Interpretable Robust Multicriteria Ranking with TODIM in Generalized Orthopair Fuzzy Settings

Authors

DOI:

https://doi.org/10.31181/sor31202632

Keywords:

TODIM, q-rung orthopair fuzzy sets, Robustness analysis, Interpretability, Prospect theory

Abstract

The endeavor to align TODIM (an acronym in Portuguese of interactive and multicriteria decision making) with prospect theory has given rise to the development of several variant methods, including power TODIM, exponential TODIM, and logarithmic TODIM. However, these existing methods fail to address high-order uncertainty within generalized orthopair fuzzy environments. To overcome this limitation, we propose an interpretable robust TODIM approach tailored for generalized orthopair fuzzy settings. First, we extend these TODIM methods to accommodate generalized orthopair fuzzy settings, integrating them into a unified framework. Second, we introduce a set of robustness analysis measures for the extended TODIM method, accounting for simultaneous uncertainty in criteria weights, value function coefficients, and the membership and non-membership degrees of generalized orthopair fuzzy sets. Third, we develop a programming model to determine representative criteria weights based on these robustness analysis measures, followed by an approach to recommend an interpretable and robust ranking within the extended TODIM framework. Finally, we present an illustrative example to demonstrate the application of this interpretable and robust TODIM approach, accompanied by a comparative analysis to highlight its advantages.

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References

Greco, S., Figueira, J., & Ehrgott, M. (2016). Multiple criteria decision analysis: State of the art surveys. Springer New York, NY. https://doi.org/10.1007/978-1-4939-3094-4

Greco, S., Słowiński, R., & Wallenius, J. (2024). Fifty years of multiple criteria decision analysis: From classical methods to robust ordinal regression. European Journal of Operational Research. https://doi.org/10.1016/j.ejor.2024.07.038

Gomes, L. F. A. M., & Lima, M. M. P. P. (1991). TODIM: Basics and application to multicriteria ranking of projects with environmental impacts. Foundations of Computing and Decision Sciences, 16, 113–127.

Gomes, L. F. A. M., & Lima, M. M. P. P. (1992). From modeling individual preferences to multicriteria ranking of discrete alternatives: A look at prospect theory and the additive difference model. Foundations of Computing and Decision Sciences, 17(3), 171–184.

Gomes, L. F. A. M. (2009). An application of the TODIM method to the multicriteria rental evaluation of residential properties. European Journal of Operational Research, 193(1), 204–211. https://doi.org/10.1016/j.ejor.2007.10.046

Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47(2), 363–391. https://doi.org/10.2307/1914185

Lourenzutti, R., & Krohling, R. A. (2013). A study of TODIM in an intuitionistic fuzzy and random environment. Expert Systems with Applications, 40(16), 6459–6468. https://doi.org/10.1016/j.eswa.2013.05.070

Lee, Y. S., & Shih, H. S. (2016). Incremental analysis for generalized TODIM. Central European Journal of Operations Research, 24, 901–922. https://doi.org/10.1007/s10100-015-0427-2

Llamazares, B. (2018). An analysis of the generalized TODIM method. European Journal of Operational Research, 269(3), 1041–1049. https://doi.org/10.1016/j.ejor.2018.02.054

Leoneti, A. B., & Gomes, L. F. A. M. (2021). A novel version of the TODIM method based on the exponential model of prospect theory: The ExpTODIM method. European Journal of Operational Research, 295(3), 1042–1055. https://doi.org/10.1016/j.ejor.2021.03.055

Krohling, R. A., & de Souza, T. T. (2012). Combining prospect theory and fuzzy numbers to multicriteria decision making. Expert Systems with Applications, 39(13), 11487–11493. https://doi.org/10.1016/j.eswa.2012.04.006

Ren, P., Xu, Z., & Gou, X. (2016). Pythagorean fuzzy TODIM approach to multi-criteria decision making. Applied Soft Computing, 42, 246–259. https://doi.org/10.1016/j.asoc.2015.12.020

