Search Results (915)

Visual programming using blocks and diagrams facilitates understanding of fundamental programming concepts, which is particularly important for children with special educational needs because it reduces their cognitive load and encourages interactive learning. This study aimed to develop and apply a hybrid multi-criteria framework to evaluate, rank, and select visual programming software solutions intended for children with special educational needs. Based on an analysis of the educational context and the target population’s needs, a set of criteria was defined to evaluate and select the most suitable software solution. Data for the analysis were collected using a structured questionnaire, from which a decision matrix was developed. Within the proposed hybrid multi-criteria decision-making (MCDM) framework, criterion weights were determined using the Rank Order Centroid (ROC) method, and the ranking of alternatives was performed using the Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE II). Additionally, a sensitivity analysis was conducted to assess the stability and robustness of the obtained rankings in relation to changes in the criterion weights. The results indicate a stable ranking of alternatives and the identification of the most favorable solution in the majority of scenarios. The projection quality of 91.1% in the Geometrical Analysis for Interactive Aid (GAIA) plane confirmed the reliability of the visual interpretation of the results. The proposed framework improves the decision-making process and provides a foundation for further research in educational software evaluation. Full article

The growth of advanced cyber threats has inspired organisations to start using powerful cybersecurity platforms, but the process of selection is analytically challenging due to the multidimensional, uncertain, and conflicting character of the evaluation criteria. The prevailing culture of decision-support frameworks is based on unyielding numerical evaluations that cannot reflect the underlying vagueness of expert judgment and the dynamic interplay of macro-environmental factors. This paper presents a combined Fuzzy Multi-Criteria Decision-Making (FMCDM) system, which uses polygonal fuzzy numbers, in particular pentagonal fuzzy representation, and four other complementary methods of MCDM (Fuzzy AHP, Fuzzy TOPSIS, Fuzzy VIKOR, and Fuzzy COPRAS), integrated by a Borda Count consensus system. Sixteen assessment sub-criteria are logically obtained through an analysis of PESTEL (Political, Economic, Social, Technological, Environmental, and Legal) and weighted using the Fuzzy Analytic Hierarchy Process. The model is used to compare six cybersecurity platforms, including Microsoft Security Framework, CrowdStrike Falcon, Cisco Cybersecurity Portfolio, Palo Alto Networks Cortex, Fortinet Security Fabric, and Sophos Central. In this study, Fuzzy AHP demonstrates that the aggregate weight of political factors is the highest (0.4181), followed by cross-border data management, regulatory compliance, and government incentives as the most popular sub-criteria. According to the results from the Fuzzy TOPSIS, Fuzzy VIKOR, and Fuzzy COPRAS methods, Microsoft Security Framework ranks consistently in the first place, and CrowdStrike Falcon and Cisco Cybersecurity Portfolio were ranked second and third, respectively. The framework presented in the study provides decision-makers with a reproducible, uncertainty-conscious basis for cybersecurity platform selection. Full article

The move to sustainable energy systems has increased the requirement for comprehensive decision support frameworks that are uncertainty-aware to guide the selection of solar power plant sites. The rapid growth of investments in solar energy has increased the demand for systematic and accurate decision-support tools to choose the best sites for photovoltaic (PV) power facilities. The selection of solar power plant sites is a complicated multi-criteria decision-making (MCDM) problem that involves technical, economic, environmental, social, and technological aspects. The process is typically associated with ambiguity and incomplete knowledge of experts. To overcome these problems, this paper offers an interval-valued neutrosophic fuzzy TOPSIS (IVN-TOPSIS) method, which extends the standard TOPSIS methodology by including truth, indeterminacy, and falsity membership degrees as interval values. The methodology is utilized in a real case study in the Mediterranean region of Türkiye, comprising three provinces with great potential: Antalya, Mersin, and Adana. An assessment of a complete set of environmental, economic, social, and technological criteria is performed using expert judgments stated in interval-valued neutrosophic language assessments. They were incorporated into a Geographic Information System (GIS) to produce a suitability map indicating the most suitable sites for the facility. The suggested approach is different from the traditional crisp or fuzzy MCDM techniques since it clearly models the degrees of truth, indeterminacy, and falsehood, thus providing a more detailed representation of the expert evaluations. According to the data, Mersin is the most ideal site for the construction of a solar power plant, followed by Antalya, and the least suitable site is Adana. The results suggest that sustainable solar energy planning must go beyond technical resource potential and include integrated and uncertainty-aware assessments. The suggested IVN-TOPSIS framework can serve as a powerful decision-support tool to policymakers, planners, and investors that wish to encourage regionally balanced and sustainable renewable energy development. Full article

