Search Results (1,014)

Due to increasing demand and consumption of electricity, as well as the need to decarbonize and mitigate climate change, solar energy is an important factor in the transition to emission-free energy sources. This study focuses on identifying the most suitable locations for the construction of large solar photovoltaic (PV) power plants while respecting environmental, economic, and technical standards. The study area covers the mainland part of Southeastern Europe (796,039 km²), including the following countries: Slovenia, Croatia, Bosnia and Herzegovina, Serbia, Montenegro, North Macedonia, Albania, Greece, Bulgaria, Romania, Moldova, and Türkiye. Using geographic information systems (GIS) and remote sensing methods, nine factors (topographic, climatic, hydrological, ecological, vegetation, and anthropogenic) were analyzed with a spatial resolution of 100 m. A fuzzy analytic hierarchy process (F-AHP) pairwise comparison matrix was constructed to quantify the relative importance of the selected criteria. The F-AHP weighting results indicate that photovoltaic output (17.9%) and land use (15.7%) are the most important among the evaluated criteria. The results show that 6.7% of Southeastern Europe is very highly suitable for installing solar PV plants, with the most suitable areas located in Moldova (14.5%) and Greece (10.5%). Through spatial analysis of the final results, 24 of the most suitable locations for large-scale solar PV power plant development were identified, with a potential to generate approximately 30.2 TWh of electricity annually. In such a scenario, the forecast indicates that 24 large-scale solar power plants would supply electricity to more than 6.7 million households, corresponding to over 17 million inhabitants. The final spatial patterns provide decision-makers at the international level with a significantly more effective basis for planning solar energy development in order to increase the share of green energy and clean technologies in this part of Europe. Full article

This study develops an interval-valued q-spherical fuzzy rough set TOPSIS framework (IVq-SFRS-TOPSIS) for multi-criteria group decision-making when expert judgments contain interval uncertainty, neutrality, and granular indiscernibility. The revised framework clarifies the relationship between interval-valued q-spherical and interval-valued T-spherical fuzzy models, defines admissible approximation operators over compatible domains, and introduces a radial projection step that guarantees closure under the IVq-SFN constraint whenever component-wise extrema would otherwise violate it. The proposed framework provides a mathematically balanced representation of interval-valued q-spherical fuzzy information, reflecting the concept of symmetry and supporting reliable group decision-making under uncertainty. The TOPSIS procedure is then formulated through expert aggregation, benefit–cost normalization, entropy-based criteria weighting, ideal-solution distance calculation, and closeness-coefficient ranking. The method is illustrated through a sustainable smart city development case using four AI-based alternatives and six criteria. Rather than claiming unconditional superiority, the revised comparative and sensitivity analyses examine how the ranking changes under alternative fuzzy decision models, different q values, perturbations to criteria weights, and perturbations to the decision matrix. The results indicate that the proposed framework provides an interpretable rough-boundary representation and a reproducible ranking mechanism for complex MCDM problems under interval-valued q-spherical uncertainty. Full article

Effective environmental sustainability decision-making requires the consideration of both positive and negative information, as well as the integration of weighting methods, to ensure decisions are accurate and representative of real-world conditions. This study presents a Systematic Literature Review (SLR) on the development of the Weighted Bipolar Fuzzy Soft Set (WBFSS) framework for environmental sustainability decision-making. Articles were retrieved from three databases: Scopus, ScienceDirect, and Dimensions. The screening process adhered to PRISMA 2020 guidelines and identified 27 relevant articles. VOSviewer was subsequently used to conduct a bibliometric analysis, mapping keyword co-occurrences and the structural landscape of research topics. The analysis examined the evolution of Bipolar Fuzzy Soft Set (BFSS) frameworks, their application domains, and the integration of weighting methods within BFSS and related Fuzzy Soft Set (FSS) frameworks. The review found that, although 11 studies addressed environmental sustainability applications, only three explicitly employed BFSS-based frameworks, indicating that the application of BFSS in this domain remains limited. Furthermore, the incorporation of explicit weighting techniques within BFSS remains scarce, particularly for objective, data-driven weighting approaches. These findings provide a comprehensive overview of current research trends, identify important methodological gaps, and support the conceptual development of the WBFSS framework as a direction for future research rather than an established decision-making framework. This study highlights opportunities to advance decision-support methods for environmental sustainability, which may support future climate-related and sustainability-oriented decision-making in the context of Sustainable Development Goal 13 (Climate Action). Full article

