Search Results (2,191)

This article presents methods and hardware for the multi-criteria non-destructive determination of chicken egg quality parameters, implemented using a multifunctional rotary system. Unlike traditional single-criteria sorting, which relies primarily on weight, the proposed approach utilizes a combination of physical and geometric parameters, including weight, linear dimensions, cross-sectional area and perimeter, volume, density, and shape. The experimental framework for the study was formed by measuring the parameters of 750 chicken eggs, covering the entire range of product categories and morphological variations. Geometric parameters were determined using machine vision methods, weight was determined using a strain gauge, and derived parameters were calculated using formalized models. A multi-criteria evaluation algorithm based on fuzzy set theory was used to make the classification decision, accounting for overlapping feature ranges and regulatory differences between EU and EAEU standards. The results of statistical and correlation analysis showed that egg density is identified as a relatively independent diagnostic parameter, weakly correlated with weight and geometric characteristics, justifying its inclusion in the quality model. A comparison of manual and automatic classification revealed differences in boundary categories during single-criteria sorting and indicated the potential of a multi-criteria approach. The obtained results support the feasibility of the developed methods and hardware under the conditions of the present study. Full article

In this study, a hybrid multi-criteria decision-making (MCDM) model was developed for analyzing consumer adoption decisions regarding generative artificial intelligence (Gen AI). By extending the technology acceptance model (TAM) into a structured decision system, the proposed framework integrates ethical and risk-related criteria, including perceived cost, perceived risk, transparency, accountability, intellectual property concerns, and data privacy, into a formal causal and evaluative structure. First, a Delphi-based consensus process is employed to identify and refine key adoption criteria. Subsequently, the decision-making trial and evaluation laboratory (DEMATEL) method is applied to quantify causal relationships among these criteria and to construct an influence network revealing prominence and directional effects. In total, 251 questionnaires were distributed in Taiwan, and 231 valid responses were collected. The results indicated the decision-making factors that underlie the adoption of Gen AI by consumers. The results highlighted transparency as a dominant causal factor that significantly influences multiple ethical and functional dimensions of Gen AI adoption. To address uncertainty and vagueness in human judgment, fuzzy importance–performance analysis was also incorporated. Best non-fuzzy performance values were obtained through defuzzification, enabling the classification and prioritization of critical adoption factors within a four-quadrant decision matrix. The proposed framework provides a mathematically grounded decision-support model for elucidating the structural interdependencies among adoption criteria and to facilitate strategic decision making for Gen AI system design and governance. This study contributes to the MCDM and operations research literature by transforming a behavioral acceptance model into a formal decision-analytic framework, thereby enhancing the analytical rigor and applicability of TAM-based adoption studies in complex socio-technical systems. Full article

The TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method is one of the most widely used multi-criteria decision-making (MCDM) approaches in industrial, financial, and scientific fields. However, its sequential computational cost of O(m × n), where m denotes the number of alternatives and n the number of criteria, becomes prohibitive when decision matrices have several million rows. Despite its geometric interpretability and simplicity, classical TOPSIS faces two key computational bottlenecks at scale: (i) Euclidean distance calculations O(m × n) dominating the total cost, and (ii) the O(m × log m) sorting step, both inherently sequential and memory-bound on CPUs. To overcome these limitations, we propose GPU-TOPSIS, a fully vectorized and parallel reformulation of TOPSIS based on tensor execution on graphics processing units (GPUs), whose main contributions are: (i) a formally correct reformulation of TOPSIS as a GPU tensor pipeline preserving mathematical fidelity to the original method; (ii) a two-pass fragment-processing algorithm guaranteeing exact mathematical equivalence with monolithic TOPSIS, while reducing the memory footprint from O(m × n) to O(m_t × n), where m_t < m is the size of each independently processed fragment; (iii) three independent implementations on CuPy, PyTorch, and TensorFlow, ensuring the framework’s portability and genericity. Experimental evaluations on real data from the Amazon Products 2023 dataset, using matrices of up to 200 million alternatives (via the 2-pass formulation), demonstrate speedups of up to 4.75× compared to the reference CPU implementation (NumPy), with inter-backend score differences below 5 × 10⁻⁸ and 100% ranking overlap across all tested Top-K thresholds. A perturbation sensitivity analysis of the criteria weights and cross-backend consistency tests confirms that GPU acceleration fully preserves robustness and decision reliability, making GPU-TOPSIS a practical, open, and reproducible solution for large-scale multi-criteria decision making in Big Data environments. Full article

