Search Results (322)

Universal accessibility remains a critical challenge for effective public transport and urban equity. This study addresses the need for operational prioritisation tools by proposing a robust hybrid methodology to rank interventions at urban mobility nodes. The approach combines the Analytic Hierarchy Process (AHP) for integrating expert and participatory criteria weighting with four Multi-Criteria Decision-Making (MCDM) techniques (TOPSIS, VIKOR, COPRAS, and ARAS) to ensure solution reliability. Empirical validation, conducted on 30 bus stops in Segovia, Spain, confirmed the methodological soundness, evidenced by near-perfect correlations (ρ = 0.99) among the compromise and additive ratio models (TOPSIS–VIKOR and COPRAS–ARAS) and stability across over 85% of sensitivity simulations. The findings validate that the methodology effectively guides resource allocation towards interventions yielding maximum social impact and demonstrate its transferability to complex urban supply chain contexts, such as logistics microhubs. Ultimately, this replicable and adaptable model supports the transition towards more equitable, resilient urban systems, aligning directly with Sustainable Development Goal 11 (Sustainable Cities and Communities). Full article

The northern coast of Mauritania presents a strategic opportunity for clean energy investment due to its remarkable potential for green hydrogen production through wind energy. To determine the best location for wind-based green hydrogen production, this paper established a Multi-Criteria Decision-Making framework (MCDM) that combines the Analytic Hierarchy Process (AHP) and Evidential Reasoning (ER) to assess five coastal sites: Nouakchott, Nouamghar, Tasiast, Boulanoir, and Nouadhibou. Four main criteria (i.e., economic, technical, environmental, and social) and twelve sub-criteria were taken into account in the assessment. To ensure reliability and contextual accuracy, the data used in this study were obtained from geographic databases, peer-reviewed literature, and structured expert questionnaires. The results indicate that site 5 (Nouadhibou) is the most suitable location for green hydrogen generation using wind energy. Sensitivity analysis confirms the robustness of the ranking results, validating the reliability of the proposed hybrid framework. The findings of this study provide critical, data-driven decision-support insights for investors and policymakers, guiding the strategic development of sustainable wind-based green hydrogen projects along Mauritania’s coastline. Full article

Scaling Agile in industrial software projects is a process control problem that must balance governance, scalability, and adaptability while keeping decisions auditable. We present a hybrid fuzzy multi-criteria decision-making (MCDM) framework that combines Fuzzy Analytic Hierarchy Process (FAHP) for uncertainty-aware weighting with a tunable VIKOR–PROMETHEE ranking stage. Weighting and ranking are kept distinct to support traceability and parameter sensitivity. A three-layer hierarchy organizes twenty-two criteria across organizational, project, group, and framework levels. In a single-enterprise validation with two independent expert panels (n = 10 practitioners), the tuned hybrid achieved lower rank error than single-method baselines (mean absolute error, $\mathrm{MAE}$ = 1.03; Spearman $\rho$ = 0.53) using pre-specified thresholds and a transparent $\alpha +\beta$ = 1 control. The procedure is practical for process governance: elicit priorities, derive fuzzy weights, apply the hybrid ranking, and verify stability with sensitivity analysis. The framework operationalizes modeling, optimization, control, and monitoring of scaling decisions, making trade-offs explicit and reproducible in industrial settings. Full article

Financial institutions face increased reputational, regulatory, and ethical risks as the frequency and complexity of Environmental, Social, and Governance (ESG) controversies increase. Whistleblowing mechanisms are essential in the context of institutional resilience and the mitigation of internal governance failures. This study quantifies the exposure of 364 European financial institutions to a variety of ESG controversies to assess the effectiveness of whistleblowing during the fiscal year 2024. A whistleblowing performance index that captures the relative influence of ESG-related risk factors—such as corruption allegations, environmental violations, and executive misconduct—is constructed using a hybrid Multi-Criteria Decision-Making (MCDM) framework that is based on Entropy Weighting and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). The results emphasize that the perceived efficacy of whistleblower systems is substantially influenced by the frequency of media-reported controversies and the presence of robust anti-bribery policies. The study provides a data-driven, replicable paradigm for assessing internal governance capabilities in the face of ESG risk pressure. Our findings offer actionable insights for regulators, compliance officers, and ESG analysts who are interested in evaluating and enhancing ethical accountability systems within the financial sector by connecting the domains of financial risk management, corporate ethics, and sustainability governance. Full article

