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Emergency Decision Support System in Cardiovascular Health Using T-spherical q-Rung Linear Diophantine Fuzzy Set and Logistic Differential Evolution 使用 T 球 q 容线性二阶模糊集和逻辑微分演化的心血管健康紧急决策支持系统
IF 4.3 3区 计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS Pub Date : 2024-09-09 DOI: 10.1007/s40815-024-01836-7
G Punnam Chander, Sujit Das

In the field of cardiovascular health, the need to make quick decisions in emergency situations is mandatory to save one’s life. During cardiovascular abnormalities, often the patients become unresponsive as the physical and mental conditions become unstable. A meticulous approach that considers various aspects of emergency circumstances is crucial to address these challenges effectively. This paper proposes an effective emergency decision-making method for cardiovascular health using a new T-spherical q-rung linear diophantine fuzzy set (TSqLDFS), logistic differential evolution optimization, and evidential reasoning methodologies. TSqLDFS is employed with a broader scope of bounds for the experts to assess the evaluation values for alternatives corresponding to specified attributes without any restriction. The optimal weights of each attribute are obtained using logistic differential evolution optimization. Then, the aggregated T-spherical q-rung linear diophantine fuzzy values (TSqLDFVs) of each alternative are calculated using evidential reasoning. Subsequently, the score values are evaluated, facilitating the selection of the optimal choice with the highest score. The outcomes of the proposed approach in the context of cardiovascular health have been compared with the existing methods, ensuring its robustness and better performance in medical scenarios.

在心血管健康领域,为了挽救生命,必须在紧急情况下迅速做出决定。在心血管出现异常时,患者往往会因为身体和精神状况不稳定而反应迟钝。要有效应对这些挑战,考虑到紧急情况各个方面的缜密方法至关重要。本文提出了一种有效的心血管健康应急决策方法,该方法采用了新的 T 球q环线性二亲和模糊集(TSqLDFS)、逻辑微分进化优化和证据推理方法。TSqLDFS 的使用范围更广,专家可以不受任何限制地评估与指定属性相对应的备选方案的评估值。使用逻辑微分进化优化法获得每个属性的最优权重。然后,利用证据推理计算出每个备选方案的 T 球形 q 梯度线性二叉模糊值(TSqLDFV)。随后,对分值进行评估,从而选出分值最高的最优选择。所提议的方法在心血管健康方面的结果与现有方法进行了比较,确保了其在医疗场景中的稳健性和更好的性能。
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引用次数: 0
A Probabilistic Hesitant Fuzzy Multi-criteria Group Decision-Making Method Integrated DIBR and Tri-reference Point Theory 融合 DIBR 和三参考点理论的概率犹豫模糊多标准群体决策方法
IF 4.3 3区 计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS Pub Date : 2024-09-08 DOI: 10.1007/s40815-024-01728-w
Feng Zhu, Yumin Liu, Jingjing Sun, Jichao Xu, Ning Wang

As an effective tool to show the fuzziness of qualitative information, the probabilistic hesitant fuzzy set (PHFS) can utilize a group of membership degrees with a clear probability distribution to show the opinions of decision-maker (DM). Given this merit, many probabilistic hesitant fuzzy multi-criteria group decision-making (PHF-MCGDM) methods have been designed. However, most of the existing PHF-MCGDM methods have some limitations, including the difficulty of reflecting DMs’ ambiguous and hesitant preferences for criteria weights and the inability to comprehensively show the impacts of DMs’ irrational behaviors. To address these limitations, this paper develops a novel PHF-MCGDM method that integrates the defining interrelationships between ranked criteria (DIBR) approach and tri-reference point (TRP) theory. First, the PHF-DIBR approach is constructed to determine criteria weights by fully expressing DMs’ ambiguous and hesitant preferences for the importance of criteria. Second, the novel probabilistic hesitant fuzzy correlation coefficient (NPHFCC) is developed for deriving the weights of DMs, which remedies the flaws of the existing correlation coefficients (CC). Moreover, TRP theory is used to describe the psychological behavior effects of DMs and derive the order of alternatives. Finally, the applicability of the proposed method is validated by the case about office flooring material selection, while the sensitivity and comparison analyses are also conducted to further prove its advantages and effectiveness.

