Search Results (99)

Decision-making in organizational and socio-technical systems often involves a responsible superior expert who must integrate committee judgments expressed under linguistic uncertainty, hesitation, and heterogeneous expertise. This paper proposes a systems-based superior–committee group decision-support method for criteria weighting under linguistic intuitionistic fuzzy uncertainty. First, the best–worst method is extended by using linguistic intuitionistic fuzzy numbers to represent pairwise preference information with linguistic membership, non-membership, and indeterminacy degrees. A utility transformation is then introduced to convert linguistic intuitionistic fuzzy comparisons into numerical preference values, enabling criteria weights to be derived through linear programming models. Second, a two-stage group decision-support framework is developed for superior–committee decision structures. In the first stage, committee expert influence is calculated by integrating prior expert weights obtained from the superior expert’s evaluation with judgment-derived expert weights based on certainty and agreement. In the second stage, the final criteria weights are obtained by combining the superior expert’s judgments with the weighted committee judgments. A constructed UAV criteria-weighting case and complementary numerical analyses are presented to illustrate the calculation process and examine the behavior of the proposed method. The results show that the framework provides a transparent mechanism for representing uncertain preferences, assigning expert influence, and deriving interpretable criteria weights in superior–committee group decision systems. Full article

In the context of urban stock renewal and metropolitanization, radial rail transit corridors in megacities need to transition from singular transportation functions to composite development carriers. This study proposes a hybrid multi-criteria decision-making (MCDM) model combining the Interval-Valued Intuitionistic Fuzzy Analytic Hierarchy Process (IVIF-AHP) with the entropy weight method, featuring an adaptive weighting mechanism that balances subjective expert judgment and objective data patterns. Novel corridor-scale indicators (i.e., hub functional matching, jobs–housing proximity, and gradient stability) are introduced, alongside a three-stage renewal pathway identification method. A case study of five Shanghai corridors (Lines 5, 9, 11, 16, and 17) reveals that jobs–housing spatial proximity is the primary vitality dimension (weight 0.244), while functional mix exhibits a pattern that identifies it as a common shortcoming across all corridors. The five corridors are classified into three vitality tiers, with five typical syndromes diagnosed. A sensitivity analysis confirms the robustness of corridor rankings to weighting and normalization choices, but reveals their sensitivity to the jobs–housing proximity threshold, which validates the 10 km standard. The proposed model offers quantitative diagnostics and differentiated renewal strategies, providing planning references for corridor renewal. Full article

Acquisition costs account for a significant share of total construction project costs, underscoring the importance of purchasing and supply management for organizational success. Supplier selection and evaluation are particularly critical because they involve multiple criteria, qualitative and quantitative attributes, and several decision-makers. In the construction industry, these activities become even more complex due to sector-specific characteristics such as convergent material flows, temporary facilities, buyer–supplier conflicts, price-oriented decisions, and the volatility of project-based markets. This paper investigates the supplier evaluation process in a construction company and identifies the company’s requirements and decision-makers’ expectations. Based on the collected data, this research proposes a model aligned with the company’s characteristics and the decision-makers’ expectations. The model combines two methods: the Intuitionistic Fuzzy approach to aggregate decision-makers’ opinions and ELECTRE TRI to classify suppliers based on predefined criteria and thresholds. The proposed model handles different weights assigned to each decision-maker for each criterion without allowing compensation among criteria. This model also explores the role of symmetry in multicriteria decision-making by combining Intuitionistic Fuzzy Sets with the ELECTRE TRI method. Decision-makers validated the proposal and emphasized its simplicity and flexibility, which allow future adjustments to both the criteria weights and the decision-makers’ assigned weights. Full article

Despite advances in using multi-criteria decision-making (MCDM) methods and their fuzzy set extensions for human evaluations of large language models (LLMs), several gaps remain in the literature, particularly in task-specific evaluations that offer a more tractable and interpretable approach. Thus, this work develops a generalized intuitionistic fuzzy MCDM approach that bridges methodological gaps by outlining two contributions. First, the integration of SWARA (Stepwise Weight Assessment Ratio Analysis) and WINGS (Weighted Influence Non-linear Gauge System) is demonstrated to compute the priority weights of the evaluation criteria, thereby augmenting the independence limitation in prior relevant studies. Second, we introduce a newly improved IF-EDAS (intuitionistic fuzzy Evaluation based on Distance from Average Solution) that preserves more uncertain information and provides a more natural extension of the canonical EDAS framework, starting with the adoption of the IFWAM (intuitionistic fuzzy weighted arithmetic mean) operator for a more intuitive approach in generating the intuitionistic fuzzy average solution vector. Also, the proposed IF-EDAS variant employs three decision rules and the Hamming distance metric in its novel computational approach. The proposed hybrid approach was deployed in two case studies evaluating five popular LLMs for text summarization across seven interdependent criteria. Results show that SWARA initially prioritizes accuracy, coherence, and consistency, but these were revised when accounting for criteria interdependence, with coherence and language quality emerging as the most preferred criteria. Both case studies suggest that Gemini may perform favorably, while Copilot may consistently rank last. The findings of the case studies share similar insights with those of three other similar IF-EDAS variants, although our claims may have limited external validity, which requires more case studies and experts in future task-specific human evaluations. The proposed approach, along with its deployment in two case studies, demonstrates human evaluations of LLMs with greater computational interpretability, which contribute to the general MCDM literature. Full article

