Search Results (1,184)

This study develops a context-sensitive framework for prioritizing the indicators of financial resilience for banking stability in Iraqi banks operating in a fragile emerging-market environment. Drawing on the financial resilience literature and the institutional, economic, and regulatory characteristics of Iraq, the study identifies the main dimensions, components, and indicators that shape banks’ capacity to absorb shocks, maintain essential functions, and sustain institutional continuity under financial and operational disruption. Data were collected in 2025 from 33 academic and professional experts in Iraq’s banking sector, including university professors, banking specialists, and senior bank executives. To operationalize the framework, the study employs an integrated multi-criteria decision-making approach, using the Analytic Hierarchy Process (AHP) to determine the relative weights of the main dimensions and components and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to rank the indicators. The expert-based prioritization indicates that micro-level banking management received a higher relative weight than macro-level banking management, with weights of 0.5674 and 0.4326, respectively. Among the components, the financial component receives the highest global priority weight, followed by corporate governance, Supervision, policies and controls, and laws and regulations. At the indicator level, forward-looking supervision and forecasting, anti-money-laundering regulatory compliance, installment-loan share, prevention and control of corruption, credit risk, and liquidity management received the highest global priority weights in the expert-based framework. The study contributes to the literature by providing an integrated and context-sensitive framework for prioritizing resilience-related factors in Iraqi banks and by highlighting the relevance of internal management capacity, governance quality, regulatory discipline, and risk control for resilience assessment and improvement in fragile institutional settings. The findings also offer practical implications for bank managers, regulators, and policymakers seeking to enhance the continuity, resilience, and stability of financial institutions in emerging economies. The reported weights represent expert-informed priorities for resilience assessment and improvement; they do not constitute direct estimates of realized bank-level resilience or causal effects on banking stability. Full article

Based on CN05.1 observational data, this study systematically evaluates the performance of the NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP-CMIP6) and the original Coupled Model Intercomparison Project Phase 6 (CMIP6) in simulating extreme high-temperature (EHT) events over the Sichuan–Chongqing region during 1981–2014. Furthermore, it projects changes in EHT indices under different emission scenarios for the mid-and late-21st century using the NEX-GDDP-CMIP6 dataset. The results indicate that NEX-GDDP-CMIP6 exhibits higher spatial correlation (>0.90) and lower root-mean-square error (RMSE) than CMIP6 in depicting the spatial distribution of maximum temperature (Tmax), with mean biases reduced from approximately −2 °C to within 1 °C. This dataset also captures the spatial patterns of EHT frequency (EHTF) and intensity (EHTI), although it exhibits a systematic underestimation of their magnitudes due to the inherent smoothing effect of the Bias Correction and Spatial Disaggregation (BCSD) method. The comprehensive TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) ranking shows that NEX-GDDP-CMIP6 generally ranks higher than the original CMIP6 in simulating EHT indices across all three weighting schemes and exhibits higher inter-model consistency, which further validates its overall applicability in the Sichuan–Chongqing region. Under the Shared Socioeconomic Pathway (SSP) scenarios SSP2-4.5 and SSP5-8.5, all 22 models project positive changes in Tmax, EHTF, and EHTI for both the mid- and late-21st century. The ensemble median increases reach approximately 5.5 °C for Tmax, 28 days per year for EHTF, and 1.8 °C for EHTI, which are 2–3 times the mid-century values and roughly double the corresponding increments under SSP2-4.5. The extreme heat hotspots are primarily located along the Sichuan–Chongqing border, with greater intensification and broader spatial coverage under SSP5-8.5 than under SSP2-4.5, indicating a progressive worsening of heatwave conditions across the study region. Full article

