enabling the development of an effective decision-making framework. The proposed methodology represents the scientific contribution of this research, as well as a second incremental improvement of the model compared to the current state of the relevant literature. The models were tested and validated using real data from an information technology company, and a comparison of the obtained results was conducted.
AggarwalC. C. (2020). Linear Algebra and Optimization for Machine Learning. Cham: Springer Nature Switzerland AG, ISBN 978-3-030-40343-0.
2.
AleksićA.MilanovićD. D.KomatinaN.TadićD. (2023). Evaluation and ranking of failures in manufacturing process by combining best worst method and VIKOR under type 2 fuzzy environment. Expert Systems, 40(2), e13148. https://doi.org/10.1111/exsy.13148.
3.
AleksićA.TadićD. (2023). Industrial and management applications of type-2 multi-attribute decision-making techniques extended with type-2 fuzzy sets from 2013 to 2022. Mathematics, 11(10), 1–24. https://doi.org/10.3390/math11102249
4.
AliM. I.FengF.LiuX. Y.MinW. K.ShabirM. (2009). On some new operations in soft set theory. Computers and Mathematics with Applications, 57(9), 1547–1553. https://doi.org/10.1016/j.camwa.2008.11.009
5.
ÇağmanN.EnginogluS. (2010). Soft set theory and uni-int decision making. European Journal of Operational Research, 207, 848–855. https://doi.org/10.1016/j.ejor.2010.05.004
6.
ÇağmanN.EnginogluS.CitakF. (2011). Fuzzy soft set theory and its applications. Iranian Journal of Fuzzy Systems, 8(3), 137–147. https://doi.org/10.22111/ijfs.2011.292
7.
CelikE.GulM.AydinN.GumusA. T.GuneriA. F. (2015). A comprehensive review of multi criteria decision making approaches based on interval type-2 fuzzy sets. Knowledge-Based Systems, 85, 329–341. https://doi.org/10.1016/j.knosys.2015.06.004
8.
DokerogluT.SevincE.KucukyilmazT.CosarA. (2019). A survey on new generation metaheuristic algorithms. Computers and Industrial Engineering, 137, 106040. https://doi.org/10.1016/j.cie.2019.106040
9.
GaoP.GaoY. (2019). Quadrilateral interval type-2 fuzzy regression analysis for data outlier detection. Mathematical Problems in Engineering, 2019(28), 1–9. https://doi.org/10.1155/2019/4914593.
10.
GutmanI. (1978). The energy of a graph. Berichte der Mathematisch-Statistischen Sektion im Forschungszentrum Graz, 103, 1–22.
11.
HeidarzadeA.MahdaviI.Mahdavi-AmiriN. (2016). Supplier selection using a clustering method based on a new distance for interval type-2 fuzzy sets: A case study. Applied Soft Computing, 38, 213–231. https://doi.org/10.1016/j.asoc.2015.09.029
12.
HwangC. L.YoonK. (1981). Methods for multiple attribute decision making. In Multiple attribute decision making (pp. 58–191). Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-48318-9_3.
13.
KahramanC.OnarS. C.OztaysiB. (2015). Fuzzy multicriteria decision-making: A literature review. International Journal of Computational Intelligence Systems, 8(4), 637–666. https://doi.org/10.1080/18756891.2015.1046325
14.
KomatinaN.TadićD.DjurićG.AleksićA. (2023). Determination of manufacturing process failures priority under type 2 fuzzy environment: Application of genetic algorithm and variable neighborhood search, proceedings of the institution of mechanical engineers, part E. Journal of Process Mechanical Engineering, 238(5), 09544089231160510. https:/doi.org/10.1177/09544089231160510.
15.
KuoM. S.LiangG. S. (2012). A soft computing method of performance evaluation with MCDM based on interval-valued fuzzy numbers. Applied Soft Computing, 12(1), 476–485. https://doi.org/10.1016/j.asoc.2011.08.020
16.
LiuZ.VandenbergheL. (2010). Interior-point method for nuclear norm approximation with application to system identification. SIAM Journal on Matrix Analysis and Applications, 31(3), 1235–1256. https://doi.org/10.1137/090755436
Mudrić-StaniškovskiLj.DjurovićLj.StojanovićN. (2024). Energy of a fuzzy soft set and its application in decision-making. Iranian Journal of Fuzzy Systems, 21(2), 35–49. https://doi.org/10.22111/ijfs.2024.46797.8243
SaatyT. L. (2013). The modern science of multicriteria decision making and its practical applications: The AHP/ANP approach. Operations Research, 61(5), 1101–1118. https://doi.org/10.1287/opre.2013.1197
23.
SharafI. M. (2020). An interval type-2 fuzzy TOPSIS using the extended vertex method for MAGDM. SN Applied Sciences, 2, 87. https://doi.org/10.1007/s42452-019-1825-1
ZhangH.ZhangW.MeiC. (2009). Entropy of interval-valued fuzzy sets based on distance and its relationship with similarity measure. Knowledge-Based Systems, 22(6), 449–454. https://doi.org/10.1016/j.knosys.2009.06.007