Over the past few decades, extensive attention has been given by researchers and practitioners to the development and application of multi-criteria decision-making (MCDM) methods within intuitionistic fuzzy environments across a wide range of fields and disciplines. This challenging research area has emerged as one of the most prominent topics, and its importance and popularity are expected to continue growing in the future. The elliptic intuitionistic fuzzy set (EIFS) addresses complex, multidimensional, non-symmetrical vagueness and uncertainty more effectively than other traditional intuitionistic fuzzy sets (IFSs). Sustainable renewable energy source selection is a critical decision-making (DM) process aiming to identify the most suitable energy alternative. The process of selecting sustainable renewable energy sources necessitates a comprehensive assessment of numerous criteria, which encompass environmental ramifications, economic feasibility, and societal acceptance. Contemporary research suggests novel methodologies to enhance this selection process, highlighting the need for an MCDM framework that integrates a variety of factors. This study presents an innovative DM framework for sustainable renewable energy source selection based on EIFS and a newly developed aggregation operator, the Elliptic Intuitionistic Fuzzy Weighted Muirhead Mean Aggregation (EIFWMMA) operator. These mechanisms expand upon conventional intuitionistic fuzzy frameworks by employing an elliptical portrayal of membership and non-membership degrees, facilitating a more accurate and lifelike representation of uncertainty and hesitation in evaluations by experts. To enhance computational efficiency, the framework weaves together machine learning-driven dimensionality reduction and weight optimization strategies of principal component analysis (PCA) for DM. The suggested operators are employed in an MCDM scenario centered around the selection of sustainable renewable energy sources, where the hierarchy of alternatives is established through score values derived from EIFWMMA. A comparative exploration of Circular Intuitionistic Fuzzy Sets (C-IFSs) and Interval-Valued Intuitionistic Fuzzy Sets (IVIFSs) uncovers that the elliptical formulation yields consistently reliable, precise, and geometrically comprehensible results. The findings affirm that EIFS-based operators offer a resilient, adaptable, and broadly applicable strategy for tackling MCDM challenges amidst uncertainty. The Min–Max normalization method is employed to validate our proposed methodology for identifying alternatives within the MCDM paradigm. It also improves accuracy, stability, and scalability in comparison to conventional approaches. Full article

Multi-criteria decision-making (MCDM) problems in complex evaluation systems are often characterized by high uncertainty in expert judgments and dynamic variations in indicator importance. Traditional analytic hierarchy process (AHP) and entropy-based weighting methods typically suffer from two inherent limitations: the inability to explicitly quantify expert hesitation and the rigidity of static weight assignment under evolving data distributions. To address these challenges, this paper proposes a dynamic hybrid weighting framework that integrates an interval-valued intuitionistic fuzzy analytic hierarchy process (IVIF-AHP) with an entropy-triggered correction mechanism. First, interval-valued intuitionistic fuzzy numbers are employed to simultaneously model membership, non-membership, and hesitation degrees in pairwise comparisons, enabling a more comprehensive representation of expert uncertainty. Second, an entropy-triggered dynamic fusion strategy is developed by jointly incorporating information entropy and coefficient of variation, allowing adaptive adjustment between subjective expert weights and objective data-driven weights. This mechanism effectively enhances sensitivity to high-dispersion criteria while preserving expert knowledge in low-variability indicators. The proposed framework is formulated in a hierarchical fuzzy decision structure and implemented through a fuzzy comprehensive evaluation process. Its feasibility and robustness are validated through a concrete case study on teaching effectiveness evaluation for a university engineering course, leveraging multi-source data. Comparative analysis demonstrates that the proposed approach effectively mitigates the weight rigidity and evaluation inflation observed in conventional methods. Furthermore, it improves diagnostic resolution and decision stability across different evaluation periods. The results indicate that the proposed entropy-triggered IVIF-AHP framework provides a mathematically sound and practically applicable solution for dynamic MCDM problems under uncertainty, with strong potential for extension to other complex evaluation and decision-support systems. Full article