This study aims to improve wagon unloading processes in a real industrial context characterized by operational variability, process constraints, and strict performance requirements. Traditional decision-making approaches in such contexts often rely on single performance indicators, which may lead to suboptimal and less sustainable decisions. In line with Industry 5.0 principles, which emphasize human-centricity, resilience, and sustainability, this paper proposes a multi-criteria decision framework to support more balanced and adaptive operational decisions. A real-world case study based on anonymized industrial data is used to evaluate different arrival-track operational configurations. The proposed model considers several indicators, including unloading time, throughput, tonnage, process variability, operational losses, and a proxy of operator exposure. To strengthen the human-centric dimension, an Operational Handling Exposure Proxy (OHEP) was introduced to capture manoeuvre-related operator exposure during wagon handling and batch repositioning. A weighted scoring system was then used to identify the most balanced configuration by considering trade-offs between performance, stability, losses and operator exposure. The results show that the arrival-track operational configuration influences loss structure, process stability and overall decision ranking more than direct throughput alone. Track 2 provides the best overall trade-off under the baseline MCDM weighting scheme, while Track 3 may become preferable when wagon-loss minimization is prioritized. The findings highlight the importance of integrating variability and human-centered indicators into industrial decision-making processes. In future work, the proposed framework could be extended using data-driven methods and machine learning to support predictive and adaptive optimization in Industry 5.0 environments. This study contributes to the literature by integrating real-world industrial analysis, multi-criteria decision-making, and sustainability-oriented optimization into a single decision support framework. Full article

Supplier selection in healthcare is a complex multi-criteria decision-making (MCDM) challenge requiring a balance of sustainability, resilience, and operational efficiency. Traditional methods struggle with scalability and subjectivity when applied to large administrative datasets. This study introduces a transparent hybrid Machine Learning–MCDM (ML–MCDM) framework, validated using a U.S. Medicare dataset of 661 suppliers. The framework integrates eXtreme Gradient Boosting (XGBoost) and SHapley Additive exPlanations (SHAP) for criterion prioritization, the Full Consistency Method (FUCOM) for mathematically consistent weighting, and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) for final ranking. As the dataset lacks direct sustainability metrics, seven indicators were synthetically generated; thus, the results serve as proof-of-concept demonstration of the framework’s architecture. Specifically, XGBoost–SHAP is trained to predict a synthetically constructed Overall Performance Score (OPS), meaning that the resulting feature importance output constitutes an algorithmic consistency check—confirming that the pipeline correctly recovers importance signals deliberately embedded in the training target. For interpretability, suppliers were segmented into five performance profiles via K-Means: Strategic Partners (17.7%), Green Leaders (18.6%), Reliable Emergency Suppliers (18.2%), Balanced Performers (20.4%), and Developing Suppliers (25.1%). Carbon Footprint Score (0.408) and Emergency Response Capability (0.316) achieved the highest feature importance. FUCOM-derived weights prioritized On-Time Delivery Rate (0.272), Carbon Footprint Score (0.222), and Emergency Response Capability (0.220). The top supplier attained a TOPSIS closeness coefficient of 0.800, showing strong discrimination. Sensitivity analysis across four scenarios confirmed ranking robustness, maintaining Spearman correlations ρ ≥ 0.977. This ML–FUCOM–TOPSIS approach provides an auditable, scalable, and policy-relevant decision-support tool, enabling procurement managers to navigate high-dimensional data while ensuring operational continuity and environmental responsibility in healthcare supply chains. Full article

