Search Results (320)

In the context of a low-carbon economy, automotive parts supply chains face multifaceted risks, making an effective supply chain risk assessment model a crucial means of ensuring supply chain stability. Traditional evaluation methods struggle to comprehensively and accurately identify all influencing factors and their interrelationships in automotive parts supply chains. This article constructs an evaluation model based on the principle of symmetry. The “structural symmetry” is determined by the ratio of the completeness of risk dimension coverage in the indicator system to the precision of indicators, while “fusion symmetry” refers to the degree of equilibrium in information contribution during the fusion of subjective and objective weights. First, Fault Tree Analysis (FTA) and the Delphi method are adopted to establish a risk evaluation index system, whereby structural symmetry is ensured by the equilibrium between the completeness of risk dimension coverage and the accuracy of indicators in the index system. Second, drawing on the symmetric fusion principle, this study proposes a hybrid evaluation approach integrating hesitant fuzzy DEMATEL with entropy weight-coefficient of variation (HDEC), and the fusion symmetry is guaranteed by the balanced integration of subjective and objective weight information. Finally, a case study of an automotive parts supply chain enterprise quantitatively assesses and ranks risk factors, with corresponding countermeasures proposed. The symmetry-guided HDEC method achieves high accuracy, identifying indicator–causal relationships. Compared with the traditional entropy-weighted AHP algorithm, the Pearson correlation coefficient is 0.8566, and Spearman’s rank correlation coefficient is 0.88, indicating strong weight correlation and robust stability. The integration of mathematical symmetry enhances the model’s theoretical rigor, which aligns with symmetry-oriented optimization research. Full article

Fuzzy implication operators are vital in the modeling of uncertain reasoning, particularly in approximate reasoning and fuzzy inference systems. The objective of this survey is to provide a structured and comprehensive overview of fuzzy implication, intuitionistic fuzzy implication, and hesitant fuzzy implication. We examine their properties, representations, and characterizations. We discuss a number of findings about fuzzy negations, fuzzy implications, intuitionistic fuzzy implication, and hesitant fuzzy implications, including their characterizations with respect to the identity principle and ordering property, which lead to fundamental results. Full article

Aiming at the problem that existing intuitionistic fuzzy entropy measures fail to fully balance the interaction between intuition (determined by hesitation degree) and fuzziness (characterized by the difference between membership degree and non-membership degree), this paper proposes the concept of isentropic arc, reveals the mutual offset effect of the two in entropy composition, and provides a new theoretical perspective for the planar analysis of entropy measures. Further research finds that there are maximum and minimum entropy points in the intuitionistic fuzzy entropy plane. Based on this, two different types of isentropic arcs can be constructed. Combining this feature with the core logic of approaching the ideal solution, this paper constructs a new intuitionistic fuzzy entropy measure formula based on the TOPSIS method. This formula can characterize the synergistic influence of intuition and fuzziness at the same time, meets all the constraints of the axiomatic definition, and is more suitable for the needs of actual decision-making scenarios. Comparative analysis of numerical examples shows that the proposed new entropy measure has significantly better discrimination than existing methods for six groups of samples with a high hesitation degree and high fuzziness, and the entropy value ranking is consistent with the ranking of the uncertainty information contained in the samples. Finally, the weight decision-making model based on this entropy measure is applied to the evaluation of coal mine emergency rescue capability, verifying its practical value in solving complex uncertainty problems. Full article

With the rapid advancement of speech synthesis and voice conversion technologies, audio deepfake techniques have posed serious threats to information security. Existing detection methods often lack robustness when confronted with environmental noise, signal compression, and ambiguous fake features, making it difficult to effectively identify highly concealed fake audio. To address this issue, this paper proposes a Dual-Path Time-Frequency Attention Network (DPTFAN) based on Pythagorean Hesitant Fuzzy Sets (PHFS), which dynamically characterizes the reliability and ambiguity of fake features through uncertainty modeling. It introduces a dual-path attention mechanism in both time and frequency domains to enhance feature representation and discriminative capability. Additionally, a Lightweight Fuzzy Branch Network (LFBN) is designed to achieve explicit enhancement of ambiguous features, improving performance while maintaining computational efficiency. On the ASVspoof 2019 LA dataset, the proposed method achieves an accuracy of 98.94%, and on the FoR (Fake or Real) dataset, it reaches an accuracy of 99.40%, significantly outperforming existing mainstream methods and demonstrating excellent detection performance and robustness. Full article

Although viral outbreaks are increasing, vaccination rates are decreasing. Our aim was to explain this baffling behavior that seems to contradict rational self-interest, and, thus, be beyond the purview of rational choice theories. We integrated fuzzy-trace theory and major theoretical alternatives and applied them to influenza, testing theoretical predictions in two samples: young adults (who are major viral vectors), N = 722, and community members, N = 185. Controlling for prior knowledge and other psychosocial factors that influence vaccination, explained variance jumped significantly when key predictors from fuzzy-trace theory were added, reaching 62% and 80% for vaccination intentions and 37% and 59% for behavior for each sample, respectively. Single items assessing global gist perceptions of risks and benefits achieved remarkable levels of diagnosticity. Key predictors were intuitive in that they were gisty, imprecise, and non-analytical. In contrast, rational system 2 measures—numeracy and cognitive reflection—were not predictive. These results provide new insights into why individuals vaccinate or not and new avenues for interventions to improve shared clinical decision-making. Full article