The growing demand for hydrogen as a low-carbon energy carrier has renewed interest in coal, an abundant and globally available resource, making it crucial to assess its suitability for efficient hydrogen production via in situ pyrolysis. The main objective of this study is to propose a new and robust hesitant fuzzy Multi-Criteria Decision-Making (MCDM) framework to the literature to determine the coal alternative that will provide the most efficient hydrogen production. For this purpose, five different coals with the largest reserves in the United States (Wyoming, Illinois, West Virginia, Kentucky, and Pennsylvania) were examined under eight criteria. To manage uncertainties in determining the weights of the criteria, the hesitant fuzzy set-based step-wise weight assessment ratio analysis (SWARA) method was used. The coal alternatives were ranked using the Alternative Ranking Technique based on Adaptive Standardized Intervals (ARTASI). The proposed framework uses expert evaluations and performance criteria as input parameters to determine criterion weights and rank the alternatives. The output consists of the calculated criterion weights, utility values, and the final ranking of the liquid hydrogen rocket oxidizer systems. The robustness of the obtained results is further verified through parametric analyses of the w and Ψ parameters and comparative other MCDM methods validation. The findings consistently demonstrate that Wyoming coal is the most suitable alternative for hydrogen production via in situ pyrolysis. This study provides a systematic decision support framework for evaluating coal resources and offers valuable guidance for future hydrogen production strategies based on in situ coal pyrolysis. Full article

Process Failure Mode and Effects Analysis (PFMEA) is the standard technique for proactive risk assessment in automotive assembly lines. To support differentiated criterion weighting, hesitancy-aware linguistic evaluation and rank stability validation, this paper proposes a symmetric extension of PFMEA that integrates Proportional Intuitionistic Fuzzy Sets (PIFSs) with the Analytic Hierarchy Process (AHP) and the Evaluation Based on Distance from Average Solution method (PIF-EDAS). PIFSs introduce a proportional balance between membership μ, non-membership ν and hesitancy π that is mathematically symmetric under linguistic-pair interchange and that preserves a constant hesitancy budget (π = 1/(1 + $k_1$ + $k_2$)) across the nine-point linguistic scale. The framework is applied to an automotive Original Equipment Manufacturer (OEM) assembly line in Türkiye, on an inventory of twelve failure modes spanning torque, fastening, welding, panel alignment, harness, sealant, paint, ECU, trim, electrical connector, part variant and tightening sequence operations. Consistency of the PIF-AHP pairwise comparisons is confirmed (CR = 0.0017 ≪ 0.1), yielding criterion weights $w_S$ = 0.598, $w_O$ = 0.245 and $w_D$ = 0.156. Comparative cross-method analysis against PIF-TOPSIS, PIF-VIKOR and a classical-style ordinal RPN benchmark indicates strong cross-method agreement: Spearman rank correlations range from 0.865 to 0.972, and Wrong Torque Application remains the unanimous top-priority failure across all four methods. Five sensitivity scenarios (proposed weights, equal weights, and severity-, occurrence- and detection-dominant) confirm that FM1 (Wrong Torque) remains the top-priority failure and FM9 (Damaged Interior Trim) remains the lowest-priority failure across all five scenarios; the composition of the upper-priority set is criterion-sensitive, with FM10 rising under equal-weight, occurrence- and detection-dominant scenarios and FM8 rising under the severity-dominant scenario. The proposed framework incorporates differentiated criterion weights, expert hesitancy and rank stability validation within a symmetric PIFS-based MCDM structure. The contribution of this study is therefore three-fold: (i) a symmetric PIFS formulation that enforces mirror symmetry under linguistic-pair interchange and a constant hesitation budget on the nine-point scale; (ii) a case-based assessment of twelve automotive PFMEA failure modes; and (iii) a transparent rank stability protocol for symmetric MCDM benchmarking. The framework integrates directly with existing FMEA workflows and scales linearly in computational complexity with the number of failure modes. Full article

This study aims to identify and prioritize digital economy factors affecting inflation and to determine effective policy strategies for managing digitally driven inflationary pressures in the context of financial systems and risk dynamics. The analysis considers twelve key digital economy indicators, including e-commerce penetration, digital payment systems, internet infrastructure, price transparency, digital advertising, Industry 4.0 technologies, data-driven inventory and demand systems, fintech adoption, cryptocurrency usage, and digital financial access. In parallel, eight policy strategies are evaluated, covering digital price transparency, expansion of digital payments, digital logistics optimization, digital public services, smart manufacturing, intelligent-based demand forecasting, fintech integration, and digital workforce development. The study employs a novel intelligent-supported decision-making framework integrating an attention-based expert weighting approach, generalized fractal fuzzy sets, the MEREC method, and the ARLON technique. The empirical design is based on expert evaluations obtained from ten specialists with at least 12 years of experience in digital economy, finance, and policymaking. Rather than relying on country-specific or time-series inflation datasets, the study examines the structural relationship between digitalization and inflation through a multi-criteria expert-based approach, with data collected in 2025. The findings indicate that e-commerce penetration and the prevalence of digital payment systems are the most influential factors affecting inflation. In addition, digital price transparency and the expansion of digital payment systems emerge as the most effective strategies for mitigating inflationary pressures. These results provide important insights into how digital transformation reshapes inflation dynamics, monetary transmission mechanisms, and inflation-related financial risks. The proposed model offers a robust and systematic framework for analyzing inflation in digitalized economies and supports policymakers and financial decision-makers in managing emerging risks in intelligent-driven economic environments. Full article