The significant growth in e-commerce has brought intense competition to the business sector. Businesses seeking to stand out in this competition are evaluated by customers not only by the quality of the products they sell, but also by their performance on digital platforms. To measure this evaluation, this study analyzed the website performance of 25 e-commerce companies in Türkiye using Multi-Criteria Decision Making (MCDM) methods with eleven technical and user-oriented performance criteria. MAXC (Maximum Criterion) and Skewness Impact Through Distributional Evaluation (SITDE) methods were used to determine the criterion weights of the websites, while CORASO (COmpromise Ranking from Alternative SOlutions) was used to rank the alternatives. According to the results, “total number of visitors” had the highest weight, while “bounce rate” had the lowest weight. According to the CORASO method, the top three performing e-commerce sites were EC1, EC2, and EC3, while the bottom three performing sites were EC11, EC14, and EC20. Full article

Choosing the most suitable tractor is a complex and high-stakes decision where technical performance, financial capability, and sustainability considerations must be balanced. However, tractor selection in existing studies lacks objective, sustainability-oriented evaluation frameworks, leaving farmers vulnerable to potentially poor investments with long-term economic, operational, and environmental impacts. Therefore, this research proposes a software-based Decision Support System (DSS) that incorporates objective multi-criteria decision-making (MCDM) models within a management control perspective focused on sustainability and provides a clear, data-driven method for tractor selection for small farmers. Four popular tractor models in Egypt were selected for evaluation based on three criteria related to sustainability: power (C1), purchase price (C2), and availability of maintenance and spare parts (C3). Subsequently, a DSS was implemented using Python, and five MCDM methods—CRITIC, MEREC, Entropy, Standard Deviation (SD), and TOPSIS—were used to select the tractor that best meets sustainability objectives. The findings indicate that tractor T2, which had the lowest purchase price (USD 12,390) and enough power (60 HP), was the best-rated tractor. The impact of each criterion varied by method: C1 was the most important in the Entropy method (0.3657), while C2 was the most important in the CRITIC (0.5552), MEREC (0.3432), and SD (0.5938) weightings. The proposed DSS improves transparency and supports more informed, evidence-based decisions in agricultural mechanization. Overall, the system offers a practical and scalable tool that helps smallholder farmers and policymakers make sustainable tractor choices, contributing to progress toward SDGs 2, 7, 12, and 13. Full article

Efficient allocation of capital toward environmental, social, and governance (ESG) objectives has become a critical challenge for emerging economies pursuing sustainable development and financial resilience. While prior research has primarily focused on ESG investment volumes, considerably less attention has been devoted to the efficiency with which financial and institutional systems transform capital into measurable sustainability outcomes. This study introduces the concept of ESG-Oriented Capital Allocation Efficiency (ECAE) and develops a hybrid multicriteria decision-making (MCDM) framework to evaluate its performance across 24 emerging market economies during the period 2021–2025. The proposed framework integrates DEMATEL, ANP, entropy weighting, TOPSIS, and VIKOR methods to capture causal relationships, interdependencies, weighting structures, and comparative efficiency rankings. The results identify governance effectiveness, ESG policy stability, and regulatory quality as the most influential drivers of ECAE, while higher ESG investment volumes alone do not necessarily generate superior sustainability outcomes. Sensitivity analysis confirms the robustness of the ranking results across alternative weighting scenarios. The findings suggest that strengthening institutional quality, policy coherence, and governance effectiveness is essential for improving sustainable finance outcomes. The study contributes to the sustainable finance literature by providing a policy-oriented framework for evaluating how effectively emerging market economies translate ESG-oriented capital into tangible sustainability performance. Full article

Face milling of aluminum 7075 hybrid metal matrix composites with 10 wt.% TiO₂ and 3 wt.% graphite (HMMCs) are needed to improve performance and sustainability. This study focuses on optimizing the milling process for Al7075 HMMCs using the desirability approach and advanced multi-criteria decision-making (MCDM) methodologies, including the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and the Combined Distance-based Assessment (CODAS). Surface roughness (SR), cutting force (CF), carbon emissions (CE), and energy consumption (EC) were systematically evaluated and ranked using the L₁₈ Taguchi Orthogonal Array. Minimum Quantity Lubrication (MQL) and cryogenic CO₂ cooling techniques were used to achieve a superior surface finish and reduce friction at the tool-workpiece interface, thereby minimizing scratches and thermal damage. Desirability evaluation results showed the optimal machining conditions for milling of Al7075 (HMMCs) occurred at a cutting speed (V_c) of 200 m/min, a feed rate (f) of 0.02 mm/rev, and a depth of cut (a_p) of 0.3 mm, proving the potential of integrating MCDM tools with effective cooling strategies. The desirability method favored a balanced compromise, while entropy-weighted TOPSIS/CODAS emphasized energy and carbon-related responses. Improvements of 6% in cutting force, 7% in surface roughness, and a 7% reduction in energy consumption, along with 8% lower carbon emissions, were achieved, demonstrating the effectiveness of hybrid cooling strategies in promoting eco-friendly and resource-efficient processes. Full article