The diversity of today’s malware and the conflicting criteria for predictive performance, computational efficiency, dependability, and interpretability have made the choice of a suitable malware detection model more complicated. Existing research focuses predominantly on predictive performance, while the multidimensional decision process required for practical model selection remains insufficiently addressed. To address this gap, this study provides an explainability-aware hybrid multi-criteria decision-making (MCDM) framework that systematically evaluates and ranks malware detection models across heterogeneous malware datasets. The methodology incorporates predictive performance, computational efficiency, false positive rate, and a composite Explainability Index into a single decision procedure. The Explainability Index integrates explanation stability, sparsity, and expert relevance, enabling interpretability to be explicitly considered in the model-selection process. The hybrid criteria weights are obtained by combining the Analytic Hierarchy Process (AHP) with the entropy weighting method, thereby integrating expert-driven criterion importance with data-driven variability. The final ranking is obtained using the Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS). Four candidate detection models, including Random Forest, XGBoost, Convolutional Neural Network, and Long Short-Term Memory, are independently used to validate the framework on the CIC-MalMem-2022 and CICMalDroid2020 datasets. Under a harmonized evaluation protocol without assuming direct cross-dataset predictive transfer, the experimental results rank XGBoost first with a TOPSIS closeness score of 0.670, followed by Random Forest with 0.624. Sensitivity and ablation analyses further show that the model ranking remains stable while changes in criterion weighting and framework components produce measurable variations in the multi-criteria preference structure. Overall, the framework provides a transparent and multidimensional alternative to conventional performance-centered malware model evaluation. Full article

Failure mode and effects analysis (FMEA), as a means of identifying, preventing, and controlling potential system failures, has been widely applied in many industries. However, the classic FMEA method has several drawbacks in practical applications, such as the uncertainty and ambiguity in the expression of evaluation information, the neglect of the reliability of evaluation information, and the failure to consider the psychological behavior of experts, which leads to inaccurate evaluation results. Therefore, this paper proposes an improved FMEA framework that integrates Z-number theory with the criteria importance through intercriteria correlation (CRITIC) and technique for order preference by similarity to ideal solution (TOPSIS) methods to improve the accuracy of failure mode risk ranking. Specifically, Z-numbers are employed to represent expert assessment information, effectively capturing both fuzziness and reliability. The CRITIC method is then extended with Z-numbers to determine the weights of risk factors, which not only accounts for the interrelationships among factors but also prevents information loss caused by defuzzification of weights. Moreover, to handle missing assessment data, a generalized Z-number distance measure is introduced, and the TOPSIS model is enhanced to rank failure modes by considering the reliability and uncertainty of the information. Finally, the effectiveness of the proposed method is verified by taking the pallet exchange device of a CNC machine tool as an example. The results show that, compared with other FMEA methods, the proposed method considers the reliability and fuzziness of the evaluation information, maintains the original Z-number information structure, and provides more accurate and reliable risk-ranking results. Full article

Reliable electricity supply is essential for healthcare institutions, particularly where grid instability can disrupt service continuity, compromise patient safety, and affect the operation of critical medical equipment. In this context, selecting an appropriate photovoltaic (PV) proposal is a complex decision problem that requires assessing technical, economic, environmental, and regulatory factors jointly. This study develops a hybrid multi-criteria decision-making framework that integrates the Fuzzy Analytic Hierarchy Process (FAHP), the Decision-Making Trial and Evaluation Laboratory (DEMATEL), and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to support PV proposal selection in healthcare institutions. The framework was applied to four competing proposals for a hospital case study in Barranquilla, Colombia. After integrating FAHP and DEMATEL, the economic, technical, and environmental criteria received balanced interdependence-adjusted weights of 0.324, 0.337, and 0.338, respectively. At the same time, DEMATEL identified the technical dimension as the main net influencing dimension within the expert-elicited influence network. The final ranking placed Proposal 1 first, followed by Proposal 4, Proposal 2, and Proposal 3, with closeness coefficients of 0.530, 0.518, 0.498, and 0.492, respectively. Additional comparative analysis showed that omitting DEMATEL changed the winning alternative, whereas preserving the FAHP–DEMATEL weighting structure and replacing TOPSIS with MARCOS yielded the same ranking. Robustness analyses further showed that the ranking remained stable in most supplier-exclusion and leave-one-expert-out scenarios. In contrast, bootstrap-based probabilistic sensitivity analysis showed that Proposal 1 ranked first in 96.2% of the replications. These results support the practical usefulness of the proposed framework for decision-making in healthcare energy planning. Full article

