Information technology for solving the multi-criteria decision-making problem using the modified Fuzzy TOPSIS method
Abstract
The relevance of the research topic is determined by the need to effectively solve multi-criteria decisionmaking problems in conditions of fuzzy information. In this regard, the creation of information technologies that would enable the user to select and use the most effective multi-criteria decision-making methods in conditions of fuzzy information is an important problem. The purpose of the study was to develop information technology for solving the multi-criteria decision-making problem using the modified Fuzzy Technique for Order Preference by Similarity to Ideal Solution (FTOPSIS) method based on the use of different metrics and the results of group expertise, which increases the reliability of the obtained decisions. Within the framework of the study, an analysis of the most popular multi-criteria decision-making methods, in particular, methods using the fuzzy set apparatus, was carried out. The article analyses different popular metrics for estimating the distances between a fuzzy positive ideal solution and a fuzzy negative ideal solution in the FTOPSIS method. A technique is proposed for comparing the results of applying different methods, in particular, FTOPSIS using triangular and trapezoidal fuzzy numbers, TOPSIS with triangular and trapezoidal fuzzy numbers for determining criteria weights, which makes it possible to analyse the scale of deviations between the obtained results and to assess the quality of the experts' work. The obtained results expand the possibilities of using TOPSIS and FTOPSIS methods for decision-making in conditions of multi-criteriality and uncertainty. As a practical application of the developed information technology and the modified FTOPSIS method, the article solves the problem of selecting the best of popular risk management standards in IT projects. This will increase the effectiveness of risk management in conditions of uncertainty and incompleteness of information, improve the validity of decisions made, as well as adapt the risk management process to specific conditions of each individual IT project
Keywords
MCDM; MADM; fuzzy sets; FTOPSIS; IT projects; risk management; risk management standards
References
[1] Arman, H., Hadi-Vencheh, A., Kiani Mavi, R., Khodadadipour, M., & Jamshidi, A. (2022). Revisiting the interval and fuzzy TOPSIS methods: Is Euclidean distance a suitable tool to measure the differences between fuzzy numbers? Complexity, 2022, article number 7032662. doi: 10.1155/2022/7032662.
[2] Awasthi, A., Chauhan, S.S., & Goyal, S.K. (2011). A multi-criteria decision making approach for location planning for urban distribution centers under uncertainty. Mathematical and Computer Modelling, 53(1-2), 98-109. doi: 10.1016/j.mcm.2010.07.023.
[3] Black, P.E. (2019). Manhattan distance. Retrieved from https://xlinux.nist.gov/dads/HTML/manhattanDistance. html.
[4] Chen, C.T. (2000). Extension of the TOPSIS for group decision-making under fuzzy environment. Fuzzy Sets and Systems, 114(1), 1-9. doi: 10.1016/S0165-0114(97)00377-1.
[5] Chen, C.T., Lin, C.T., & Huang, S.F. (2006). A fuzzy approach for supplier evaluation and selection in supply chain management. International Journal of Production Economics, 102(2), 289-301. doi: 10.1016/j.ijpe.2005.03.009.
[6] Decisioner. (n.d.). Retrieved from https://dss.knsa.chdtu.edu.ua/.
[7] Deza, M.M., & Deza, E. (2016). Encyclopedia of distances. Berlin: Springer. doi: 10.1007/978-3-662-52844-0.
[8] Han, F., Alkhawaji, R., & Shafieezadeh, M. (2024). Evaluating sustainable water management strategies using TOPSIS and fuzzy TOPSIS methods. Applied Water Science, 15, article number 4. doi: 10.1007/s13201-02402336-7.
[9] Hwang, C.L., & Yoon, K.P. (1981). Multiple attribute decision making. Methods and applications. A state-of-the-art survey. Berlin: Springer. doi: 10.1007/978-3-642-48318-9.
[10] ISO 31000:2018. (2018). Risk management – guidelines. Retrieved from https://www.iso.org/iso-31000-riskmanagement.html.
[11] Kustiyahningsih, Y., Rahmanita, E., Khusnul Khotimah, B., & Purnama, J. (2024). Type-2 fuzzy ANP and TOPSIS methods based on trapezoid fuzzy number with a new metric. International Journal of Advances in Intelligent Informatics, 10(2), article number 239. doi: 10.26555/ijain.v10i2.1285.
[12] Makki, A., & Abdulaal, R. (2023). A hybrid MCDM approach based on fuzzy MEREC-G and fuzzy RATMI. Mathematics, 11(17), article number 3773. doi: 10.3390/math11173773.
[13] Maksymov, A. (2025). Analysis of risk management standards and their application in IT projects. Management of Development of Complex Systems, 61, 66-75. doi: 10.32347/2412-9933.2025.61.66-75.
[14] National Institute of Standards and Technology Special Publication 800-53. (2020). Retrieved from https:// nvlpubs.nist.gov/nistpubs/SpecialPublications/NIST.SP.800-53r5.pdf.
[15] PMBOK guide. (2021). Retrieved from https://www.pmi.org/standards/pmbok.
[16] Rane, N., & Choudhary, S. (2023). Fuzzy AHP and fuzzy TOPSIS as an effective and powerful multi-criteria decision-making (MCDM) method for subjective judgments in selection process. International Research Journal of Modernization in Engineering Technology and Science, 5, 3786-3799. doi: 10.56726/IRJMETS36629.
[17] Rudin, W. (1987). Real and complex analysis. Singapore: McGraw-Hill.
[18] Shyur, H.-J., & Shih, H.-S. (2024). Resolving rank reversal in TOPSIS: A comprehensive analysis of distance metrics and normalization methods. Informatica, 35(4), 837-858. doi: 10.15388/24-INFOR576.
[19] Talukdar, P., & Dutta, P. (2019). A comparative study of TOPSIS method via different distance measure. International Journal of Research in Advent Technology, 7(5), 118-126. doi: 10.32622/ijrat.75201937.
[20] Yin, H., Li, X., & Gao, Y. (2020). Relative Euclidean distance with application to TOPSIS and estimation performance ranking. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 52(2), 1052-1064. doi: 10.1109/ TSMC.2020.3017814.