Distance measures play a crucial role in fuzzy decision-making, pattern recognition, and uncertainty modeling. However, some existing distance measures for Complex Picture Fuzzy Sets (CPiFSs) have shown limitations and may produce counterintuitive results in certain cases. Moreover, only a few studies have explored such measures. To overcome these issues, in this study, some novel measures of distance for CPiFSs are proposed to effectively handle two-dimensional uncertainty characterized by amplitude and phase components. The proposed measures are developed by integrating both magnitude and phase information in a unified mathematical framework, ensuring improved discrimination capability and structural consistency. We rigorously prove that the suggested measures fulfill the essential properties of a distance function. Additionally, the normalization characteristics and stability behavior are analytically examined to ensure robustness in practical implementations. The proposed measure of distance is then applied to a multi-criteria decision-making (MCDM) case study, where alternatives are evaluated under Complex Picture Fuzzy information to demonstrate its practical effectiveness and ranking consistency. Using a CPiFS-based TOPSIS framework, distances from the positive and negative ideal solutions are computed via the developed metric, and the relative closeness coefficient is employed to obtain a stable and discriminative ranking of alternatives. Furthermore, comparative analysis with several existing distance measures demonstrates the stability and superiority of the proposed method in distinguishing complex fuzzy information. Full article

This study presents a systematic bibliometric analysis and critical review of assessment indicators and multi-criteria decision-making methods for rural energy systems from 2010 to 2025. It examines the evolving definitions and regional variations in these indicators and methods. The research hotspots of rural energy systems have shifted from basic rural electrification to multi-dimensional assessment indicators and hybrid multi-criteria decision-making methods. The assessment indicators for rural energy systems demonstrate a marked imbalance, dominated by economic and technical dimensions. Specifically, economic evaluations for rural energy systems frequently utilize net present cost and levelized energy cost, shifting from static capital comparisons to comprehensive lifecycle assessments. Meanwhile, loss of power supply probability is identified as the primary inherent constraint among technical assessment indicators for rural energy systems. Geographically, assessment indicators for rural energy systems priorities exhibit significant divergence. Developing regions prioritize basic power supply and affordability, whereas developed regions focus on grid stability and market risk resilience. In addition, environmental evaluations for rural energy systems remain fixated on carbon emissions. Developed nations emphasize global climate benefits, while developing nations focus on localized dividends like indoor air quality improvement. Critically, despite an increasing focus on rural livelihoods, social indicators remain systematically marginalized in rural energy systems, leading to the neglect of local requirements and increasing technical risks. The field of rural energy system assessment is advancing toward multi-criteria decision-making indicators. Future methodologies must integrate robust, dynamic adaptive mechanisms that respond to evolving developmental priorities in order to effectively address inherent data scarcity and complex socio-economic uncertainties of rural energy systems. Full article

The accelerating electrification of public transport systems has increased the need for objective and transparent decision-support tools in electric bus (e-bus) procurement. Although multi-criteria decision-making (MCDM) approaches are frequently employed to evaluate e-bus alternatives, limited attention has been paid to the consistency of rankings produced by different objective weighting techniques. This study addresses this gap by proposing an integrated evaluation framework that combines the CRITIC-MARCOS and Entropy-MARCOS methods to assess e-bus alternatives against technical and operational criteria. Six e-bus models are evaluated using nine performance indicators structured as benefit and cost criteria, reflecting the procurement context of a central public transport authority in a large metropolitan area. Criterion weights are independently calculated using the CRITIC and Entropy approaches and subsequently integrated into the MARCOS method to generate alternative rankings. To examine the robustness of the results, a sensitivity analysis based on the TOPSIS and Average Ranking Methods is conducted. The findings indicate that the proposed framework produces consistent and stable rankings across different weighting techniques. These results suggest that integrating multiple objective weighting methods within an MCDM framework can enhance transparency and reliability in high-investment public transport procurement decisions and support strategic planning for low-carbon urban mobility. Full article

