Search Results (188)

In flood disaster emergency decision-making, the dynamic changes in expert trust relationships directly affects the efficiency of reaching a decision consensus. This paper constructs a dynamic evolution model of expert trust relationships in flood disaster emergency decision-making from the perspective of preference distance: the initial trust matrix and weights of experts based on four dimensions including cooperation intensity, social relations, background similarity, and subjective initial trust; the cognitive trust is quantified by using the intuitionistic fuzzy Hamming distance, and the trust relationship is dynamically update through the exponential fusion method; the Louvain community discovery algorithm is introduce to achieve dynamic clustering of experts and weight updates of experts in combination with the dynamic changes in trust relationships; and a consensus feedback adjustment mechanism is designed to optimize the preferences of experts with lower consensus. At the same time, the dynamic trust model is verified by combining a flood disaster case. Case validation shows that the model completes consensus iteration in just four rounds, with the maximum increase in cognitive trust due to opinion convergence reaching 0.18 during the evolution process. The model effectively captures changes in trust among experts during decision-making, improving consensus convergence speed while ensuring that the final solution aligns with the comprehensive considerations required in emergency scenarios. This study provides a quantitative tool for large-group decision-making in flood emergencies under high-pressure, information-poor environments; one that balances dynamic trust evolution with efficient consensus building. Full article

Inclusion and preference relations are fundamental comparison tools in intuitionistic fuzzy set (IFS) theory and play an important role in decision analysis under uncertainty. In IFS representations, the hesitation degree reflects information that is not captured by membership and non-membership values alone. This study investigates the structural relationship between hesitation and the inclusion and preference relations of IFSs. A proposed interpretation of membership and non-membership degrees is employed to provide a geometric perspective on hesitation. Within this framework, analytical relations between hesitation inequalities and preference conditions are derived. In particular, it is shown that the hesitation inequality constitutes a necessary condition for preference, whereas inclusion relations remain compatible with a wider range of hesitation configurations. The theoretical observations are illustrated using electoral datasets from the 2002 South Korean presidential election and the 2000 United States presidential election in Florida. Regional vote shares are transformed into intuitionistic fuzzy representations to analyze the distribution of hesitation across regions. The examples demonstrate how hesitation may influence the stability of preference relations while inclusion relations remain structurally preserved. Full article

Energy scarcity has evolved into one of the most pressing challenges confronting the global community today. Fuel-driven loaders suffer from drawbacks such as high fuel consumption, low energy conversion efficiency, and heavy pollution, which not only aggravate atmospheric environmental pollution but also exacerbate the global energy crisis, directly undermining sustainable development goals. In contrast, permanent magnet synchronous motors (PMSMs) have become the preferred choice for the electrification of loaders owing to their exceptional torque density, strong overload capacity, and high reliability. However, during the optimal design of high-power interior permanent magnet synchronous motors (IPMSMs), traditional methods encounter issues with inadequate optimization efficiency and excessive computational expenses, thus hindering the large-scale deployment of power systems for eco-friendly loaders. Therefore, this paper takes a 125 kW, 3000 rpm IPMSM as the research object and proposes a multi-objective optimization strategy integrating a high-precision surrogate model with modern intelligent algorithms. This approach not only enhances motor performance but also cuts down computational overhead, which holds considerable significance for reducing industrial carbon emissions and driving the sustainable development of the manufacturing industry. Taking the key performance of IPMSM as the optimization objective and the related structural parameters as the optimization variables, the multi-performance characteristic index, interaction effect and comprehensive sensitivity of the variables are calculated and analyzed by fuzzy Taguchi experiment, and the hierarchical dimension reduction in the variables is completed. The Multicriteria Optimal-Latin Hypercube Sampling (MO-LHS) method is adopted to construct the sample data space, and a back-propagation neural network (BPNN) surrogate model is used to predict and fit the motor performance. The second-generation non-dominated sorting genetic algorithm (NSGA-II) is employed for iterative optimization, and the optimized motor dimension parameters are obtained through the Pareto optimal solution. Finally, through finite element analysis (FEA) and experiments, the rated torques obtained are 417.6 N·m and 425.1 N·m, respectively, with an error not exceeding 1.8%. This verifies the correctness and effectiveness of the proposed multi-objective optimization method based on the surrogate model. Full article