Electronic textiles (e-textiles) have gained significant importance due to their potential to enable wearable electronic systems. Conductive pathways in textiles can be fabricated using various approaches; among these, printing technologies stand out for their cost-effectiveness and suitability for rapid design customization. In this study, conductive patterns were produced on 100% cotton woven fabrics using rotary screen printing with different conductive paste formulations and printing layer configurations. The electrical resistance, fabric thickness, microscopic surface morphology, tensile strength, elongation, and tearing strength of the printed e-textiles were evaluated. Results indicated that resistance decreased with increasing printed track width and number of printed layers, with samples A4 and A5 exhibiting the highest conductivity. Thickness measurements and microscopic surface images showed that repeated printing increased layer build-up and surface coverage, particularly for A3 and A4. Mechanical performance tests revealed reductions in tensile strength, elongation, and tear strength after printing, attributed to restricted fiber mobility caused by the conductive paste and curing process. Despite these reductions, the mechanical property losses remained within acceptable limits for wearable applications. To determine the most suitable conductive textile for use in electronic textile product design, the Multi-Objective Optimization on the Basis of Ratio Analysis (MOORA) method, a multi-criteria decision-making (MCDM) approach, was applied using mechanical performance criteria. Electrical resistance was evaluated separately as a functional performance indicator and interpreted together with the MOORA-based mechanical ranking. Considering both mechanical and electrical performance, sample A5 was identified as the optimal alternative. Overall, this study demonstrates that printed conductive textiles can be systematically evaluated and ranked using a multi-criteria decision-making approach for material selection in wearable electronics. Full article

Background: Circular supply chains (CSC) have emerged as a strategic response to sustainability challenges, while adoption remains uneven. Supplier selection is a key driver of effectiveness, shaped by organizational capabilities, institutional support, and leadership. This study develops a structured framework for circular supplier selection (CSS) using a hybrid multi-criteria decision-making approach, addressing fragmented research and strengthening the link between methodological innovation and practice. Methods: The proposed framework integrates fuzzy DEMATEL, the Best-Worst Method (BWM), and the Analytic Hierarchy Process (AHP) within MCDM. Fuzzy DEMATEL identifies cause-and-effect relationships among criteria, distinguishing net causes from net effects. The most influential and dependent criteria serve as anchors for the BWM weighting, followed by AHP to evaluate sub-criteria and alternatives. Results: Environmental governance emerged as the most influential driver in the causal analysis, while circular performance received the highest weight in BWM. The final AHP evaluation ranked Alternative 5 as the most suitable, followed by A9 and A3, confirming the framework’s ability to deliver consistent, actionable insights for circular supplier selection. Conclusions: This integration enables a more granular and robust evaluation of supplier strategies within CSC, reinforcing their role in accelerating sustainability transitions. It establishes a structured framework for CSS, highlighting CSS performance and upstream supply chain decision-making. Full article

The growing adoption of distributed renewable energy systems (DRES) calls for advanced planning methodologies capable of addressing their inherent complexity and multi-dimensional trade-offs. Multi-Criteria Decision-Making (MCDM) frameworks are widely used to balance diverse objectives, but their effectiveness depends heavily on the selection of criteria, weighting techniques, and integration methods. This paper undertakes a systematic review of the existing literature to analyze how MCDM approaches have been applied in the planning and optimization of DRES projects. The review focuses on the criteria considered in MCDM, the techniques used to assign their relative importance, and the methods employed to integrate these weights into multi-objective evaluations. The analysis draws from a diverse set of peer-reviewed papers, examining economic, technical, environmental, and social dimensions, as well as the relationships between project-specific features and the criteria selection process. Results show that social criteria remain underrepresented both in terms of frequency and of relative importance in the evaluation process, while economic criteria are the most used and influential, underlining the need for more balanced, context-sensitive, and socially inclusive MCDM frameworks. Among MCDM methods and weighting methods, TOPSIS and AHP are by far the most common approaches, respectively. This review provides a foundation for future research aimed at improving the adaptability and effectiveness of MCDM frameworks in DRES. Full article