Mining plays a key role in economic development but faces increasing challenges in reconciling sustainability with social expectations in the territories where extractive activities operate. In regions with a strong mining presence, incorporating community perceptions has become essential for guiding sustainable development strategies. However, systematic evidence to prioritize these dimensions at the local level remains limited. In this context, the present study identifies and ranks critical sustainability factors from the perspective of communities located near mining projects in the Coquimbo Region, Chile. To structure the decision problem, the Analytic Hierarchy Process (AHP) was applied. This multi-criteria decision-making (MCDM) method integrates qualitative and quantitative judgments through pairwise comparison matrices processed using Expert Choice software, based on a hierarchical structure of criteria, subcriteria, and decision elements associated with social, economic, and environmental dimensions. The results indicate that the criterion with the highest global priority was “Improvement in health, social cohesion, and quality of life” (36.3%), followed by “Economic development” (20.3%) and “Local development and social participation” (15.7%). Among the most prioritized actions were “Construction of health facilities” (15.5%), “Promote the hiring of local labor” (8.7%), and “Protection and continuous monitoring of aquifers” (6.3%). Sensitivity analysis confirmed the stability of the model, suggesting that the proposed framework can support the systematic incorporation of community perceptions into the planning of mining sustainability strategies. Full article

In transport and logistics systems, decision-making is increasingly influenced by uncertainty stemming from demand variability, technological disruptions, and systemic risks present in supply chains. In these contexts, organizations need approaches that are rooted in data and analysis to assess key elements affecting system resilience and performance. Although current studies widely utilize stochastic and fuzzy models for operational decision-making, there has been insufficient focus on the systematic assessment of human-centric system elements—especially competencies—as decision variables in intricate logistics systems. This research proposes an analytical framework for multi-criteria decision-making that is driven by data and aimed at evaluating the significance of various competencies that affect labor market competitiveness and the adaptability of supply chains. The approach combines expert assessment with statistical and information-theoretic metrics, utilizing Kendall’s coefficient of concordance for evaluating consistency, Shannon entropy for analyzing distributional uncertainty, and the Gini coefficient for measuring concentration. This integrated method allows for the measurement of both variability and inequality within decision frameworks in the face of uncertainty. The findings indicate that hands-on experience and professional skills play a crucial role in decision-making structures, whereas the ability to adapt to technological advancements and a commitment to ongoing learning greatly enhance system resilience. The entropy results reveal a significant degree of structural balance in the decision criteria, while the low Gini values affirm a lack of concentration, indicating a distributed and multi-dimensional decision-making environment. The study provides analytical insights into the structure and relative importance of competencies in decision-making contexts related to supply chain resilience. Full article

Sustainable forklift technologies have become essential in modern industrial logistics due to increasing environmental regulations, rising energy costs, and heightened occupational safety requirements. Given the complexity and variety of sustainable forklift options, selecting the most appropriate one has become a critical multi-criteria decision-making (MCDM) problem for companies. This study aims to determine the most appropriate sustainable forklifts by considering multiple qualitative and quantitative criteria that play a critical role in the forklift selection process of companies. To this end, meetings are conducted with managers possessing expertise in sustainability and logistics at companies operating in Turkey. Based on these insights, ten forklift alternatives and six evaluation criteria are identified. This is the first time, in this study, sustainability criteria such as sustainability in occupational health and safety, sustainability in agility, sustainability in ergonomics, durability and material sustainability, sustainability in load lifting capacity and sustainability in price are incorporated into the evaluation. To the best of our knowledge, no study in existing literature has specifically focused on sustainable forklift selection, incorporating the comprehensive sustainability-oriented criteria considered in this study. The Analytic Hierarchy Process (AHP) is employed to determine the weight of each criterion. Subsequently, forklift alternatives are ranked using the Multi-objective Optimization by Ratio Analysis (MOORA) ratio approach, the Additive Ratio Assessment (ARAS), and the Elimination and Choice Translating Reality (ELECTRE) methods. Moreover, weights derived based on different subjective and objective weighting schemes, specifically FUCOM, BWM, and Entropy, as well as the resulting ranking outcomes are comparatively examined to assess the impact of varying weighting structures on the robustness and consistency of the final decision results. The proposed methodology is applied within manufacturing and logistics companies in Turkey to assess its practical effectiveness. As a result of this study, the most appropriate sustainable forklifts for the companies are identified. Furthermore, the outcomes of the applied methods yield consistent/similar results. The results emphasize that managers should place greater importance on the criteria of sustainability in occupational health and safety—identified as the most critical factor—followed by durability and material sustainability, and sustainability in load lifting capacity when selecting forklifts. Sensitivity analyses indicate that the method yields consistent and effective results. Moreover, it demonstrates the robustness and accuracy of the forklift evaluations. In this context, this study serves as a guided reference for companies in the selection of sustainable forklifts. Full article