The transportation problem (TP) is a canonical linear programming model for minimizing the cost of distributing goods from multiple sources to multiple destinations. Classical TPs assume deterministic costs, supplies, and demands, whereas real supply chains are affected by volatility in fuel prices, inflation, disruptions, and weather, making such parameters uncertain. Fuzzy sets (FSs) and intuitionistic fuzzy sets (IFSs) have been widely used to handle vagueness; however, while Atanassov’s IFSs incorporate hesitation in addition to membership and non-membership, they remain limited to isotropic representations of uncertainty. This paper introduces an index-matrix interpretation for a two-stage three-dimensional transportation problem (2-S 3-D TP) defined under Elliptic Intuitionistic Fuzzy Quadruples (E-IFQs). Within this framework, transportation costs, supplies, and demands are represented as E-IFQs, allowing the modeling of anisotropic and correlated uncertainty along the membership and non-membership axes. The two-stage formulation extends previous intuitionistic fuzzy approaches by adding a temporal dimension and incorporating practical constraints such as cost thresholds and feasibility checks. The objective is to determine optimal producer–hub–buyer allocations that minimize the total E-IFQ cost while preserving consistency across all stages and time periods. A detailed case study on EV battery module distribution demonstrates the effectiveness of the proposed model. Compared with conventional fuzzy and intuitionistic fuzzy formulations, the 2-S 3-D E-IFTP yields more robust and precise decisions under complex, multidimensional uncertainty, offering improved interpretability and policy integration over time. Full article

A novel multiple attribute group decision-making (MAGDM) model is introduced in this study, utilizing a diversified hesitant fuzzy multi-granulation information system to address challenges in incomplete information settings. The analysis commences with an exploration of hesitant fuzzy sets and multi-granulation approximation. Subsequently, the integration of cumulative prospect theory into Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) within a hesitant fuzzy framework is discussed, emphasizing the incorporation of a risk preference coefficient in GDM to enhance individual assessments. The model proposes a systematic approach to address various MAGDM scenarios under hesitant fuzzy conditions. An illustrative case study on resource-sharing is provided to demonstrate the efficacy of the diversified MAGDM model, with evaluation outcomes expressed using hesitant fuzzy elements, offering valuable insights into group decision-making in hesitant fuzzy environments. 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

Under the dual pressures of global climate change and energy structure transition, the offshore wind–solar–current energy coupling hydrogen production (OCWPHP) system has emerged as a promising integrated energy solution. However, its complex multi-energy structure and harsh marine environment introduce systemic risks that are challenging to assess comprehensively using traditional methods. To address this, we develop a novel risk assessment framework based on hesitant fuzzy sets (HFS), establishing a multidimensional risk criteria system covering economic, technical, social, political, and environmental aspects. A hybrid weighting method integrating AHP, entropy weighting, and consensus adjustment is proposed to determine expert weights while minimizing risk information loss. Two aggregation operators—AHFOWA and AHFOWG—are applied to enhance uncertainty modeling. A case study of an OCWPHP project in the East China Sea is conducted, with the overall risk level assessed as “Medium.” Comparative analysis with the classical Cumulative Prospect Theory (CPT) method shows that our approach yields a risk value of 0.4764, closely aligning with the CPT result of 0.4745, thereby confirming the feasibility and credibility of the proposed framework. This study provides both theoretical support and practical guidance for early-stage risk assessment of OCWPHP projects. Full article

In the digital context, how to achieve symmetrical integration between subjective evaluation and structural stability becomes the key to improving the design effect of iterative product optimization. In this paper, we propose an iterative design method for CIHFS-DEMATEL products that incorporates structural symmetry analysis. The method is based on online review mining and constructs a credibility-based interval hesitant fuzzy set (CIHFS) to symmetrically express the ambiguity and credibility differences in the decision-maker’s subjective evaluation. In turn, a novel exact score function called credibility interval hesitant fuzzy score function (CHFSF), incorporating information symmetric weights, is proposed to realize the bidirectional symmetric mapping between subjective fuzzy inputs and objective exact outputs. Subsequently, the CIHFS-DEMATEL model is introduced to identify the causal paths and a symmetric interaction structure between potential users’ demands. Finally, the demand module mapping matrix is constructed to realize the symmetric decision-making closure loop from demand to solution. Taking the “Intelligent Classified Trash Can” as a case study, we verify the superiority of the method in terms of recognition accuracy, rationality of weight allocation, and structural stability. This study emphasizes the structural symmetry between “input–evaluation–output”, which provides a theoretical foundation and practical framework for the optimal design of products with complex multi-source information. Full article