作为显示定性信息模糊性的有效工具,概率犹豫模糊集(PHFS)可以利用一组具有明确概率分布的成员度来显示决策者(DM)的意见。鉴于这一优点,人们设计了许多概率犹豫模糊多标准群体决策(PHF-MCGDM)方法。然而,现有的 PHF-MCGDM 方法大多存在一些局限性,包括难以反映 DM 对标准权重的模糊和犹豫偏好,以及无法全面展示 DM 非理性行为的影响。针对这些局限性,本文开发了一种新型的 PHF-MCGDM 方法,该方法整合了排序标准间相互关系定义(DIBR)方法和三参考点(TRP)理论。首先,PHF-DIBR 方法通过充分表达 DM 对标准重要性的模糊和犹豫偏好来确定标准权重。其次,开发了用于推导 DM 权重的新型概率犹豫模糊相关系数(NPHFCC),弥补了现有相关系数(CC)的缺陷。此外,还利用 TRP 理论来描述 DM 的心理行为效应,并推导出备选方案的顺序。最后,通过办公室地板材料选择案例验证了所提方法的适用性,并进行了敏感性分析和比较分析,以进一步证明其优势和有效性。
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引用次数: 0
Design of Fuzzy Delay Compensation Controller Based on Amplitude Compensation Method for Power System with Communication Delay 基于振幅补偿法为带通信延迟的电力系统设计模糊延迟补偿控制器
IF 4.3 3区 计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS Pub Date : 2024-09-07 DOI: 10.1007/s40815-024-01781-5
Jun Li, Hongliang Gao, Yong Wang

In a networked power system, communication delay in the feedback signal during transmission process can have a detrimental impact on the effectiveness of the power system stabilizer (PSS) in suppressing low-frequency oscillations. To address this problem, a controller design method to compensate for short constant time delays is proposed. The proposed approach utilizes Mamdani fuzzy inference system to design a fuzzy delay compensation damping controller (FDCDC) and establishes control rules based on the amplitude compensation method. The proposed FDCDC takes the delayed feedback signal as input and generates additional excitation control signal as output. The controller considers the effect of delay on the performance of PSS controller and compensates for the delay through fuzzy output. To evaluate the effectiveness of the proposed FDCDC, a networked power system model is developed using MATLAB Simulink’s SimPowerSystems library and TrueTime 2.0 toolbox. The aim of this study is to investigate the impact of short constant time delay on the performance of PSS controller in a networked environment and to assess the performance of the proposed controller. The simulation results demonstrate that the proposed FDCDC exhibits good adaptability and robustness in compensating for the effect of delay on PSS control.

在联网电力系统中,传输过程中反馈信号的通信延迟会对电力系统稳定器(PSS)抑制低频振荡的效果产生不利影响。为解决这一问题,提出了一种补偿短恒定时间延迟的控制器设计方法。所提出的方法利用 Mamdani 模糊推理系统来设计模糊延迟补偿阻尼控制器(FDCDC),并根据振幅补偿方法建立控制规则。拟议的 FDCDC 将延迟反馈信号作为输入,并产生额外的激励控制信号作为输出。该控制器考虑了延迟对 PSS 控制器性能的影响,并通过模糊输出对延迟进行补偿。为了评估所提出的 FDCDC 的有效性,我们使用 MATLAB Simulink 的 SimPowerSystems 库和 TrueTime 2.0 工具箱开发了一个联网电力系统模型。本研究的目的是调查网络环境中短恒定时间延迟对 PSS 控制器性能的影响,并评估所提出的控制器的性能。仿真结果表明,所提出的 FDCDC 在补偿延迟对 PSS 控制的影响方面表现出良好的适应性和鲁棒性。
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引用次数: 0
A Fast Finite-Time Output Feedback Control of Uncertain Nonlinear Systems 不确定非线性系统的快速有限时间输出反馈控制
IF 4.3 3区 计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS Pub Date : 2024-09-06 DOI: 10.1007/s40815-024-01678-3
Maoxian Zhao, Zheng Li, Fang Wang

This work reflects on a fast finite-time control issue of nonlinear systems. Both the unmeasurable states of system and unknown nonlinearities are consideblack in this article. A state observer is constructed to eliminate the constraint that the states of system need to be measublack. By utilizing the fuzzy logic systems (FLSs), the unknown nonlinearities are coped with. The singularity issue from the virtual controllers design is skillfully tackled by constructing the smooth piecewise function. The practical fast finite-time stability criterion (fast FTS criterion) is firstly established. Then, a fast finite time output feedback controller is established. By means of the proposed stability criterion, the fast finite time stability of the closed-loop system is guaranteed. Eventually, two examples demonstrate the efficacy of the proposed control strategy.