The acceleration of financial innovation and pro-crypto regulations in the digital asset space have spurred interest in cryptocurrencies among funds, and institutional and retail investors. Like any risky assets, investment in digital assets offers opportunities in terms of returns and challenges in terms of risk. However, unlike traditional assets, digital assets like cryptocurrencies are highly volatile. Accordingly, applying conventional single-criterion financial metrics for portfolio construction may not be sufficient as the method falls short in capturing the complex, multidimensional risk-return dynamics of innovative financial assets like cryptocurrencies. To address this gap, this study introduces a novel, integrated hybrid Multi-Criteria Decision-Making (MCDM) framework that provides a structured, transparent, and robust approach to cryptocurrency fund selection. The framework seamlessly integrates three well-established operations research methodologies: the Decision-Making Trial and Evaluation Laboratory (DEMATEL), the Analytic Network Process (ANP), and the Vlse Kriterijumsk Optimizacija I Kompromisno Resenje (VIKOR) algorithm. DEMATEL is utilized to map and analyze the intricate causal interdependencies among a comprehensive set of evaluation criteria, categorizing them into foundational “cause” factors and resultant “effect” factors. This causal structure informs the ANP model, which computes precise criterion weights while accounting for complex feedback and dependency relationships. Subsequently, the VIKOR algorithm is invoked to use these weights to rank cryptocurrency fund alternatives, delivering a compromise between optimizing group utility and minimizing individual regret. To illustrate the application and efficacy of the proposed method, a diverse set of 20 cryptocurrency funds is analyzed. From the analysis, it is shown that foundational criteria, such as “Fee (%)” and “Annualized Standard Deviation,” are the primary causal drivers of financial performance outcomes of funds. This proposed framework supports strategic capital allocation in a rapidly evolving domains of digital finance. Full article

Improving ship energy efficiency has become a critical priority for reducing fuel consumption and meeting international decarbonization targets. In this study, eight major groups of energy efficiency improvement systems—including wind and solar energy technologies, hull and propeller modifications, air lubrication, green propulsion options, waste heat recovery, and engine power limitation—were evaluated against seven critical success factors. A hybrid neutrosophic fuzzy multi-criteria decision-making (MCDM) framework was employed to capture expert uncertainty and prioritize alternatives. Neutrosophic fuzzy sets were adopted because they more comprehensively represent uncertainty—simultaneously modeling truth, indeterminacy, and falsity, providing superior capability to address expert ambiguity compared with classical fuzzy, intuitionistic fuzzy, gray, or other uncertainty-handling frameworks. Trapezoidal Neutrosophic Fuzzy Analytic Hierarchy Process (AHP) (TNF-AHP) was first applied to determine the relative importance of the criteria, highlighting fuel savings and cost-effectiveness as dominant factors with 38% weight. Subsequently, the Fuzzy Combined Compromise Solution (F-CoCoSo) method was used to rank the alternatives. Results indicate that solar energy systems and wind-assisted propulsion consistently rank highest (with 3.35 and 2.92 performance scores) across different scenarios, followed by green propulsion technologies, while waste heat recovery and engine power limitation show lower performance. These findings not only provide a structured assessment of current technological options, but also offer actionable guidance for shipowners, operators, and policymakers seeking to prioritize investments in sustainable maritime operations. Full article