The Voluntary Carbon Market (VCM) is emerging as a complementary governance mechanism supporting global net-zero transitions. While small and medium-sized enterprises (SMEs) represent the backbone of most emerging economies, their organizational readiness to engage in carbon markets remains underexplored. This study advances the organizational readiness literature by contextualizing SME preparedness within emerging carbon market governance and sustainability transition frameworks. Integrating the Analytic Hierarchy Process (AHP) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), this study identifies and prioritizes key determinants of SME readiness and compares readiness levels across three major sectors in Vietnam: Industry and Construction (IC), Services (SV), and Agriculture, Forestry, and Fishing (AFF). The analysis assesses five dimensions of readiness: knowledge, financial, institutional, managerial, and technological readiness. The findings indicate that knowledge readiness received the highest priority weight, followed by financial and institutional readiness. Sectoral analysis reveals that IC SMEs exhibit the highest readiness and remain the top-ranked sector across all leave-one-expert-out and criterion-weight sensitivity analyses, whereas the relative ordering of SV and AFF is more sensitive to panel composition. By revealing the relative importance of readiness dimensions and sectoral differences in an early-stage carbon governance context, this study provides policy-relevant insights for fostering inclusive low-carbon development pathways. Full article

Corrosion-severity grading of grounding-grid connectors from optical images supports proactive power-infrastructure maintenance. Existing approaches rely on single-time-point, manually thresholded hue–saturation–value (HSV) metrics and static multi-criteria decision-making (MCDM) frameworks that cannot capture corrosion dynamics. In this paper we present a pipeline that (1) defines a four-class corrosion grade from an HSV area fraction (S_corr) measured on RGBA optical images, and validates those labels against a baseline-referenced CIEDE2000 metric zero-referenced to each connector’s as-received appearance; (2) generates 240 color-prior-constrained procedural synthetic images from 53 real images across six connector types; (3) fine-tunes a ResNet-18 to estimate corrosion coverage continuously, deriving the reported severity class from that estimate rather than predicting it directly; and (4) fits power-law kinetics C(t) = k·tⁿ to the S_corr time series, propagates bootstrap uncertainty into a Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) framework, and reports kinetics-aware rankings as rank probabilities. The label validation quantifies two limitations of single-threshold HSV grading: a material-color offset that scores an unexposed copper connector at S_corr = 0.442, and insensitivity to achromatic corrosion products covering roughly 80% of the aluminum and galvanized-steel surface. Ablation experiments replicated over five random seeds show that neither contribution claimed from a single run survives replication: synthetic augmentation changes macro-F1 by +0.050 (p = 0.46) under the adopted checkpoint-selection rule and by −0.059 (p = 0.43) under the rule used in the original experiments, and the monotonicity-consistency loss by −0.011 (p = 0.87) and +0.001 (p = 0.99) respectively; the previously reported single-run values of 0.208 and 0.494 are draws from opposite tails of the same seed distributions (0.403 ± 0.140 and 0.344 ± 0.073). The one formulation that improves significantly is the continuous one adopted here, which raises Spearman agreement with the independent metric from 0.316 ± 0.150 to 0.698 ± 0.108 (p = 0.005). Measured against controls, a classifier that never sees the image reaches macro-F1 = 0.425 and, after Holm–Bonferroni correction, no deep configuration is distinguishable from it; none exceeds a one-dimensional linear rule on S_corr (0.664); and under leave-one-material-out cross-validation the network does not improve on S_corr used directly as a predictor (ρ = +0.627 against +0.744, paired p = 0.14). Time-resolved energy-dispersive X-ray spectroscopy (EDS) provides a partial chemical consistency check, with welding at ρ = 0.82 (raw p = 0.023), but no material survives Holm correction across the six tested. A U-Net segmentation head supervised only by synthesis-derived masks attains Dice = 0.85 in-domain and collapses to a 0.033 output range on real images, 5% of the HSV metric’s range; the photometric-stability advantage previously claimed for it is an artifact of that collapse and is withdrawn. Kinetics-aware MCDM with propagated uncertainty resolves 9 of 15 pairwise orderings, placing stainless steel above welding at 30 chamber days with probability 1.000 and reversing the static ranking. The pipeline, code and fixed data split are fully reproducible (random seed 42). Full article