这项工作反映了非线性系统的快速有限时间控制问题。在本文中,不可测量的系统状态和未知的非线性都被视为黑洞。本文构建了一个状态观测器,以消除系统状态必须是可测量的这一约束。通过利用模糊逻辑系统(FLS),可以解决未知非线性问题。通过构建平滑的分片函数,巧妙地解决了虚拟控制器设计中的奇异性问题。首先建立了实用的快速有限时间稳定性准则(fast FTS criterion)。然后,建立了快速有限时间输出反馈控制器。通过提出的稳定性准则,保证了闭环系统的快速有限时间稳定性。最后,两个实例证明了所提控制策略的有效性。
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引用次数: 0
An Identification Method for Rotor Axis Orbits based on Enhanced Hierarchical Multivariate Fuzzy Entropy and Extreme Learning Machine 基于增强分层多变量模糊熵和极限学习机的转子轴轨道识别方法
IF 4.3 3区 计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS Pub Date : 2024-09-06 DOI: 10.1007/s40815-024-01801-4
Chen Fei, Lan Pengfei, Liu Ting, Zhang Tingting, Wang Kun, Liu Dong, Fan Mao, Wang Bin, Wu Fengjiao

The rotor system is the core equipment of industrial rotating machinery, and ensuring its safety is an essential basis for improving the productivity of the equipment. As a critical monitoring quantity reflecting the operating status of the rotor system, identification models based on axis orbits are effective means for detecting equipment faults. However, most of the existing axis orbit identification models belong to the category of image recognition, and these methods have defects such as unclear physical meaning of features and weak generalization performance. Therefore, the paper returns to the essence of axis orbits and proposes a rotor axis orbit recognition method based on multivariate swing signals, feature extraction and pattern recognition. Firstly, the mutually perpendicular swing signals of the rotor are obtained based on eddy current sensors. Secondly, we propose a feature extraction tool for extracting the multivariate signals named enhanced hierarchical multivariate fuzzy entropy (EHMvFE), a nonlinear dynamics metric based on the enhanced hierarchical decomposition method. Next, the features of axis orbits are extracted by the EHMvFE. Finally, some of the extracted features are input into an extreme learning machine (ELM) for model training, and the effectiveness of the method is verified with the remaining samples. We apply the proposed method to the rotor axis orbit identification case, and the results show that its recognition rate is 98.963%. In comparison experiments with recognition models based on nonlinear dynamics indicators, multivariate signal processing methods, traditional image feature extraction methods, and popular deep learning models, the proposed model shows substantial advantages, verifying the reasonableness and superiority of the proposed method. This study provides a new idea for rotor shaft fault diagnosis, which has significant reference value for promoting the development of intelligent operation and maintenance of industrial equipment.

转子系统是工业旋转机械的核心设备,确保其安全是提高设备生产率的重要基础。作为反映转子系统运行状态的重要监测量,基于轴轨道的识别模型是检测设备故障的有效手段。然而,现有的轴轨道识别模型大多属于图像识别范畴,这些方法存在特征物理意义不明确、泛化性能弱等缺陷。因此,本文回归轴轨道本质,提出一种基于多元摆动信号、特征提取和模式识别的转子轴轨道识别方法。首先,基于涡流传感器获取转子相互垂直的摆动信号。其次,我们提出了一种用于提取多变量信号的特征提取工具,命名为增强分层多变量模糊熵(EHMvFE),这是一种基于增强分层分解法的非线性动力学度量。然后,通过 EHMvFE 提取轴轨道特征。最后,将提取的部分特征输入极端学习机(ELM)进行模型训练,并用其余样本验证该方法的有效性。我们将提出的方法应用于转子轴轨道识别案例,结果表明其识别率为 98.963%。在与基于非线性动力学指标的识别模型、多元信号处理方法、传统图像特征提取方法以及流行的深度学习模型的对比实验中,所提出的模型显示出了巨大的优势,验证了所提方法的合理性和优越性。该研究为转子轴故障诊断提供了新思路,对推动工业设备智能运维的发展具有重要的参考价值。
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引用次数: 0
Exponentially Weighted Moving Average Charts Based on Interval Type-2 Fuzzy Numbers: Analyses of Quality Control and Performance 基于区间 2 型模糊数的指数加权移动平均图表:质量控制和性能分析
IF 4.3 3区 计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS Pub Date : 2024-09-06 DOI: 10.1007/s40815-024-01794-0
Nur Hidayah Mohd Razali, Lazim Abdullah, Ahmad Termimi Ab Ghani, Zati Aqmar Zaharudin, Asyraf Afthanorhan