Decision-making is increasingly guided by the integration of Multi-Criteria Decision-Making (MCDM) and Machine Learning (ML) approaches. Despite their complementary strengths, the literature lacks clarity on which forms of integration exist, what contributions they offer, and how to determine the most effective form for a given decision problem. This study systematically investigates integration modes through a methodology that combines a literature review, expert judgment, and statistical analyses. It develops a novel categorization of integration modes based on methodological characteristics, resulting in five distinct modes: sequential approaches (ML → MCDM and MCDM → ML), hybrid integration (MCDM + ML), and performance comparison approaches, including ML vs. MCDM and ML vs. ML evaluated through MCDM. In addition, new evaluation criteria are introduced to ensure rigor, comparability, and reliability in assessing integration forms. By applying correspondence, cluster, and discriminant analyses, the study reveals distinctive patterns, relationships, and gaps across integration modes. The primary outcome is a novel evidence-based framework designed to guide researchers and practitioners in selecting the appropriate integration modes based on problem characteristics, methodological requirements, and application context. The findings reveal that sequential approaches (ML → MCDM and MCDM → ML) are most appropriate when efficiency, structured decision workflows, bias reduction, minimal human intervention, and the management of complex multi-variable decision problems are key objectives. Hybrid integration (MCDM + ML) is better suited to dynamic and data-rich environments that require flexibility, continuous adaptation, and a high level of automation. Performance comparison approaches are most appropriate for validation-oriented studies that evaluate outputs (MCDM[ML vs. ML]) and benchmark alternative methods (ML vs. MCDM), thereby supporting reliable method selection. Furthermore, the study underscores the predominance of integration modes that combine value-based MCDM methods with classification-based ML algorithms, particularly for enhancing interpretability. Environmental science and healthcare emerge as leading domains of adoption, primarily due to their high data complexity and the need to balance diverse, multi-criteria stakeholder requirements. Full article

The rapid proliferation of Industry 4.0 technologies has created an urgent need for intelligent and reliable predictive maintenance (PdM) systems. While multi-criteria decision-making (MCDM) frameworks like the Best–Worst Method (BWM) offer structured approaches for prioritizing maintenance tasks, their traditional reliance on subjective expert opinion limits their scalability and adaptability in dynamic industrial settings. This study addresses these limitations by introducing a robust, data-driven framework that integrates machine learning (ML) with BWM. This study presents a framework integrating ML models with BWM, an MCDM technique. While prior work has explored ML for fault detection/classification and hybrid MCDM + ML approaches, our innovation lies in automating BWM weight calculation via ML-derived feature importances, transforming tacit expert knowledge (traditionally subjective) into explicit, data-driven criteria weights aligned with Knowledge Management (KM) principles. The proposed methodology moves beyond a single-model proof-of-concept to present a comprehensive validation blueprint for industrial deployment. The framework’s efficacy is demonstrated using the standard Case Western Reserve University (CWRU) dataset, where rigorous cross-validation and statistical significance testing identified the optimal model, offering a compelling balance of high stability and efficiency for adaptive systems. Furthermore, simulations demonstrated the framework’s real-time viability, with low processing latency, and its resilience to concept drift through an adaptive retraining strategy. By integrating the empirically validated model’s feature importances into the BWM, this work establishes an objective, data-driven, and adaptive system for prioritizing maintenance, thereby advancing the transition toward autonomous and self-optimizing industrial ecosystems. Full article

This study aims to identify the most suitable slag waste-filled polymer composite for automotive braking applications. It employs a hybrid multi-criteria decision-making (MCDM) model that integrates the “method based on the removal effects of criteria” (MEREC) and the “root assessment method” (RAM) method. Eight slag waste-filled polymer composites, evaluated using seven performance-defining criteria, were considered in the MCDM analysis. The performance evaluation criteria included the friction coefficient, wear, friction fluctuations, friction stability, fade-recovery aspects, and rise in disk temperature. The criteria were weighted through the MEREC approach, which identified fade% (0.2890) and wear (0.2829) as the most important attributes in the assessment. The RAM was employed to rank the alternatives and suggested that the composite alternative with 60 wt.% slag waste and 5 wt.% coir fiber proved to be the best composition for automotive braking applications. The results were validated using nine MCDM models and Spearman correlation coefficients, which showed that the ranking of alternatives was consistent and stable even when the normalization steps of MEREC were swapped. Statistical validation demonstrated a strong predictive accuracy (p < 0.05) with a strong correlation coefficient (>0.8) alongside a minimal mean absolute error. Furthermore, sensitivity analysis was performed by examining several weight situations to determine whether the priority weights influenced the ranking of the composite alternatives. The findings from both the correlation and sensitivity analyses confirm the proposed hybrid MEREC-RAM model’s consistency and effectiveness. Full article

