mcdm fusion

Existing R packages provide inadequate support for emerging multi-criteria decision analysis (MCDM) methods. To address this gap, we introduce RMCDA—an open-source R package that unifies the implementation of over 12 classical and state-of-the-art MCDM methods, including AHP, TOPSIS, PROMETHEE, VIKOR, and, for the first time in R, hierarchical SMCDM and the Stepwise Benchmarking Weighting Method (SBWM). Designed with object-oriented and modular architecture, RMCDA complies with CRAN submission standards. It features standardized method interfaces, automated parameter tuning, and interactive, ggplot2-based visualizations—substantially lowering the barrier to method adoption. The package enables rapid cross-domain deployment and has been widely adopted in teaching, research, and real-world decision-making contexts. By enhancing accessibility, reproducibility, and practical efficiency of MCDM techniques, RMCDA advances methodological transparency and usability in decision science.

Composite indicators in multi-criteria evaluation often suffer from a “black-box” nature, undermining transparency, interpretability, and comprehensibility. Method: This paper proposes a dominance-based rough set approach (DRSA)-driven methodology for constructing interpretable composite indicators. It systematically integrates DRSA with four key interpretability requirements—score explanation, quantile-based classification, preference modeling, and result traceability—via ordinal qualitative coding and threshold-based decision rule induction, yielding human-readable “if–then” rules that explicitly link indicator thresholds to categories or scores. Contribution/Results: Unlike conventional statistical aggregation paradigms, the proposed method enables a paradigm shift toward logic-driven, rule-based evaluation. It significantly enhances transparency, auditability, and the capacity for automatic classification and attribution of new evaluation units, thereby supporting accountable and explainable decision-making in complex multi-criteria assessment contexts.

Existing multi-criteria assessment (MCA) methods often rely on subjective assumptions and homogeneity premises when integrating quantitative (cardinal) and qualitative (ordinal) indicators, neglecting inherent heterogeneity across decision-making units—leading to ranking distortion, compromised fairness, and poor interpretability. This paper proposes a novel MCA framework based on dual virtual gap analysis (VGA), which employs linear programming to construct an adaptive hybrid-data processing architecture. By synergistically integrating data envelopment analysis (DEA) and multi-criteria decision-making (MCDM) principles, the method eliminates homogeneity assumptions and enables coherent modeling of both cardinal and ordinal data. The approach ensures theoretical rigor, computational transparency, and structural interpretability. Two numerical experiments demonstrate that the proposed method significantly enhances ranking stability, robustness against perturbations, and practical decision-support capability.

This paper addresses three core challenges in multi-criteria decision-making (MCDM): difficulty in modeling group consensus, neglect of criterion interdependencies, and weak representation of decision-maker preference uncertainty (e.g., normal/triangular distributions, interval-valued preferences). To this end, we propose the first unified Bayesian probabilistic framework for MCDM. Methodologically, we integrate probabilistic graphical models with finite mixture models and introduce a novel hierarchical group identification algorithm to detect homogeneous subgroups, alongside a probabilistic mechanism for ranking criteria and alternatives. Our key contribution lies in establishing a holistic probabilistic modeling paradigm for MCDM, enabling flexible incorporation of diverse preference representations and rigorous handling of interval-valued probabilities. Empirical evaluations demonstrate that our approach significantly improves accuracy in group consensus identification and robustness in ranking outcomes, while outperforming conventional MCDM methods in uncertainty adaptability and statistical interpretability.

This study addresses the inconsistency of multi-criteria decision-making (MCDM) methods in bank performance evaluation. It systematically compares the ranking outcomes of four MCDM methods—RAM, MOORA, FUCA, and CURLI—applied to 30 Vietnamese commercial banks, benchmarking results against the authoritative CAMELS rating framework. For the first time, FUCA and CURLI are employed for bank ranking, utilizing a six-dimensional indicator system: capital adequacy, asset quality, management capability, profitability, liquidity, and market risk sensitivity. Method effectiveness is quantified via Spearman’s rank correlation coefficient (ρ). Results show FUCA and CURLI achieve exceptional alignment with CAMELS (ρ = 0.9996 and 0.9984, respectively), whereas RAM and MOORA exhibit negative correlations, indicating poor suitability. This work not only clarifies the differential applicability of MCDM methods in financial supervision contexts but also extends the empirical application frontier of FUCA and CURLI in financial performance assessment.

This work addresses the instability and inconsistency in model rankings arising from disparate aggregation methods or sensitivity to model ensembles in multi-metric benchmarking. Framing the issue as a social choice problem, the study models each evaluation metric as generating a preference ordering over models across datasets, with a benchmark operator aggregating these preferences through voting. By identifying structural conditions—such as single-peakedness, group separability, and bounded distance—that circumvent the constraints of Arrow’s impossibility theorem, the paper demonstrates that coherent and stable multi-criterion rankings are achievable. Empirical analysis of prominent benchmarks, including HELM and MMLU, confirms the presence and practical relevance of these preference structures, thereby establishing that rational and stable multi-metric rankings are attainable in real-world settings.

This study addresses the challenge users face in constructing, refining, and validating personalized evaluation criteria when making decisions based on reviews, particularly due to the frequent oversight of infrequent yet critical details. To tackle this issue, the authors propose a visual analytics system that integrates large language models with the Analytic Hierarchy Process (AHP) to automatically generate an initial decision model from user reviews and support human-AI collaborative iterative refinement of criteria, weights, and evidence provenance. The system introduces a novel coverage gap detection mechanism to identify missing evaluation dimensions, incorporates AHP-based consistency constraints for interactive weight adjustment, and enhances decision transparency through multi-level scorecards and exportable reports. Experimental results demonstrate that the system significantly improves users’ control over their evaluation criteria and the overall trustworthiness of their decisions.

This work addresses the limitation of existing scenario theory, which is confined to single-criterion robustness evaluation and thus ill-suited for real-world decision problems involving multiple criteria that rely on distinct datasets. To overcome this, the paper proposes a general data-driven multi-criteria scenario framework that jointly models violation risks across all criteria, enabling precise quantification of overall robustness guarantees that simultaneously satisfy every criterion. By integrating scenario optimization, probabilistic robustness analysis, and joint risk modeling over multiple datasets, the approach substantially improves the accuracy of robustness certificates. This advancement transcends the constraints of traditional single-criterion methods and offers a theoretically rigorous, scalable, and more accurate framework for delivering joint robustness guarantees in a broad range of multi-criteria decision-making settings.

This study addresses the limitations of traditional B2B customer segmentation approaches, such as the RFM model, which rely on singular metrics and struggle to capture the complexity and dynamics of business interactions. To overcome this, the authors propose a dynamic, multi-criteria segmentation framework that extends RFM by incorporating stability and growth dimensions. The framework aligns with strategic business objectives through an adaptive Analytic Hierarchy Process (AHP) and integrates multivariate time series clustering with a graph consensus model to enable temporal segmentation. Evaluated on data from over 3,000 manufacturing enterprises, the approach demonstrates strong temporal robustness and significantly enhances the precision of customer strategy formulation through preference-driven dynamic clustering.