A control chart is one of the most important techniques used to monitor processes of variability in the manufacturing data. However, conventional charts are relatively not suitable to deal with crisp data. Fuzzy charts are inevitable to evaluate the process with fuzzy data. Nevertheless, much of the data used in daily life cannot be used as a type-1 fuzzy number due to the complexity and uncertainty of information. It is suggested that type-2 fuzzy numbers are more capable in detecting the meaning of process shifts. This paper aims to develop interval type-2 fuzzy (IT2F) Exponentially Weighted Moving Average (IT2F-EWMA) control charts as a new method where the advantages of lower membership and upper membership, which can capture sensitivity and variability in manufacturing data. In the proposed method, we also employed the Best Nonfuzzy Performance method as the defuzzification method instead of the typical centroid method. In order to confirm the performance of the proposed control chart, the average run length (ARL) is calculated and compared to the other three charts. To test the performance of the proposed EWMA, twenty samples were analysed to identify the defects in the fertilizers’ production. Based on the result of the conventional chart, 8 out of 20 samples are “uncontrolled”. In contrast, the type-1 chart found 16 samples are “uncontrolled”, whereas IT2F-EWMA found 18 samples are “out of control”. Consequently, it is proven that IT2F-EWMA is the best method to be used in dealing with vague and fuzzy data since it is more precise and vulnerable. Lastly, the ARL test shows that IT2F-EWMA charts outperform the other control charts.

控制图是用于监控生产数据变化过程的最重要技术之一。然而,传统图表相对不适合处理清晰数据。模糊图表是评估模糊数据过程的必然选择。然而,由于信息的复杂性和不确定性,日常生活中使用的很多数据都不能用作 1 型模糊数。有人认为,2 型模糊数更能检测过程变化的含义。本文旨在开发区间 2 型模糊(IT2F)指数加权移动平均(IT2F-EWMA)控制图,作为一种新方法,它具有下成员和上成员的优点,可以捕捉生产数据中的敏感性和变异性。在所提出的方法中,我们还采用了最佳非模糊性能法作为去模糊化方法,而不是典型的中心法。为了确认所提控制图的性能,我们计算了平均运行长度(ARL),并与其他三个控制图进行了比较。为了测试拟议 EWMA 的性能,对 20 个样本进行了分析,以确定肥料生产中的缺陷。根据传统图表的结果,20 个样本中有 8 个 "失控"。相比之下,1 型图表发现 16 个样品 "失控",而 IT2F-EWMA 发现 18 个样品 "失控"。因此,事实证明,IT2F-EWMA 是处理模糊数据的最佳方法,因为它更精确、更脆弱。最后,ARL 检验表明,IT2F-EWMA 图表优于其他控制图表。
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引用次数: 0
A Fault Diagnosis Method for Manufacturing System Based on Adaptive BRB Considering Environmental Disturbance 基于考虑环境干扰的自适应 BRB 的制造系统故障诊断方法
IF 4.3 3区 计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS Pub Date : 2024-09-06 DOI: 10.1007/s40815-024-01799-9
Boying Zhao, Lingkai Kong, Wei He, Guohui Zhou, Hailong Zhu

Timely fault diagnosis is essential to ensure the reliable performance of manufacturing systems. Aiming at the problems of insufficient prior information and incomplete reliability of monitoring data affected by environmental disturbance during the diagnosis process in manufacturing system, an adaptive belief rule base with index uncertainty (ABRB-u) is proposed. Initially, the adaptive method is used to accurately estimate the initial parameters, facilitating the construction of belief rule base (BRB). Subsequently, considering the limitations of the current model in dealing with uncertain monitoring data, a method for transforming matching degree is introduced, which incorporates the index uncertainty into the model. Finally, the results of the case study demonstrate that this method not only achieves favorable diagnostic outcomes in the absence of prior information but also successfully addresses the challenge of incomplete reliability in monitoring data. This offers a promising solution for fault diagnosis in manufacturing systems.