We integrated a Karakuri mechanism into a low-latency (14.2 ms) event-driven software decision support system by combining the analytic hierarchy process (AHP) and the technique for order preference by similarity to ideal solution (TOPSIS). Expert matrices ($C.R.=0.0729$) prioritized labor saving with machine idle-time reduction (global weight = 0.1119) as the most critical requirement. TOPSIS results showed that the cam mechanism was the optimal configuration, followed by pulley and roller mechanisms. These top-ranked mechanical solutions serve as a low-power, zero-electricity baseline that enhances shop-floor management efficiency, providing a stable foundation for advanced sensor integration. By filtering mechanical noise and stabilizing structural motion, the developed system enables seamless incorporation of micro-sensors and telemetry systems, effectively bridging mechanical automation with high-precision digital sensing. This systematic approach reduces reliance on trial-and-error design, supports lean and Jidoka principles, and offers a scalable pathway for sustainable manufacturing transformation. Full article

Growing material pressure and dependence on external supplies require an assessment of the circular economy not only in terms of recycling but also in terms of the structural conditions supporting resource resilience. The aim of the study was to assess and compare such conditions across the 27 Member States of the European Union and to identify the diverse profiles of their strengths and weaknesses. To this end, a multi-criteria framework was developed based on averages from 2021 to 2023 for eight Eurostat indicators with complete data coverage for material consumption, municipal waste, resource productivity, recycling, the use of secondary materials, import dependency and the economic significance of circular economy sectors. Weights were determined using the Criteria Importance Through Intercriteria Correlation (CRITIC) method, and the ranking was carried out using the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method. Estonia, Italy and Germany achieved the highest scores, whilst Luxembourg, Cyprus and Denmark achieved the lowest; however, similar aggregate scores were achieved through different configurations of the criteria. The broad structure of the ranking was relatively stable across several methodological variants (Kendall’s W = 0.8716), but this global consistency coexisted with substantial country-specific sensitivity, particularly to the weighting scheme; CRITIC–TOPSIS and Entropy–TOPSIS showed only moderate pairwise agreement (Spearman’s ρ = 0.5629). A leave-one-country-out jackknife confirmed high stability for most omissions while identifying the Netherlands and Estonia as influential reference-set observations. Replacing domestic material consumption (DMC) with the material footprint did not substantially alter the results. The index should be interpreted as a diagnostic tool for assessing structural conditions, rather than a direct measure of the dynamic response to a shock. Full article

Background/Objectives: The delayed repair of failed medical devices, particularly when multiple devices are simultaneously unavailable, may adversely affect patient safety and healthcare continuity, making repair prioritization a critical hospital decision problem. This study develops an integrated multi-criteria decision-making (MCDM) framework for prioritizing failed medical devices for repair and evaluates the consistency and robustness of the resulting priorities. Its principal contribution is to address repair prioritization as a distinct operational decision problem by integrating deterministic and fuzzy MCDM approaches and robustness analyses within a unified framework. Methods: Seven evaluation criteria—patient safety risk (PSR), device function (DF), device criticality (DC), utilization frequency (UF), availability of alternative devices (AAD), resource impact (RSI), and device age (DA)—were considered. The framework was implemented in a hospital case study involving 32 medical devices using the Analytic Hierarchy Process (AHP), AHP-weighted scoring, the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), Fuzzy Analytic Hierarchy Process (Fuzzy AHP), and Fuzzy Technique for Order Preference by Similarity to Ideal Solution (Fuzzy TOPSIS). Robustness was examined through ±10%, ±30%, and ±50% criterion-weight sensitivity analyses, criterion-removal analysis, comparative ranking analysis, and hospital expert-based AHP and class-based AHP scenarios. Results: In the baseline AHP-weighted scoring ranking, Anesthesia Machine (4.9233) and External Cardiac Pacemaker (4.8862) received the highest repair priorities. Comparisons with TOPSIS, Fuzzy TOPSIS, hospital expert-based AHP, and class-based AHP yielded Spearman’s rank correlation coefficient ($\rho$) values ranging from 0.8328 to 0.9952. Sensitivity analysis showed high overall ranking stability under substantial criterion-weight variations, while criterion-removal analysis also maintained high correlations ($\rho$ = 0.9901–0.9963), supporting the robustness of the overall ranking structure. Conclusions: The findings demonstrate that the framework can support hospital biomedical units in prioritizing failed devices, enhance the transparency and consistency of repair decisions, and contribute to more effective maintenance resource allocation. Full article