The selection of transportation service providers (TSPs) is a strategically critical decision in sustainable supply chain management. However, existing decision-making frameworks exhibit three recurring limitations: the absence of formally validated, sector-specific sustainability criteria; reliance on weighting methods that inadequately handle expert judgment uncertainty; and limited application to emerging market contexts, particularly export-oriented garment and textile industries facing growing environmental, social, and traceability pressures from global buyers. To address these gaps, this study develops and validates an integrated multi-criteria decision-making framework combining Content Validity Ratio $\left(CVR\right)$ analysis, the Fuzzy Best–Worst Method (FBWM), and Fuzzy Additive Ratio Assessment (FARAS). $CVR$ analysis was applied to an initial pool of $28$ candidate criteria, retaining $22$ validated criteria spanning economic, environmental, social, and operational dimensions. FBWM was subsequently used to derive criterion weights from nine decision-makers (DMs) representing garment manufacturers, transportation providers, and academia in Vietnam, while FARAS ranked five candidate TSPs. Results indicate that operational and economic criteria are the most influential dimensions, while cost for the service, financial performance, industry experience, environmental awareness, and environmental legal and policy framework emerge as the five highest-weighted sub-criteria. The final ranking order, TSP2 > TSP4 > TSP5 > TSP1 > TSP3, remained stable across benchmarking with FTOPSIS, FVIKOR, and FMOORA, as well as underweight perturbation and equal-weighting scenarios, confirming the robustness of the ranking results. 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

Background: Evaluating centralized purchasing performance is a complex multi-criteria decision-making problem involving uncertainty, linguistic assessments, and subjective judgments from internal clients. Existing approaches provide limited support for handling these characteristics simultaneously. Methods: This study proposes a Mamdani fuzzy inference model integrating four criteria: Service Quality, Responsiveness, Compliance, and Collaboration. The fuzzy rule base was developed using expert knowledge and organizational evaluation practices. The model was applied to a real industrial case study based on an annual evaluation conducted collaboratively by four internal evaluators. Results: The model transformed qualitative assessments into an interpretable performance score while capturing interactions among evaluation criteria and handling uncertainty in the evaluation process. Conclusions: The proposed approach provides a structured decision-support framework for evaluating centralized purchasing performance. It enables the integration of linguistic assessments and expert knowledge, offering a flexible and coherent evaluation tool for industrial environments. 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

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

Hybrid Evolutionary–Fuzzy Systems (HEFS) have emerged as a powerful computational paradigm for addressing complex engineering optimization and intelligent decision-making problems under uncertainty. This study presents a systematic review, conducted following the PRISMA 2020 methodology, to analyze advancements in the integration of evolutionary algorithms, swarm intelligence, fuzzy logic, and Multi-Criteria Decision-Making (MCDM) techniques over the period 2020–2026. The analysis focuses on identifying key algorithmic mechanisms, hybridization strategies, performance metrics, and application domains. The results indicate that HEFSs significantly enhance optimization performance by balancing exploration and exploitation, improving robustness, and enabling adaptive and interpretable decision-making in uncertain and multi-objective environments. In particular, fuzzy systems contribute to effective uncertainty modeling and interpretability, while evolutionary and metaheuristic algorithms provide strong global search capabilities. Despite these advantages, important challenges remain, including high computational complexity, scalability limitations, and the trade-off between accuracy and interpretability. The review also identifies emerging research directions involving Explainable Artificial Intelligence (XAI), deep learning integration, digital twins, and big-data-enabled optimization. However, the reviewed evidence suggests that these technologies should currently be interpreted as promising but still evolving extensions, whose maturity and large-scale validation remain heterogeneous across application domains. 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 study examines how cyber awareness is translated into secure employee behavior in organizations through a systems thinking perspective that connects human resource management, organizational processes, and technological conditions. Rather than treating awareness as a final outcome, the study conceptualizes it as part of an awareness-to-action process shaped by individual cognition, organizational support, and governance mechanisms. Using survey data from white-collar employees in the banking and finance sector, the study applies a q-rung orthopair fuzzy decision framework to prioritize the drivers of this transition. The q-ROF-SWARA results identify self-efficacy, awareness/threat recognition, and awareness climate as the most important dimensions, indicating that employees are more likely to act securely when they can recognize risks, feel capable of responding, and work in an environment that reinforces cybersecurity. The q-ROF-MARCOS results show that remote employees have the strongest overall awareness-to-action profile, although the utility differences across on-site, hybrid, and remote work modes are relatively small and should be interpreted cautiously. The findings contribute to cybersecurity behavior research by integrating systems thinking with fuzzy multi-criteria prioritization and by showing that secure behavior depends on the interaction of cognitive, behavioral, HRM, and governance-related conditions. Practically, the study provides decision-makers with a structured basis for strengthening cybersecurity interventions beyond general awareness programs and toward sustainable behavioral change. Full article