In multi-attribute group decision-making (MAGDM) problems, decision-makers often express their subjective preferences using linguistic terms, and their decision-making behavior is influenced by psychological factors. To address the limitations of existing methods in effectively handling the fuzziness of linguistic evaluation information and the psychological behavioral characteristics of decision-makers, this paper proposes an improved TODIM-MAGDM model based on probabilistic linguistic term sets (PLTSs) and prospect theory (PT). First, PLTSs are employed to represent the decision-makers' linguistic evaluation information, capturing the hesitation and randomness in the evaluation process through the introduction of probability distributions. Second, based on PT, a gain-loss relative dominance function is constructed to quantify the differences in decision-makers' loss aversion and risk attitudes during the alternative comparison process. Third, a mechanism combining subjective and objective weighting is introduced, which integrates decision-makers' subjective preferences with the objective characteristics of the evaluation data to comprehensively determine attribute weights. Finally, the combined weights are embedded into the dominance degree calculation of the TODIM method, enabling the ranking and optimization of multi-attribute alternatives in a probabilistic linguistic environment. Results from case analysis and comparative experiments demonstrate that the proposed method performs well in terms of ranking consistency, result stability, and parameter robustness. It can effectively process uncertain linguistic evaluation information and reflect the psychological behavioral characteristics of decision-makers. This study provides an analytical tool for complex MAGDM problems under uncertain environments, offering both behavioral interpretability and information expression capability.

Juxiang Wang, Ying Zang. The Extended TODIM Method and Its Applications. Academic Journal of Computing & Information Science (2026), Vol. 9, Issue 4: 39-48. https://doi.org/10.25236/AJCIS.2026.090405.

[1] Liu P, Shen J, Zhang P. A dual-driven MAGDM method based on single-valued neutrosophic credibility numbers Einstein variable extended power geometric aggregation operator and SPA-MARCOS[J]. Artificial Intelligence Review, 2025, 58(10): 330.

[2] Li W, Ye J. MAGDM model using an intuitionistic fuzzy matrix energy method and its application in the selection issue of hospital locations[J]. Axioms, 2023, 12(8): 766.

[3] Shi Z, Ji W. Advanced technique for intuitionistic fuzzy MAGDM and applications to park landscape planning and design schemes evaluation[J]. International Journal of Agricultural and Environmental Information Systems (IJAEIS), 2025, 16(1): 1-19.

[4] Akram M, Ali U, Santos-García G, et al. 2-tuple linguistic Fermatean fuzzy MAGDM based on the WASPAS method for selection of solid waste disposal location[J]. Math Biosci Eng, 2023, 20(2): 3811-3837.

[5] Ma X, Han X, Xu Z, et al. Fusion of probabilistic linguistic term sets for enhanced group decision-making: Foundations, survey and challenges[J]. Information Fusion, 2025, 116: 102802.

[6] Wang Y, Zhan J, Zhang C, et al. A group consensus model with prospect theory under probabilistic linguistic term sets[J]. Information Sciences, 2024, 653: 119800.

[7] Liu Z, Liao H, Li M, et al. A deep learning-based sentiment analysis approach for online product ranking with probabilistic linguistic term sets[J]. IEEE Transactions on Engineering Management, 2023, 71: 6677-6694.

[8] Lin G, Lin M S, Song H. An assessment of prospect theory in tourism decision-making research[J]. Journal of Travel Research, 2024, 63(2): 275-297.

[9] Zhang H, Wang H, Wei G. Spherical fuzzy TODIM method for MAGDM integrating cumulative prospect theory and CRITIC method and its application to commercial insurance selection[J]. Artificial Intelligence Review, 2023, 56(9): 10275-10296.

[10] Sun H, Yang Z, Cai Q, et al. An extended Exp-TODIM method for multiple attribute decision making based on the Z-Wasserstein distance[J]. Expert Systems with Applications, 2023, 214: 119114.

[11] Wu W. Probabilistic Linguistic TODIM Method with Probabilistic Linguistic Entropy Weight and Hamming Distance for Teaching Reform Plan Evaluation[J]. Mathematics, 2024, 12(22): 3520.

[12] Tang J, Liu X, Wang W. Consensus-based generalized TODIM approach for occupational health and safety risk analysis with opinion interactions[J]. Applied Soft Computing, 2024, 150: 111093.

[13] Pang Q, Wang H, Xu Z S. Probabilistic linguistic term sets in multi-attribute group decision making[J]. Information sciences,2016(369):128-143.

[14] Xu D S, Wei X L, Ding H, Bin H Q. A New Method Based on PROMETHEE and TODIM for Multi-Attribute Decision-Making with Single-Valued Neutrosophic Sets[J]. Mathematics,2020, 8, 1816.

[15] Chaube S, Pant S, Kumar A, et al. An overview of multi-criteria decision analysis and the applications of AHP and TOPSIS methods[J]. International Journal of Mathematical, Engineering and Management Sciences, 2024, 9(3): 581.

[16] Mallick R, Pramanik S, Giri B C. TOPSIS and VIKOR strategies for COVID-19 vaccine selection in QNN environment[J]. OPSEARCH, 2024, 61(4): 2072-2094.