Abstract
Decision support systems embedded in modern information systems often rely on multi-criteria decision analysis (MCDA) to evaluate alternatives; however, selecting an appropriate MCDA method and managing imprecision in stakeholder assessments remain persistent challenges. The Iterative Compromise Ranking Analysis (ICRA) addresses the former by compromising rankings from multiple MCDA methods, but its original formulation assumes crisp inputs. This paper extends ICRA to operate on triangular fuzzy numbers (TFNs), enabling the processing of imprecise expert evaluations under uncertainty. The proposed Fuzzy ICRA applies alpha-cut arithmetic to propagate uncertainty through the MCDA methods at each iteration, preserving the informational content of fuzzy inputs throughout the compromise process. A simulation study with three experiments examines the method using TOPSIS and VIKOR. A step-by-step example demonstrates convergence and uncertainty evolution; a propagation analysis across six spread levels shows that ICRA reduces input uncertainty over iterations; and a robustness analysis evaluates sensitivity to expert-assessment noise. Results indicate reliable convergence and a consistent uncertainty-reduction effect, with TFN spreads decreasing monotonically after the second iteration.
Paper Type
Full Paper
DOI
10.62036/ISD.2026.68
Fuzzy ICRA: Iterative Compromise Ranking under Uncertainty for Decision Support Systems
Decision support systems embedded in modern information systems often rely on multi-criteria decision analysis (MCDA) to evaluate alternatives; however, selecting an appropriate MCDA method and managing imprecision in stakeholder assessments remain persistent challenges. The Iterative Compromise Ranking Analysis (ICRA) addresses the former by compromising rankings from multiple MCDA methods, but its original formulation assumes crisp inputs. This paper extends ICRA to operate on triangular fuzzy numbers (TFNs), enabling the processing of imprecise expert evaluations under uncertainty. The proposed Fuzzy ICRA applies alpha-cut arithmetic to propagate uncertainty through the MCDA methods at each iteration, preserving the informational content of fuzzy inputs throughout the compromise process. A simulation study with three experiments examines the method using TOPSIS and VIKOR. A step-by-step example demonstrates convergence and uncertainty evolution; a propagation analysis across six spread levels shows that ICRA reduces input uncertainty over iterations; and a robustness analysis evaluates sensitivity to expert-assessment noise. Results indicate reliable convergence and a consistent uncertainty-reduction effect, with TFN spreads decreasing monotonically after the second iteration.
Recommended Citation
Paradowski, B. & Sałabun, W.(2026). Fuzzy ICRA: Iterative Compromise Ranking under Uncertainty for Decision Support Systems. 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.2026.68