Choosing sites for solar photovoltaic (PV) power plants in developing countries like India is a crucial task while considering multiple conflicting factors and sub-factors simultaneously. Multi-criteria decision-making (MCDM) is an optimisation method that provides a framework for handling such situations in an intuitionistic fuzzy environment. The complexity and uncertainty associated with the site selection model are dealt with professionally. The Criteria Importance Through Intercriteria Correlation (CRITIC) method is applied to determine the relative importance of the criteria, identifying airflow speed as the most influential factor, followed by humidity ratio, level of dust haze, availability of labour and resources, and ecological effects. This shows that airflow speed plays an important role in the power plant’s efficiency and performance. The Vlse Kriterijumska Optimizacija I Kompromisno Rešenje (VIKOR) method is then used to prioritise the alternatives as potential locations for setting up a solar PV power plant in India. A new de-i-fuzzification method based on the relative difference between two real numbers is also proposed. Sensitivity analyses and comparative studies are conducted to assess the robustness and effectiveness of the framework. Overall, the results demonstrate that the proposed framework is useful and effective for optimising site selection for solar power plants in India. Full article

Traditional multi-criteria decision-making methods often operate on the assumption of symmetry, presupposing that the positive impact of a criterion’s presence is perfectly complementary to the negative impact of its absence. However, in real-world decision problems, this relationship is frequently asymmetric; some criteria act merely as “delighters,” while others represent “must-have” constraints. This study proposes a novel methodology, the Presence–Absence Synthesis (PAS) Method, which addresses this asymmetry by treating the “Presence Effect” and “Absence Effect” of criteria as two independent dimensions. The method is built upon intuitionistic fuzzy sets (IFSs) to effectively model the uncertainty and hesitation inherent in expert evaluations. The applicability of the proposed approach is demonstrated through a real-world workforce management problem aimed at assigning employees to the most suitable tasks based on their competencies in a retail store. In the study, the suitability scores derived from the PAS method are integrated into a mathematical optimization model for weekly employee scheduling, presenting a two-stage decision support framework. The results and comparisons with the Technique for Order Preference by Similarity to Ideal Solution method reveal that the PAS method more effectively distinguishes critical competency gaps (i.e., criteria with high absence effects), leading to more realistic task assignments and a measurable reduction in operational risks, such as skill mismatches and infeasible schedules. Furthermore, sensitivity analysis confirms that the proposed model yields consistent and robust results under varying conditions. Beyond the retail context, the proposed PAS framework is applicable to a wide range of decision-making problems, including healthcare staff allocation, project team formation, supplier selection, and other resource allocation settings where their presence cannot compensate for the absence of critical criteria. Full article

Selecting a sustainable healthcare waste treatment method is a complex multi-criteria problem influenced by environmental, economic, social and technological factors. This study addresses key gaps in the literature by proposing an intuitionistic fuzzy AHP–TOPSIS framework that explicitly models cognitive uncertainty and expert hesitation, while demonstrating its application through a real-world case study in Adana, Türkiye. In contrast to prior studies utilizing fewer criteria, our framework evaluates four treatment alternatives—incineration, steam sterilization, microwave, and landfill—across 17 comprehensive criteria that directly integrate circular economy principles such as resource recovery and energy efficiency. The results indicate that steam sterilization is the most sustainable option, demonstrating superior performance across environmental, economic, social, and technological dimensions. A 15-scenario sensitivity analysis ensures ranking resilience across varying decision contexts. Furthermore, a systematic comparative analysis highlights the methodological advantages of the proposed framework in terms of analytical granularity and robustness compared to existing models. The study also offers step-by-step operational guidance, creating a transparent and policy-responsive decision-support tool for healthcare waste management authorities to advance sustainable practices. Full article

In this article, a new similarity measure is discussed on interval-valued intuitionistic fuzzy values (IVIFVs). Here, the proposed similarity measure has been derived based on transformed intervals and its probability density functions, mean values, and standard deviations of IVIFVs. Based on the proposed similarity measure, several essential properties have been illustrated in this paper. Additionally, a new algorithm has been developed using the similarity measure of interval-valued intuitionistic fuzzy values (IVIFVs) to solve multi-attribute decision-making (MADM) problem. The proposed method is highly effective for solving various types of MADM problems. To demonstrate the effectiveness of the proposed similarity measure, a car selection problem has been considered, where the objective is to choose a suitable car for a decision maker from a set of alternatives evaluated under multiple criteria. In car selection, different features often involve conflicting criteria with imprecise data. Therefore, the proposed similarity measure of interval-valued intuitionistic fuzzy values assists in determining the best alternative among these conflicting criteria. Full article