Tian, X., Niu, M., Zhang, W., Li, L., & Herrera-Viedma, E. (2021). A novel TODIM based on prospect theory to select green supplier with q-rung orthopair fuzzy set. Technological and Economic Development of Economy, 27(2), 284–310. https://doi.org/10.3846/tede.2020.12736

Qin, J., Liu, X., & Pedrycz, W. (2017). An extended TODIM multi-criteria group decision making method for green supplier selection in interval type-2 fuzzy environment. European Journal of Operational Research, 258(2), 626–638. https://doi.org/10.1016/j.ejor.2016.09.059

Fan, Z. P., Zhang, X., Chen, F. D., & Liu, Y. (2013). Extended TODIM method for hybrid multiple attribute decision making problems. Knowledge-Based Systems, 42, 40–48. https://doi.org/10.1016/j.knosys.2012.12.014

Peng, X., & Luo, Z. (2021). A review of q-rung orthopair fuzzy information: Bibliometrics and future directions. Artificial Intelligence Review, 54, 3361–3430. https://doi.org/10.1007/s10462-020-09926-2

Liu, Y., Qin, Y., Liu, H., Abdullah, S., & Rong, Y. (2024). Prospect theory-based q-rung orthopair fuzzy TODIM method for risk assessment of renewable energy projects. International Journal of Fuzzy Systems, 26(3), 1046–1068. https://doi.org/10.1007/s40815-023-01652-5

Zhang, W., Gao, H., Guo, H., & Pamučar, D. (2025). Competition-driven robust multicriteria ranking for managing interactive generalized orthopair information in humanitarian operations. Information Sciences, 700, 121819. https://doi.org/10.1016/j.ins.2024.121819

Zhang, W., Ju, Y., & Gomes, L. F. A. M. (2017). The SMAA-TODIM approach: Modeling of preferences and a robustness analysis framework. Computers & Industrial Engineering, 114, 130–141. https://doi.org/10.1016/j.cie.2017.10.006

Lima, Y. Q. D., Gomes, L. F. A. M., & Leoneti, A. B. (2023). Decommissioning offshore oil and gas production systems with SMAA-ExpTODIM. Pesquisa Operacional, 43, e267436. https://doi.org/10.1590/0101-7438.2023.043.00267436

Yager, R. R. (2017). Generalized orthopair fuzzy sets. IEEE Transactions on Fuzzy Systems, 25(5), 1222–1230. https://doi.org/10.1109/TFUZZ.2016.2604005

Liu, P., & Wang, P. (2018). Some q-rung orthopair fuzzy aggregation operators and their applications to multiple-attribute decision making. International Journal of Intelligent Systems, 33(2), 259–280. https://doi.org/10.1002/int.21927

Du, W. S. (2018). Minkowski-type distance measures for generalized orthopair fuzzy sets. International Journal of Intelligent Systems, 33(4), 802–817. https://doi.org/10.1002/int.21968

Arcidiacono, S. G., Corrente, S., & Greco, S. (2023). Scoring from pairwise winning indices. Computers & Operations Research, 157, 106268. https://doi.org/10.1016/j.cor.2023.106268

Mousseau, V., Figueira, J., Dias, L., da Silva, C. G., & Clímaco, J. (2003). Resolving inconsistencies among constraints on the parameters of an MCDA model. European Journal of Operational Research, 147(1), 72–93. https://doi.org/10.1016/S0377-2217(02)00233-3

Tervonen, T., van Valkenhoef, G., Baştürk, N., & Postmus, D. (2013). Hit-and-run enables efficient weight generation for simulation-based multiple criteria decision analysis. European Journal of Operational Research, 224(3), 552–559. https://doi.org/10.1016/j.ejor.2012.08.026

Published

2025-03-14

How to Cite

Zhang, W., & Gao, H. (2025). Interpretable Robust Multicriteria Ranking with TODIM in Generalized Orthopair Fuzzy Settings. Spectrum of Operational Research, 3(1), 14-28. https://doi.org/10.31181/sor31202632