The rapid expansion of air cargo transportation has necessitated fleet expansion to meet growing demand. Due to the high capital costs associated with new aircraft acquisitions, attention has increasingly shifted toward used aircraft as a cost-effective alternative. However, selecting an appropriate used aircraft from a range of heterogeneous options is a critical multi-criteria decision-making challenge. To address this issue, this study introduces an integrated decision-making framework for used aircraft selection by combining the technique for order preference by similarity to ideal solution (TOPSIS) and the best–worst method (BWM) in a hesitant fuzzy environment. First, in response to the limitations of existing distance measures, a novel distance measure for hesitant fuzzy sets (HFSs) is proposed that explicitly incorporates the hesitation degree to better capture uncertainty. Subsequently, this measure is incorporated into a modified hesitant fuzzy TOPSIS (M-HFTOPSIS) to enable a more precise evaluation of alternatives. The hesitant fuzzy BWM (HFBWM) is employed to calculate criteria weights, and the proposed M-HFTOPSIS is used to rank the alternatives. A case study involving ten criteria from technical, economic, and environmental perspectives is conducted to validate the effectiveness of the proposed method. Comparative results demonstrate that the proposed approach provides reasonable and reliable outcomes and that the enhanced HFS distance measure effectively models the differences between hesitant fuzzy sets. Full article

As social infrastructure intensively developed during the high economic growth period of the 1970s faces simultaneous aging, there is an urgent need to transition from conventional reactive maintenance to preventive maintenance utilizing various data (data-driven asset management. However, the greatest barrier in practice is that inspection data is unevenly distributed in analog formats such as paper and unstructured files, and heavily relies on the subjective visual evaluation of expert engineers (e.g., discrete graded evaluations from A to D). The intervention of this “Assessor Bias” makes it difficult to ensure the robustness required for direct statistical analysis. This paper serves as a bridge between this analog expert knowledge and quantitative data science. It formulates human cognitive conflicts (true state, peer pressure, avoidance of cognitive load) using the distance-decay model of the Analytic Hierarchy Process (AHP) and the Softmax function, constructing a micro-macro link model accompanied by stochastic variations. Through large-scale multi-agent simulations ($N={10}^7$) validating the model’s convergence, it was demonstrated that in long-tail distributions formed under peer pressure, macroscopic statistical distance metrics such as the Kullback-Leibler (KL) divergence ignore the fact that a small number of true signals are non-linearly suppressed, causing a statistical misinterpretation that “the error is within an acceptable range”. This implies that as long as macroscopic statistical indicators are over-trusted, signs of critical deterioration (minorities) will be structurally marginalized. Returning to the debate on “Homogeneity (Homogenität)” in German social statistics, this paper advocates that in order to realize objective “Micro-segmentation of Homogeneous Statistical Populations,” a paradigm shift from qualitative methods relying on human intuition to quantitative methods incorporating multi-criteria decision making is essential, rather than simply expanding the sample size. Full article

Aero-engine quality assessment under the RAMS framework faces two persistent challenges: the inherent epistemic and linguistic uncertainty in expert evaluation, and the systematic neglect of inter-dimensional coupling. This paper proposes an integrated assessment method that combines Interval Type-2 Fuzzy Sets (IT2FS)-based group decision-making with Partial Least Squares Structural Equation Modeling (PLS-SEM). At the measurement level, IT2FS encodes dual-layered uncertainty through the Footprint of Uncertainty (FOU); multi-expert judgments are aggregated via the fuzzy weighted geometric average operator and defuzzified using the Karnik–Mendel algorithm. At the structural level, a reflective second-order PLS-SEM model built on the RAMS framework enables parametric estimation and significance testing of inter-dimensional coupling. Validation on a 63-engine turbofan dataset confirms that all measurement model criteria are satisfied, the second-order model explains 82.4% of the variance in overall quality (R² = 0.824), and predictive relevance is strong (Q² = 0.567). Comparative experiments against three benchmark methods demonstrate consistent advantages in quality grade discrimination, information richness, sensitivity to technical improvements, and ranking robustness. These properties position the framework as a statistically rigorous, model-based complement to existing condition-monitoring and digital health management systems for complex propulsion systems, supporting quantitative decision-making within digital engineering programmes. Full article