In this study, the evaluation and ranking of competencies in traditional and agile project management were examined using a structured Multi-Criteria Decision-Making (MCDM) algorithm. To determine the most important competency group, a direct assessment method by experts was employed. The Analytic Hierarchy Process method extended with triangular fuzzy sets (FAHP) was used to determine the criteria weights applied for ranking the specific competencies within the most important groups. For ranking competencies within these key groups, the Technique for Order Preference by Similarity to Ideal Solution method extended with triangular fuzzy sets (FTOPSIS) was applied. The same algorithmic procedure was carried out for both traditional and agile project management approaches, in a case study conducted across four companies in the automotive industry. The study showed that, in traditional project management, the most important competency group is related to organizational and managerial skills and competencies. On the other hand, in agile project management, the most important competency group refers to contextual skills and competencies. Furthermore, within the traditional approach, the most significant specific competency is project goal orientation, while in the agile approach, the most significant specific competency is customer and stakeholder orientation. Full article

Fully integrated airport access requires managing many aspects from both the passengers’ and the operational point of view. It is noted that air passenger preferences, influenced by distance, time, and other travel-related factors, are one of the fundamentals for understanding airport choice within multi-region airport systems. Therefore, an online survey was conducted in Europe, collecting more than two thousand responses, from which passengers’ attitudes and motives for selecting airport access travel modes were obtained. On the basis of the mobility profile of respondents, Fuzzy C-means (FCM) clustering analysis was performed to identify segments with similar travel attributes. The outcomes of clustering were validated through the comparison between the FCM and K-means clustering algorithms. The results of the study showed that (i) the car was the most preferred mode of transport across different age groups, and (ii) waiting time, travel costs, and travel time were rated as important, with reliability identified as the most important factor when making travel mode choices. These findings may serve as a reference for improving multimodal airport access services and encouraging a shift from private to public transportation modes. Full article

Previous studies, constrained by the overly rigid stability requirements, often fail to adapt to complex systems and struggle to identify stable outcomes that align with the practical context of multi-agent resource allocation. To address the three-sided matching problem in complex socio-technical and business management systems, this paper proposes a fuzzy stable matching method for three-sided agents under a framework of combinatorial preference relations, integrating network and decision theory. First, we construct a membership function to measure the degree of preference satisfaction between elements of different agents, and then define the concept of fuzzy stability. By incorporating preference satisfaction, we introduce the notion of fuzzy blocking strength and derive the generation conditions for blocking triples and fuzzy stability under the fuzzy stable criterion. Furthermore, we abstract the three-sided matching problem with combined preference relations into a shortest path problem. Second, we prove the equivalence between the shortest path solution and the stable matching outcome. We adopt Dijkstra’s algorithm for problem-solving and derive the time complexity of the algorithm under the pruning strategy. Finally, we apply the proposed model and algorithm to a case study of project assignment in software companies, thereby verifying the feasibility and effectiveness of this three-sided matching method. Compared with existing approaches, the fuzzy stable matching method developed in this study demonstrates distinct advantages in handling preference uncertainty and system complexity. It provides a more universal theoretical tool and computational approach for solving flexible resource allocation problems prevalent in real-world scenarios. Full article

Advancing the Sustainable Development Goals (SDGs)—framed around social, environmental, and governance dimensions—offers societies across the world the possibility of achieving long-term prosperity and ensuring that future generations enjoy a high quality of life. Governments pursue the 17 SDGs in accordance with their own socioeconomic and cultural contexts, institutional capacities, and available resources. Because countries differ substantially in structure and capability, their progress toward these goals varies, making the systematic measurement and analysis of SDG performance essential for appropriate timing and efficient resource allocation. This study proposes a hybrid assessment system to evaluate the sustainable development performance of upper-middle-income developing countries under both fuzzy and non-fuzzy environments. This integrated evaluation system consists of four main stages. In the first stage, evaluation criteria and alternative countries are specified, relevant data are obtained, and an initial decision matrix is developed. In the second stage, an efficiency analysis is conducted to identify countries that are efficient and those that are not. In the third stage, evaluation criteria are weighted using AHP and Fuzzy AHP methods. In the final stage, the TOPSIS and Fuzzy TOPSIS methods are used to rank efficient countries depending on sustainable development performance criteria. As a result, six countries were identified as inefficient countries based on sustainable development: China, Kazakhstan, Mongolia, Paraguay, Namibia and Turkmenistan. The AHP and Fuzzy AHP methods produced similar criterion weight values compared to each other. The criteria were prioritized from most important to least one as follows: Life expectancy, expected years of schooling, mean years of schooling, gross national income per capita, CO₂ emissions per capita, and material footprint per capita. While some countries achieved similar rankings using the TOPSIS and Fuzzy TOPSIS methods, most countries achieved different rankings because of the multidimensional nature of sustainable development. When the rankings obtained from the fuzzy and non-fuzzy approaches were compared, a noticeable level of overlap was observed, with a Spearman’s rank correlation coefficient of 68.73%. However, the fuzzy TOPSIS method is considered more reliable for assessing sustainable development performance due to its ability to handle data uncertainty, imprecision, and the multidimensional nature of SDG indicators. The results of this study demonstrate that analyses related to sustainable development, which may not contain precise and clear values and have a complex structure encompassing many areas such as social, environmental, and governance, should preferably be conducted within a fuzzy logic framework to ensure more robust and credible evaluations. Full article

