Abstract
An accurate representation of decision-maker preferences is a critical requirement for modern information systems and decision support systems, particularly in environments characterized by uncertainty. To address this challenge, this study proposes an extension of the Fuzzy Ranking Comparison (RANCOM) method by introducing alternative relationship functions and a controllable uncertainty parameter $\gamma$, enabling more flexible modeling of subjective judgments. A simulation-based analysis was conducted to evaluate the impact of different relationship functions on the accuracy of representing fuzzy criteria weights using multiple performance metrics. The results show that the logistic function consistently achieves the lowest TFN distance and the highest TFN similarity across most configurations, indicating the most accurate reconstruction of the reference preference structure. Additionally, the ratio-based and exponential functions outperform the standard three-value scheme, particularly for smaller problem sizes. These improvements enhance the ability of decision support systems to operate under uncertainty, leading to better alignment with user preferences and more reliable decision outcomes. The findings highlight the potential of the extended Fuzzy RANCOM method as an effective component of advanced information systems.
Paper Type
Full Paper
DOI
10.62036/ISD.2026.72
Improving decision-makers' preference representation in decision support systems through an extended Fuzzy Ranking Comparison method
An accurate representation of decision-maker preferences is a critical requirement for modern information systems and decision support systems, particularly in environments characterized by uncertainty. To address this challenge, this study proposes an extension of the Fuzzy Ranking Comparison (RANCOM) method by introducing alternative relationship functions and a controllable uncertainty parameter $\gamma$, enabling more flexible modeling of subjective judgments. A simulation-based analysis was conducted to evaluate the impact of different relationship functions on the accuracy of representing fuzzy criteria weights using multiple performance metrics. The results show that the logistic function consistently achieves the lowest TFN distance and the highest TFN similarity across most configurations, indicating the most accurate reconstruction of the reference preference structure. Additionally, the ratio-based and exponential functions outperform the standard three-value scheme, particularly for smaller problem sizes. These improvements enhance the ability of decision support systems to operate under uncertainty, leading to better alignment with user preferences and more reliable decision outcomes. The findings highlight the potential of the extended Fuzzy RANCOM method as an effective component of advanced information systems.
Recommended Citation
Więckowski, J., Paradowski, B. & Sałabun, W.(2026). Improving decision-makers' preference representation in decision support systems through an extended Fuzzy Ranking Comparison method. In M. Valenta, B. Mannová, R. Pergl, A. Przybylek, M. Lang, H. Linger, C. Schneider, N. Iivari, & E. Insfran (Eds.), Making ISD Sustainable: Reloaded with AI and Automation (ISD2026 Proceedings). Prague, Czech Republic: Czech Technical University in Prague. ISBN: 978-80-01-07585-2. https://doi.org/10.62036/ISD.2026.72