Quality evaluation in production systems represents a significant challenge in the manufacturing industry, particularly in environments where expert judgment plays a key role in managing the inherent uncertainty of the production system. This study proposes a fuzzy multicriteria decision-making control chart, termed Fuzzy Decision-Making Control Chart based on AHP-Extent and Triangular Fuzzy Numbers (FDMCC-AHPE). The method integrates expert knowledge through triangular fuzzy numbers and a Fuzzy Analytic Hierarchy Process supported by Extent Analysis, to define fuzzy decision intervals for quality assessment and subsequently perform a structured analysis to classify the product within a control chart framework. In this framework, expert judgments expressed through linguistic evaluations are systematically translated into triangular fuzzy numbers and processed using FAHP–Extent Analysis, allowing the aggregation of subjective assessments within a structured mathematical decision model. The proposed method was validated in a tannery company, specifically in the retanning process. The industrial case study considers both qualitative criteria, such as surface defects and color uniformity, and quantitative process variables that include bath pH, treatment duration, and processing temperature. The results were compared with an empirical expert-based evaluation and a structured expert assessment supported by a multicriteria decision-making method. The findings demonstrate that the FDMCC-AHPE exhibits greater sensitivity in discriminating between quality states under uncertain evaluation conditions, particularly when samples involve complex evaluation conditions. Full article

Decision-making problems in complex environments are often characterised by uncertainty, vagueness, and dynamically evolving information. In such contexts, decision makers may express hesitant and fluctuating evaluations over time, which cannot be adequately captured by classical hesitant fuzzy frameworks. To address this limitation, time-sequential hesitant fuzzy sets (TSHFSs) have been introduced as an effective tool for modelling temporal hesitancy. However, the development of information measures for TSHFSs, particularly entropy measures for quantifying uncertainty and deriving criteria weights, remains limited. In this paper, we propose a novel class of entropy measures for TSHFSs by constructing transformation mechanisms based on proximity-driven formulations derived from similarity structures. The proposed measures are developed using arithmetic and algebraic operators to capture the dispersion of information across time sequences, enabling a more refined representation of temporal uncertainty. These entropy measures are further integrated into a multi-criteria decision-making (MCDM) framework, where they are employed to determine criteria weights under incomplete information and combined with the TOPSIS method for ranking alternatives. The effectiveness of the proposed framework is validated through comparative analysis with existing TSHFS entropy measures and sensitivity analysis under varying decision conditions. The results demonstrate that the proposed measures maintain ranking consistency while providing improved discrimination and interpretability of alternatives. In particular, the framework effectively captures fluctuating hesitancy and enhances the robustness of decision outcomes in dynamic environments. The proposed approach contributes to the advancement of TSHFS-based decision analysis by offering a mathematically grounded and practically applicable entropy-driven framework for handling time-dependent uncertainty in complex decision-making problems. Full article

Sustainable Development Goal 7 (SDG 7) aims to ensure access to affordable, reliable, sustainable, and modern energy for all. Evaluating the effectiveness of national energy policies in achieving this goal requires comprehensive and quantitative assessment frameworks. Countries’ performance toward SDG 7 is influenced by various structural factors, including energy demand growth, dependency on energy imports, renewable energy potential, and policy priorities. Therefore, systematic performance evaluation is essential for understanding policy effectiveness and identifying areas requiring improvement. This study evaluates Türkiye’s SDG 7 energy policy performance on a yearly basis over the period 2014–2023. A multi-criteria decision-making (MCDM) framework combining the Stepwise Weight Assessment Ratio Analysis (SWARA) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) methods is employed to quantify and compare performance across years. The proposed approach allows for the determination of indicator weights and the ranking of yearly performance levels based on multiple energy sustainability criteria. The results reveal an overall upward trend in Türkiye’s SDG 7 performance during the study period, although notable fluctuations are observed. A significant decline occurs in 2017, followed by a rapid recovery in subsequent years. Another temporary downturn is identified in 2021, while a remarkable improvement emerges in 2023. A sensitivity analysis based on multiple weighting scenarios was also conducted to examine the robustness of the obtained rankings, and the results confirm the stability of the overall ranking structure, with 2023 consistently maintaining the top position across most scenarios. These findings provide insights into how policy measures, market dynamics, and energy system developments influence the country’s progress toward sustainable energy goals. By incorporating a time-based evaluation framework, this study contributes to the SDG 7 literature by offering a quantitative and policy-oriented assessment of national energy performance. The proposed framework also provides a practical analytical tool for policymakers and energy regulators to monitor progress, identify vulnerable areas, and support evidence-based decision-making in the transition toward sustainable and clean energy systems. Full article