Considering the complexity and hazardous nature of construction jobsites, selecting the effective safety risk control strategies is crucial to prevent accidents, protect labor crews, and achieve project objectives related to cost, schedule, and quality in the construction project. However, the evaluation of different safety strategies involves multiple conflicting criteria and uncertain expert judgments, making it a complex multi-criteria decision-making (MCDM) problem. To address this problem, this study develops a fuzzy-integrated MCDM framework that combines two methods: Fuzzy Analytic Hierarchy Process (FAHP), which systematically captures the relative importance of safety criteria under uncertainty, and ELECTRE III, which ranks alternative strategies by modeling preferences and veto conditions, reflecting real-world “non-compensatory” safety logic. FAHP determines criterion weights based on expert judgments, while ELECTRE III evaluates and ranks alternative safety strategies. The framework is validated through a piping construction case study, where it successfully identified the optimal safety plan. A sensitivity analysis is conducted to confirm the robustness of results, and comparative tests with other MCDM methods further support its reliability. Therefore, the proposed fuzzy-integrated framework offers an effective approach for evaluating safety risk control strategies, enhancing both safety and overall project performance, and advancing systematic safety management in the construction industry. Full article

Fomes fomentarius (L.) Fr. sensu lato is a common, widespread polypore and a pathological decayer in many hosts such as poplar, beech, and birch. It is either regarded as a single species, a species complex, or displaying a significant intraspecific variability. Limits between populations are fuzzy, and local differences have been mainly related to the current distribution of preferred hosts. The aim of the work was to test an imaging technique (RGB profiling) of cultures’ macromorphology on Petri plates to implement the traditional growth profiles of pure cultures in order to point out differences between strains from different European regions, hosts, and climates. Growth rates at 24 °C and 30 °C poorly segregated strains based on the origin, whereas there is a marked difference at 15 °C between strains from oceanic climates and continental climates. K-means clustering of RGB profiles also marked a difference at 15 °C between Central/North European strains and the Italian strains, although this variability gradually attenuates by increasing temperature. The combined approach, including a radial growth measuring and RGB profiling, successfully pointed out the intraspecific diversity in F. fomentarius, suggesting local adaptations. This study contributes to establishing a methodology to investigate the ecotype concept in polypores. Full article

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

Failure Mode and Effects Analysis (FMEA) is a systematic risk assessment tool that effectively evaluates the safety and reliability of products prior to their deployment. However, traditional FMEA fails to consider and leverage inherent system-specific information during risk assessment, while also neglecting the weights of risk factors (RFs) when processing data related to the Risk Priority Number (RPN). This leads to significant subjectivity in the final risk ranking of failure modes. To overcome these drawbacks, this study proposes an improved FMEA risk assessment method based on load sharing, aiming to develop an improved FMEA method that addresses the critical limitations of traditional approaches by integrating load sharing principles and systematic weight determination, thereby enhancing risk assessment objectivity and accuracy in complex multi-component systems. First, probabilistic linguistic terms are adopted to quantify experts’ risk assessment information, and the geometric mean method is then used to aggregate assessments from multiple experts. Second, the Fuzzy Best–Worst Method (FBWM) is employed to determine the relative weights of the three RPN factors (Occurrence, Severity, and Detection). Additionally, partial system structural data are obtained through load sharing, and these data—combined with the calculated factor weights—are integrated into the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to generate the final risk ranking of failure modes. Finally, a case study of a magnetic crane is conducted to verify the feasibility and effectiveness of the proposed method, supplemented by comparative experiments to demonstrate its superiority. Full article

This paper presents a practical and auditable methodology for prioritizing drinking water hazards based on fuzzy preference relations (FPR). The method is based on additive pairwise comparisons of tap water quality parameters, which are aggregated (median) into a complete preference matrix. For each parameter, a Fuzzy Priority Index (FPI) was determined as the average “advantage” over the others. The FPI values were mapped to five fuzzy priority levels (very low–very high) using triangular/trapezoidal membership functions, followed by a defuzzification process using the centroid of singletons (COGS) method. The final step is to map the categories to operational actions, ensuring a clear transition from assessment to decision (from routine monitoring to immediate intervention). The method was demonstrated on nine parameters that are relevant for regulatory (WHO/DWD) and operational purposes: As, Pb, THM, NO₃⁻, Hg, Cr, Mn, Cu, Fe. Thirty-six pairwise assessments were determined, which, after aggregation, formed fuzzy relations. The resulting ranking (FPI) is: As (0.76) > Pb (0.70) > THM (0.64) > NO₃⁻ (0.56) > Hg (0.50) > Cr (0.43) > Mn (0.36) > Cu (0.30) > Fe (0.25). Fuzzy categorization assigned As, Pb, THM to the High level, NO₃⁻, Hg, Cr to Medium, and Mn, Cu, Fe to Low, with the Score reflecting the “proximity” of higher levels. The approach is transparent, replicable, and supports sensitivity analysis. The combination of FPI with fuzzy categorization and a decision map transforms expert knowledge and uncertainty into prioritized, actionable steps for water safety management. Full article