及时的故障诊断对确保制造系统的可靠性能至关重要。针对制造系统诊断过程中受环境干扰影响的先验信息不足和监测数据可靠性不高的问题,提出了一种具有指数不确定性的自适应信念规则库(ABRB-u)。首先,利用自适应方法精确估计初始参数,从而促进信念规则库(BRB)的构建。随后,考虑到当前模型在处理不确定监测数据时的局限性,引入了一种转换匹配度的方法,将指数的不确定性纳入模型。最后,案例研究结果表明,这种方法不仅能在没有先验信息的情况下取得良好的诊断结果,还能成功解决监测数据不完全可靠的难题。这为制造系统的故障诊断提供了一个前景广阔的解决方案。
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引用次数: 0
Two-Warehouse Green Inventory Cloudy Fuzzy Model for Deteriorating Items with Two-Level Trade Credit and Shortages 具有两级贸易信贷和短缺的变质物品的双仓库绿色库存云模糊模型
IF 4.3 3区 计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS Pub Date : 2024-08-23 DOI: 10.1007/s40815-024-01780-6
Subhashree Parida, Milu Acharya

In the present era, the most delicate environmental issue is global warming, and because of this, countries across the globe are trying to manage the most hazardous emissions by making certain investments in projects to promote green industrial practices. In the current study, the inventory models are developed by including the emission of CO2 from transportation which is controlled by the optimum investments in green technology (GT). Here, sustainable deteriorating inventory models in both crisp and cloudy fuzzy (CF) environments with a two-level trade credit scheme are proposed to boost the demand, where a delay in payment opportunity is there for suppliers and retailers. In this credit scenario, delay in payment options is given from the supplier to the retailer, and also from the retailer to the customer. In the present research, two warehouses are considered to manage the stock-out situation. For the model problems, demand is considered to be time dependent, where a multiple prepayment option for the purchasing cost involving an installment is provided to the retailers. Here the solution processes for the proposed models suggest algorithms, and then a numerical approach is followed to test the optimality criteria. The said optimum results are also presented in graphs. Again, a comparative study of the obtained results concerning the models is also highlighted. Lastly, a sensitivity analysis is performed to study the influence of variations in input parameters, which allows drawing some managerial insights.

当今时代,最微妙的环境问题是全球变暖,正因为如此,全球各国都在努力通过对项目进行一定的投资来管理最有害的排放物,以促进绿色工业实践。在当前的研究中,通过对绿色技术(GT)的最佳投资来控制运输过程中的二氧化碳排放,从而建立了库存模型。在此,我们提出了在清晰模糊(CF)环境和多云模糊(CF)环境下的可持续恶化库存模型,以及两级贸易信贷计划,以促进供应商和零售商延迟付款的需求。在这种信用方案中,从供应商到零售商以及从零售商到客户都有延迟付款的选择。在本研究中,考虑了两个仓库来管理缺货情况。在模型问题中,需求被认为与时间有关,零售商可以选择多种预付款方式来支付分期付款的采购成本。在此,对所建议模型的求解过程提出了算法建议,然后采用数值方法来测试优化标准。上述最优结果也以图表形式呈现。此外,还强调了对所获得的模型结果进行比较研究。最后,还进行了敏感性分析,以研究输入参数变化的影响,从而得出一些管理见解。
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引用次数: 0
An Interval Intuitionistic Fuzzy Characterization Method Based on Heterogeneous Big Data and Its Application in Forest Land Quality Assessment 基于异构大数据的区间直觉模糊特征描述方法及其在林地质量评估中的应用
IF 4.3 3区 计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS Pub Date : 2024-08-19 DOI: 10.1007/s40815-024-01765-5
Junzhe Zhang, Jian Lin, Tao Wu