Rapid progress in sustainable and intelligent transportation has intensified interest in smart airport initiatives, driven by the need to support environmentally responsible and technology-enabled aviation development. As complex sociotechnical subsystems of smart aviation, smart airports integrate advanced digital, operational, and organizational technologies to enhance efficiency, resilience, and passenger experience. With increasing emphasis on such transformations, multiple strategic development plans have emerged, each with distinct priorities and implementation pathways, which necessitates a rigorous and transparent evaluation mechanism to support informed decision-making under uncertainty. This study proposes an integrated spherical fuzzy multi-criteria decision-making (MCDM) framework for assessing and ranking smart airport development plans. Subjective expert judgments are modeled using spherical fuzzy sets, allowing for the simultaneous consideration of positive, neutral, and negative membership degrees to better capture linguistic and ambiguous information. Expert importance is determined through a hybrid weighting scheme that combines a social trust network model with an entropy-based objective measure, thereby reflecting both relational credibility and informational contribution. Criterion weights are computed through an integrated approach that merges criteria importance through the inter-criteria correlation (CRITIC) method with the stepwise weight assessment ratio analysis (SWARA) method, balancing data-driven structure and expert strategic preferences. The weighted evaluations are aggregated using a spherical fuzzy extension of the combined compromise solution (CoCoSo) method to obtain the final rankings. A case study involving smart airport development planning in China is conducted to illustrate the applicability of the proposed approach. Sensitivity, ablation, and comparative analyses demonstrate that the framework yields stable, discriminative, and interpretable rankings. The results confirm that the proposed method provides a reliable and practical decision support tool for smart airport development and can be adapted to other smart transportation planning contexts. Full article

The increasing global emphasis on sustainable transportation drives the need for strong electric vehicle (EV) charging networks. While national plans set high targets for EV adoption, translating these into practical infrastructure placement poses a significant hurdle. This study tackles this by creating detailed maps to show suitable locations for EV charging stations (EVCS) across the Attica region of Greece. Our main approach combines Geographic Information System (GIS) with Multi-Criteria Decision-Making (MCDM), specifically using the Analytic Hierarchy Process (AHP). After reviewing existing research to find important location factors, we adjusted these to fit the unique urban and social features of metropolitan Athens. We established four main criteria, accessibility, social, energy, and environmental, which were then divided into nine sub-criteria for our analysis. We developed four different models, each applying a unique weighting to these criteria (basic, energy-focused, environmental, and social) to see how various planning goals affect spatial outcomes. These models generated graded suitability maps, highlighting areas with high potential for new infrastructure. Central Athens consistently showed the highest suitability, which matches current research and confirms our method’s reliability. This work provides a useful, repeatable framework for local governments to strategically deploy EVCS, supporting urban planning and helping meet national goals for decarbonization and air quality. Full article