The activity of serpins uses a specific mechanism or process. This process comprises several steps and is related to significant structural changes that involve significant displacement of chain fragments and whole molecules of protease. An important role is played by a segment of the serpin chain called the Reactive Central Loop (RCL), which interacts with the protease by inhibiting its activity. For the covalent binding of the protease to serpin, the movement of the protease molecule is an effect of splicing the RCL segment into beta-sheet A of serpin. There are structural forms—native, latent, Michaelis complex (non-covalent enzyme-inhibitor complex prior to RCL cleavage), covalent serpin–protease complex, and cleaved—associated with serpin activity. In this work, all these structural forms are discussed using the fuzzy oil drop (FOD-M) model, where the assessment criterion of structuring is based on identifying the type of hydrophobicity distribution. The analysis reveals the specificity of the inhibition mechanism, including the specific action of the RCL. The structural changes involved in this process have been shown to preserve the distribution of hydrophobicity in the form preferred by the aqueous environment in which serpins are active. The disorder (according to FOD-M model) in two complexes (Michaelis and covalent) is hypothetically treated as code for degradation factors. The applied model assesses the function-related structures using the hydrophobicity distribution as the criterion in contrast to many publications based on energetic aspects of serpin activity. Structural changes appear appropriate for water environments—the environment of serpin activity. Full article

With the rapid increase in global trade in recent years, the demand for maritime transportation has significantly intensified vessel activity, leading to a considerable rise in carbon emissions originating from the maritime sector. As a result, in line with the 2050 decarbonization targets set by the International Maritime Organization (IMO) and the European Union (EU), legal regulations addressing carbon emissions have been dynamically tightened and gradually enacted. This study aims to determine the significance levels of the factors affecting the maritime sector in response to carbon emission regulations and to reveal the interrelationships among these factors. In this context, the criteria regarding the impacts of climate-related carbon emission regulations were identified based on expert opinions using the Fuzzy Delphi method. The interaction strengths and significance levels among the factors were analyzed using the Fuzzy DEMATEL method, and the relationships were modeled through Interpretive Structural Modeling (ISM). According to the findings, “Fuel Preferences and Alternative Fuel Usage” (C2) emerged as the most critical factor under recent international regulations. “Adaptation to International and National Regulations” (C8) and “Port Infrastructure” (C3) were also identified as the key factors impacting shipping industry efficiency. The analysis revealed that “Logistics Costs” (C5) and “Environmental Protection and Sustainability” (C7) are the most significantly affected outcome factors within the system. The hierarchical structural modeling revealed that “Port Infrastructure” (C3) serves as a defining starting point within the system. This study contributes to the literature by uncovering the causal relationships among the factors determining the effectiveness of ever-evolving carbon emission regulations. It offers a valuable decision-support tool for maritime companies and policymakers. Accordingly, it provides an alternative roadmap and a structural model indicating which strategic areas should be prioritized to achieve the targeted low-carbon emission goals in maritime transportation. Full article

Background: Improving operational efficiency in the mining industry increasingly de-pends on a mature asset management framework and the careful selection of reliable, sustainable suppliers for systems, personnel, equipment, and services. Given the complexity of mining operations and the growing use of digital tools, choosing the right maintenance management system requires a robust decision-making process that considers economic, environmental, and social sustainability factors. Methods: This study develops and compares two multi-criteria decision-making approaches, a ranking method and a fuzzy logic-based model to evaluate four maintenance management systems against fifteen sustainability-related criteria. Expert opinions from executives and operational managers in the South African mining sector were gathered, focusing on factors such as cost, integration, reliability, ease of use, inventory control, and predictive capabilities. Results: The ranking method produced a clear, quantitative order of preference, while the fuzzy model addressed uncertainty and subjectivity in expert judgments. Both methods identified the same top choice: UPKEEP, followed by SAP, FIIX, and LIMBLE. Conclusions: This comparison shows that combining fuzzy logic with sustainability-focused evaluation can improve the flexibility and reliability of supplier selection in asset management. The proposed approach offers practical guidance for aligning maintenance system choices with broader sustainability goals in mining operations. Full article