Renewable energy investment in the European Union (EU) occurs amid substantial variation in resources, market structures, energy security and regulatory quality. This study develops a comparative market-screening framework for the EU-27 in 2022–2024; it does not estimate the determinants of observed investment flows or the expected return of individual projects. A 12-criterion model combining data-driven CRITIC (Criteria Importance Through Inter-criteria Correlation) weights with TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) was applied. Denmark, Sweden and Austria formed the leading group, whereas Poland ranked last. Under the indicators and EU-27 sample conditions selected in this study, photovoltaic potential, the national electricity-price environment and regulatory quality carried the greatest informational weights; these weights do not represent causal effects or universal investor preferences. Robustness analyses showed very high temporal stability and high consistency after changing the aggregation method, while greater sensitivity arose from the weighting method and the assumed direction of electricity prices. The wind-resource criterion is represented by the midpoint of a reported national range rather than a spatially weighted developable-area average, which limits spatial precision. The results describe the relative maturity and quality of current national investment environments, not future transformation opportunity. The framework can support initial market pre-selection, but investor-specific weighting and project-level due diligence remain necessary. Full article

The increasing deployment of renewable energy resources in geographically remote locations has accelerated the need for efficient long-distance power transmission solutions. High-voltage direct current (HVDC) technology is widely recognized as a preferred option for transmitting large quantities of electrical power over long distances compared to conventional high-voltage alternating current (HVAC) systems. However, determining the most suitable HVDC termination point within an interconnected HVAC network remains a complex planning problem due to the presence of multiple feasible termination locations and competing selection criteria. To address this challenge, this paper presents a multi-criteria decision analysis (MCDA) methodology for the evaluation and ranking of potential HVDC termination points. The Analytic Hierarchy Process (AHP) is utilized to derive the weights of the evaluation criteria, while the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is employed to assess and rank the candidate alternatives. The proposed methodology is demonstrated through a case study involving the transmission of renewable energy from a remote generation source to a distant load center. The findings indicate that the AHP-TOPSIS framework is capable of effectively distinguishing among competing alternatives and identifying the most suitable HVDC termination point. The proposed approach provides a robust, transparent, and practical decision-support tool for power system planners involved in renewable energy integration and transmission network expansion planning. Full article

Due to the increase in the number of ontologies in various domains, ranking them to facilitate their selection for reuse is an important task in ontology engineering to date. To assess the multi-faceted quality configurations of candidate ontologies, Multi-Criteria Decision Making (MCDM) frameworks are used. In particular, the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is an MCDM method that is widely adopted for the task of ontology ranking. However, traditional TOPSIS implementations rely almost exclusively on the Euclidean distance metric. This introduces severe rank volatilities and systematic biases when evaluating heterogeneous ontology metadata. To address these limitations, this paper introduces a novel Multi-Distance Ensemble TOPSIS (Ensemble-TOPSIS) method for robust ontology ranking. Rather than forcing a localized geometric choice, the proposed Ensemble-TOPSIS method simultaneously projects alternative ontologies through a multi-distance ensemble composed of Euclidean, Chebyshev, cosine, and Mahalanobis configurations. The Ensemble-TOPSIS method was applied to three datasets of ontologies from the artificial intelligence, agricultural, and biological domains to test its scalability and multi-domain applicability. The experimental results reveal that all the ontologies from the three domains were successfully ranked by the proposed Ensemble-TOPSIS method. Furthermore, the statistical rank correlation using Spearman’s $\rho$, Kendall’s $\tau$, and the $WS$ rank similarity coefficients was calculated between the TOPSIS variants, and the proposed Ensemble-TOPSIS method achieved the highest correlation in the majority of cases. Moreover, a comprehensive Monte Carlo simulation across 1200 stochastically generated, non-linear, and skewed multicollinear decision domains established the asymptotic stability of the proposed Ensemble-TOPSIS method, which achieved the highest global mean performance ($\overline{\rho }=0.88$, $\overline{\tau }=0.75$, $\overline{WS}=0.94$), minimized rank variance (${\sigma }^2\left(WS\right)=0.0006$), and optimally maximized the lower-bound worst-case performance profile ($\rho =0.67$) compared to individual baseline formulations. Full article