This study proposes an integrated Kano–QFD–MCDM decision-support framework for sustainable fiber selection in sportswear by combining user-oriented evaluation with environmental and technical performance criteria. User requirements are classified using the Kano model and translated into technical criteria through Quality Function Deployment (QFD), enabling criterion weights that reflect both user priorities and product performance. Six fiber alternatives—soy, bamboo, Tencel, modal, cotton, and polyester—are evaluated based on nine criteria covering mechanical properties, comfort-related attributes, environmental impact, and economic factors, with emphasis on carbon footprint and biodegradability. The evaluation is performed using three multi-criteria decision-making (MCDM) methods—TOPSIS, EDAS, and VIKOR—supported by statistical analyses including Spearman correlation and the Friedman test to assess ranking consistency. Results indicate that soy fiber achieved the highest aggregated ranking under the proposed weighting and aggregation framework, followed by bamboo and Tencel, while polyester ranks lowest due to its environmental disadvantages. Overall, the proposed framework provides a transparent and statistically validated approach that integrates sustainability considerations into fiber selection, supporting more informed and responsible decision-making in sportswear design. Full article

Selecting the right energy storage system (ESS) for grid integration is a high-stakes decision involving conflicting technical, economic, environmental, and risk criteria under deep uncertainty. The existing fuzzy multi-criteria decision-making (MCDM) methods either fail to capture neutral or abstaining expert judgments or treat evaluation criteria as independent, which is an unrealistic assumption in complex engineering decisions. To address both limitations simultaneously, this study develops four new aggregation operators by extending the Bonferroni mean (BM) into the q-rung picture fuzzy sets (q-RPFSs) framework: the q-RPFBM-based, q-RPFWBM-based, q-RPFGBM-based, and q-RPFWGBM-based operators. Unlike the existing q-RPFS operator families (Dombi, Frank, Fermatean, Yager, Maclaurin), which aggregate criteria independently, BM-based operators explicitly model pairwise interactions among criteria with a structurally distinct aggregation logic that is especially critical when criteria such as cost, risk, reliability, and environmental impact are mutually correlated. The theoretical validity of the operators is confirmed through proofs of idempotency, monotonicity, and boundedness. Applied to a comprehensive ESS selection problem for Türkiye (covering nine alternatives across nineteen sub-criteria and five main criteria, including an explicit risk dimension), the framework consistently identifies pumped hydro storage as the optimal choice. Sensitivity analyses under varying q, s, and t parameters, as well as perturbed criterion weights, confirm the robustness of this ranking. The proposed framework offers energy planners and decision-makers a principled and transparent tool for evaluating ESS under high uncertainty and criterion interdependence. Full article

This paper proposes a multi-criteria decision making (MCDM) model, namely, Weighted Product Evaluation Based on Distance from Average Solution (WPEDAS), to evaluate the financial performance of companies. The proposed WPEDAS model focuses on the weighted product approach, which is different from the existing Evaluation Based on Distance from Average Solution (EDAS) model that adopts a weighted sum approach based on distance from average solution. Besides that, we further enhance the model performance by developing a hybrid MCDM model. The proposed hybrid Weighted Aggregated Sum Product Evaluation Based on Distance from Average Solution (WASPEDAS) model is developed based on the weighted sum EDAS and the proposed WPEDAS. The proposed hybrid WASPEDAS model offers higher flexibility and robustness of customizing decision strategies based on the decision makers in solving MCDM problems. The proposed hybrid model is demonstrated using the financial ratios of companies in the Consumer Discretionary sector in the NASDAQ Exchange. The entropy weight method is integrated into the proposed models to determine the weights of decision criteria. Based on the results of sensitivity analyses, the proposed hybrid WASPEDAS model proves its reliability and robustness in performance evaluation. This implies that the proposed hybrid WASPEDAS model offers greater stability in ranking the companies, thus helping investors and fund managers in analyzing the companies during investment decision making. In addition, this study also provides guidance to the companies’ management teams in their strategic and tactical decision making to reduce volatility in driving the companies towards excellence. Full article