The rapid integration of generative artificial intelligence (AI) into newsroom workflows has transformed journalistic writing. Still, selecting reliable co-writing tools remains a multi-criteria challenge as it involves technical, ethical, and economic trade-offs. This study develops a hybrid multi-criteria decision-making (MCDM) framework that integrates the Measurement of Alternatives and Ranking according to the Compromise Solution (MARCOS) model with Entropy, CRITIC, MEREC, CILOS, and Standard Deviation objective weighting methods fused through the Bonferroni operator to reduce subjectivity and enhance robustness. Nine generative AI tools, including ChatGPT, Claude, Gemini, and Copilot, were evaluated against sixteen benefit- and cost-type criteria encompassing accuracy, usability, transparency, risk, and scalability. The decision matrix was normalized and benchmarked against ideal and anti-ideal profiles. The MCDM model was validated through correlation and sensitivity analyses using Spearman’s and Kendall’s coefficients. The results indicate that Gemini and Claude achieved the highest overall performance due to superior factual accuracy, transparency, and workflow integration, while ChatGPT demonstrated high linguistic versatility. The hybrid model achieved a stability index above 0.9 across perturbation scenarios, confirming its consistency and reliability. Overall, the proposed MARCOS–objective weight framework provides a mathematically transparent and reproducible decision protocol for newsroom technology evaluation, supporting evidence-based selection of generative AI co-writing systems. Full article

In this research, the impact of applications on improving urban logistics was examined using the example of the company EX, with an emphasis on the sustainability of its business. To conduct this research, expert decision-making was used. The model used ten criteria and eight applications. To incorporate uncertainty into this research, an intuitionistic fuzzy approach was used. Based on the obtained CC values, the criteria weights were determined using the SiWeC (Simple Weight Calculation) method, while the WASPAS (Weighted Aggregated Sum Product Assessment) method ranked the applications. The results showed that “Security and data protection” and “System reliability and stability” were the most important criteria, while Application 1 achieved the best results. These results were confirmed by the consistency analysis of the WASPAS method and the sensitivity analysis, which considered 30 scenarios. Full article

Shading system design is a complex, multi-objective optimization problem that requires balancing interdependent economic, environmental, social, energy, architectural, and daylighting factors, while also integrating decision-makers’ preferences and user satisfaction. This study aims to develop and validate a hybrid decision-support framework that addresses both quantitative and qualitative data under uncertainty to improve shading system performance. This paper proposes a novel framework that integrates fuzzy logic with multi-criteria decision-making (MCDM) methods. The Fuzzy Analytic Hierarchy Process (Fuzzy-AHP) is employed for criteria prioritization, whereas the Fuzzy Quality Function Deployment (Fuzzy-QFD) translates customer needs into technical requirements. Two evolutionary algorithms, the Single-Objective Genetic Algorithm (SOGA) and the Non-Dominated Sorting Genetic Algorithm II (NSGA-II), were implemented and compared. The framework was validated through its application to an existing educational building in Mansoura, Egypt, evaluating both fixed and dynamic shading solutions. The results indicate that the proposed framework effectively translates customer requirements into design criteria and accurately identifies optimal shading solutions, with SOGA outperforming NSGA-II in optimization performance, while dynamic shading systems significantly enhance glare control and visual comfort, thereby confirming the framework’s efficiency in managing interdependent objectives under uncertain conditions. Overall, the framework provides a robust and systematic methodology for incorporating customer satisfaction into shading design and advancing sustainable building performance. Full article

This paper assesses the green tourism readiness of six EU member states from Central and Eastern Europe—Slovenia, Croatia, Slovakia, Hungary, Bulgaria, and Romania—using a hybrid multi-criteria decision-making (MCDM) model. As tourism sectors face increasing pressure to align with the European Green Deal and sustainability goals, integrating green skills, environmental protection, and institutional governance becomes essential. The study applies a three-step framework that combines the Analytic Hierarchy Process (AHP), Best-Worst Method (BWM), and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to evaluate national performance across four criteria: natural capital, rural infrastructure, governance readiness, and green skills in vocational education and training (VET). Results show that environmental sustainability and governance are the dominant enablers of green tourism transformation, with Slovenia and Croatia leading in overall readiness. Although green skills have a lower relative weight, their integration significantly strengthens performance in more advanced systems. The hybrid model demonstrated methodological robustness through sensitivity and consistency checks. This research contributes to both methodological innovation and evidence-based policymaking by offering a replicable tool for evaluating sustainable tourism development in transition economies. It provides actionable insights for aligning education, tourism, and environmental policy within the broader EU green transition framework. Full article