In complex decision-making scenarios, such as smart city renewable energy project selection, decision-makers must contend with multi-dimensional uncertainty, conflicting expert opinions, and evolving temporal dynamics. This study introduces a novel Fractional q-Rung Orthopair Fuzzy Tensor (Fq-ROFT)-based group decision-making methodology that integrates the flexibility of q-rung orthopair fuzzy sets with tensorial representation and fractional-order dynamics. The proposed framework allows for the modeling of positive and negative membership degrees in a multi-dimensional, time-dependent structure while capturing memory effects inherent in expert evaluations. A detailed case study involving six renewable energy alternatives and six criteria demonstrates the method’s ability to aggregate expert opinions, compute fractional dynamic scores, and provide robust, reliable rankings. Comparative analysis with existing approaches, including classical q-ROFSs, intuitionistic fuzzy sets, and weighted sum methods, highlights the superior discriminative power, consistency, and dynamic sensitivity of the Fq-ROFT approach. Sensitivity analysis confirms the robustness of the top-ranked alternatives under variations in expert weights and fractional orders and membership perturbations. The study concludes by discussing the advantages, limitations, and future research directions of the proposed methodology, establishing Fq-ROFT as a powerful tool for dynamic, high-dimensional, and uncertain group decision-making applications. Full article

This study proposes a multi-dimensional, fuzzy logic-based decision-making framework to assess airport sustainability performance under uncertainty, addressing a notable gap in the literature. The proposed model integrates the Fuzzy Analytic Hierarchy Process (FAHP) to determine the weights of sustainability criteria and the Intuitionistic Fuzzy COPRAS (IF-COPRAS) method to evaluate airport alternatives. The assessment considers four main sustainability dimensions: environmental, economic, social, and technical/institutional. A case study involving five major airports in Türkiye reveals that environmental and economic indicators play a pivotal role in shaping sustainability performance. While Istanbul Airport (IST) demonstrated the highest performance across all scenarios, a comparison with Airport Carbon Accreditation (ACA) levels indicates that carbon-focused certification alone is insufficient to reflect the full spectrum of sustainability outcomes. This research presents a novel and robust evaluation framework, contributing to the limited body of fuzzy logic-based MCDM applications for airport sustainability in the Turkish context. The findings offer actionable strategic insights for policymakers and airport managers regarding investment prioritization, operational strategy reinforcement, and the alignment of airport development with long-term sustainability goals. The results are validated through rigorous sensitivity analyses, confirming the robustness of the model despite the focused expert panel. 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

Accurate fault risk assessment is essential for maintaining wind turbine reliability. Traditional failure modes and effects analysis (FMEA)-based approaches struggle to handle the fuzziness, uncertainty, and conflicting nature of multi-criteria evaluations, which may lead to delayed fault detection and increased maintenance risks. To address these limitations, this paper proposes an enhanced risk assessment framework that integrates subjective-objective weighting and fuzzy decision-making. First, a combined subjective–objective weighting (CSOW) model with adaptive fusion is developed by integrating the analytic hierarchy process (AHP) and the entropy weight method (EWM). The CSOW model optimizes the weighting of severity (S), occurrence (O), and detection (D) indicators by balancing expert knowledge and data-driven information. Second, a fuzzy decision-making model based on interval-valued intuitionistic fuzzy numbers and VIKOR (IVIFN-VIKOR) is established to represent expert evaluations and determine risk rankings. Notably, the overlap rate between the top 10 failure modes identified by the proposed method and a fault-tree-based Monte Carlo simulation incorporating mean time between failures (MTBF) and mean time to repair (MTTR) reaches 90%, substantially higher than other methods. This confirms the superior performance of the framework and provides enterprises with a systematic approach for risk assessment and maintenance planning. Full article

Traditional multi-criteria decision-making (MCDM) methods face problems in sustainable product design evaluation, including weak semantic expression, single weight modeling, and insufficient ranking robustness. To address these issues, this paper proposes an evaluation framework based on Trapezoidal Intuitionistic Fuzzy (TrIF), named TrIF-DEC, which integrates Decision-Making Trial and Evaluation Laboratory (DEMATEL), Entropy, and Combined Compromise Solution (CoCoSo). Firstly, design criteria across four dimensions—social, economic, technological, and environmental—are identified based on sustainability considerations. Then, TrIF is used to capture the fuzziness and hesitation in expert judgments. The DEMATEL and Entropy methods are combined to extract causal relationships between criteria and quantify data variation, enabling the collaborative weighting of subjective and objective factors. Finally, multi-strategy integrated ranking is performed through TrIF-CoCoSo to enhance decision stability. An empirical case study on nursing bed design demonstrates the effectiveness of the proposed framework. Results demonstrate that TrIF-DEC can systematically integrate uncertainty information with multidimensional sustainability goals, providing reliable support for complex product design evaluation. Full article