Consensus in group decision-making has become a hotspot to ensure the agreement opinions of decision makers (DMs). The irrational behaviors of DMs, such as confidence, will impact the consensus results, which should be considered. In addition, the existing self-confidence level directly given by DMs rather than exacted from evaluation information may generate malicious manipulation. Furthermore, double-hierarchy hesitant fuzzy linguistic term set (DHHFLTS) is an effective tool to express the complex evaluations of DMs. In this paper, the endo-confidence of DHHFLTS to reflect confidence of DMs from the perspective of evaluation information is defined. Then, we propose a novel consensus model with endo-confidence of DMs based on DHHFLTSs. First, some improved operators of DHHFLTSs are developed. Second, the weight is determined based on both entropy and endo-confidence. Due to the fact that the consensus threshold should decrease as the endo-confidence increases, we give a novel method to obtain the consensus threshold considering endo-confidence level. Moreover, the two-stage adjustment mechanism is presented for non-consensus DMs and the selection process is constructed. Finally, an illustrative example is carried out to demonstrate the feasibility of the proposed model, and a series of comparative analysis is used to show its stability. Full article

Investment decision-making is often characterized by uncertainty and the subjective weighting of criteria. This study aims to develop a more robust decision support framework by integrating the Generalized Interval-Valued Hesitant Intuitionistic Fuzzy Soft Set (GIVHIFSS) with the Analytic Hierarchy Process (AHP) to objectively weight criteria and handle multi-evaluator hesitancy. In the proposed GIVHIFSS-AHP model, the AHP is employed to derive mathematically consistent criterion weights, which are subsequently embedded into the GIVHIFSS structure to accommodate interval-valued and hesitant evaluations from multiple decision-makers. The model is applied to a numerical case study evaluating five investment alternatives. Its performance is assessed through a comparative analysis with standard GIVHIFSS and GIFSS models, as well as a sensitivity analysis. The results indicate that the model produces financially rational rankings, identifying blue-chip technology stocks as the optimal choice (score: +2.4). The comparative analysis confirms its superiority over existing models, which yielded less-stable rankings. Moreover, the sensitivity analysis demonstrates the robustness of the results against minor perturbations in criterion weights. This research introduces a novel and synergistic integration of the AHP and GIVHIFSS. The key advantage of this approach lies in its ability to address the long-standing issue of arbitrary criterion weighting in Fuzzy Soft Set models by embedding the AHP as a foundational mechanism for ensuring validation and objectivity. This integration results in mathematically derived, consistent weights, thereby yielding empirically validated, more reliable, and defensible decision outcomes compared with existing models. Full article

The concept of Multi Q-Fermatean hesitant fuzzy soft sets (MQFHFSS), derived from the integration of multi-Q fuzzy soft sets and Fermatean hesitant fuzzy sets, can be applied in practice to optimise the resolution of complex multi-criteria decision-making problems. The method exceeds traditional approaches such as Fermatean hesitant fuzzy sets, fuzzy soft sets, and Pythagorean fuzzy sets in enhancing the ability to capture higher levels of uncertainty, hesitation, and symmetry in multi-criteria evaluations, thereby supporting more balanced judgments in complex decision-making situations. In this study, we investigate the novel MQFHFSS concept along with the associated operations. The fundamental characteristics of aggregation operators derived from MQFHFSS have been examined to address some complex decision-making issues. Moreover, we discuss some key algebraic features and their different cases, emphasizing the role of symmetry under the influence of MQFHFSS. Finally, we illustrate some numerical examples and solve the real-world decision-making problem by using the proposed technique. Full article

Terminal forensics in large mobile networks is a vital activity for identifying compromised devices and analyzing malicious actions. In contrast, the study described here begins with the domain of terminal forensics as the primary focus, rather than the threat itself. This paper proposes a new multi-criteria decision-making (MCDM) model that integrates complex picture fuzzy sets (CPFS) with the combinative distance-based assessment (CODAS), referred to throughout as complex picture fuzzy CODAS (CPF-CODAS). The aim is to assist in forensic analysis for detecting mobile botnet command and control (C&C) systems. The CPF-CODAS model accounts for the uncertainty, hesitation, and complex numerical values involved in expert decision-making, using degrees of membership as positive, neutral, and negative values. An illustrative forensic case study is constructed where three mobile devices are evaluated by three cybersecurity professionals based on six key parameters related to botnet activity. The results demonstrate that the model can effectively distinguish suspicious devices and support the use of the CPF-CODAS approach in terminal forensics of mobile networks. The robustness, symmetry, and advantages of this model over existing MCDM methods are confirmed through sensitivity and comparison analyses. In conclusion, this paper introduces a novel probabilistic decision-support tool that digital forensic specialists can incorporate into their workflow to proactively identify and prevent actions of mobile botnet C&C servers. Full article