The water resources carrying capacity (WRCC) lays a foundational basis for long-term coordinated water resource governance. Based on the Driving–Pressure–State–Impact–Response (DPSIR) framework, this study constructed a WRCC evaluation system containing 21 indicators and adopted a combined weighting method integrating entropy weight and coefficient of variation. Weighted Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and geographical detector tools were jointly applied to quantify spatial-temporal WRCC disparities within the Jialu River Basin, alongside extraction of core driving forces during 2010–2022. Marked spatial disparities existed across administrative units, with basin-average WRCC ranging from 0.18–0.35. Zhengzhou maintained relatively high carrying levels, Kaifeng stayed chronically low, Xuchang improved after 2019, while Zhoukou experienced an overall decline, forming a relatively stable spatial pattern: Zhengzhou > Zhoukou > Xuchang > Kaifeng. Socioeconomic factors stood among the major drivers of spatial divergence. R&D expenditure and urbanization rate exhibited the highest explanatory capacity, with respective q statistics of 0.58 and 0.57. In contrast, natural factors including precipitation and groundwater reserves showed limited impacts, with q values of only 0.11 and 0.09. Factor interaction analysis showed that bivariate enhancement was the primary interaction type (70.53%), followed by nonlinear enhancement (21.05%) and nonlinear weakening (8.42%). The mean q value of the interactive effects reached 0.58, which was 45.0% higher than that of individual factors, suggesting prominent multi-factor synergistic effects. These results deliver empirical evidence for differentiated watershed regulation and cross-jurisdictional water–ecological coordination, and offer actionable governance insights for densely urbanized plain tributary basins with intense human–water conflicts. Full article

Hybrid marine energy systems that integrate wave and current technologies can improve resource complementarity and spatial utilization. However, the ranking stability of selected hybrid concepts under changes in criterion weights, score assumptions, and multi-criteria decision analysis (MCDA) methods requires further examination. This study extends an existing two-stage concept-selection procedure by evaluating four shortlisted wave energy converter–hydrokinetic turbine configurations using stochastic weight-space sampling, criterion-wise weight sensitivity, cross-method consistency, and bounded score-perturbation analyses. A fixed normalized decision matrix is first evaluated using the Simple Additive Weighting (SAW) method across three sets of 10,000 criterion-weight scenarios generated using normalized-uniform, Dirichlet α = 1, and Dirichlet α = 0.5 distributions. The same scenarios are then evaluated using the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), with ranking consistency quantified using Spearman’s rank correlation and complete-ranking agreement. Score sensitivity is subsequently examined through bounded one-point perturbations of the Stage 2 criterion scores, with SAW and TOPSIS recalculated under equal criterion weights to identify dominance-breaking and rank-reversal conditions. The oscillating water column–Savonius configuration, W1H3, remains first-ranked under all three sampled weight distributions because its normalized criterion scores are equal to or higher than those of every competing configuration across all five criteria. Criterion-wise sensitivity analysis shows that W1H3 is not outranked over the investigated weight range, although it ties with the point absorber–Savonius configuration, W2H3, when the full weight is assigned to mooring synergy or control compatibility. A crossover between W2H3 and the oscillating water column–hybrid Savonius–Darrieus configuration, W1H4, occurs at a co-location-feasibility weight of 0.384615. Across the three weight-sampling distributions, SAW and TOPSIS achieve complete-ranking agreement of 65.91–87.08%, with mean Spearman rank correlations of 0.9318–0.9742; the remaining differences are confined to the ordering of W2H3 and W1H4. Bounded score perturbations show that single one-point score change is sufficient to break the dominance of W1H3 over W2H3, whereas four changes are required for W2H3 to attain a unique first rank under both methods. The results demonstrate that W1H3 is rank-stable under the investigated weight and method variations for the adopted decision matrix, while the score-perturbation analysis identifies the bounded score changes under which the preferred ranking may change. Full article