With the rapid advancement and ongoing evolution of data information technology, the methods and approaches for data collection have become increasingly varied. The synthesis of heterogeneous big data to minimize information loss during the aggregation process poses a significant challenge. In practical applications, fuzzy dimensionality reduction characterization has proven to be an effective approach for handling heterogeneous big data. In this study, a novel approach is proposed for characterizing and evaluating heterogeneous big data using an interval intuitionistic fuzzy framework. We establish the interval intuitionistic fuzzy transformation method for large-scale quantitative data by defining satisfaction intervals, dissatisfaction intervals, and hesitation intervals. To integrate calculation and processing for linguistic evaluation information with different granularities, a transformation formula that handles multi-granularity uncertain linguistic information and interval intuitionistic fuzzy numbers is introduced. The proposed formula aggregates heterogeneous attribute values into interval intuitionistic fuzzy numbers. We employ interval intuitionistic fuzzy entropy to determine the objective weight of each evaluation indicator. Subsequently, the interval intuitionistic fuzzy comprehensive evaluation information for each alternative scheme, enabling effective ranking based on the information, is derived. Finally, the applicability of our proposed method is verified through a case study conducted on forest land in the county area of Fujian province. This case study comprehensively assesses and ranks the forest land quality in 16 sample plots. The evaluation serves as a theoretical framework for advancing sustainable development and conservation initiatives about forest land within the county.

随着数据信息技术的快速发展和不断演进,数据收集的方法和途径也变得越来越多样化。如何对异构大数据进行综合处理,最大限度地减少聚合过程中的信息损失,是一项重大挑战。在实际应用中,模糊降维表征已被证明是处理异构大数据的有效方法。本研究提出了一种利用区间直观模糊框架表征和评估异构大数据的新方法。通过定义满意区间、不满意区间和犹豫区间,我们建立了大规模定量数据的区间直观模糊变换方法。为了整合不同粒度的语言评价信息的计算和处理,我们引入了一种处理多粒度不确定语言信息和区间直觉模糊数的转换公式。所提出的公式将异质属性值聚合为区间直观模糊数。我们采用区间直觉模糊熵来确定每个评价指标的客观权重。随后,得出每个备选方案的区间直觉模糊综合评价信息,从而根据这些信息进行有效排序。最后,通过对福建省县域林地的案例研究,验证了我们所提方法的适用性。该案例研究对 16 个样本地块的林地质量进行了全面评估和排序。评估结果可作为推进县域林地可持续发展和保护措施的理论框架。
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引用次数: 0
A Novel Interval Type-2 Fuzzy CPT-TODIM Method for Multi-criteria Group Decision Making and Its Application to Credit Risk Assessment in Supply Chain Finance 用于多标准群体决策的新型区间-2 型模糊 CPT-TODIM 方法及其在供应链金融信用风险评估中的应用
IF 4.3 3区 计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS Pub Date : 2024-08-17 DOI: 10.1007/s40815-024-01759-3
Wen Li, Luqi Wang, Obaid Ur Rehman

The assessment of credit risk in supply chain finance (SCF) stands as a pivotal procedure in facilitating enterprises to identify appropriate financing solutions, reduce financing costs, enhance capital utilization efficiency, and mitigate the risk of debt default. Multi-criteria group decision-making (MCGDM), a systematic evaluation tool, is widely used for the assessment of both qualitative and quantitative criteria. However, the conventional framework of MCGDM exhibits limitations in addressing scenarios characterized by high uncertainty in risk information, disparity in weights among decision-makers (DMs) and criteria, alongside complex and non-linear risk perception. To address these limitations, this paper introduces an analytical model that integrates Interval Type-2 Fuzzy Sets (IT2FSs), Cumulative Prospect Theory (CPT), and the TODIM (an acronym from Portuguese for Interactive and Multicriteria Decision Making) method to evaluate credit risk in SCF. Firstly, the IT2FSs are utilized to represent high uncertainty in risk assessment information of DMs. Secondly, the Dice Similarity is applied to determine the weights of DMs. Then, we seek to improve the Criterion Importance Through Intercriteria Correlation (CRITIC) method by addressing its limitations and further integrating it with the Bayesian Best–Worst Method (BBWM), offering a robust computational framework of integrated weights for criteria. Finally, the CPT-TODIM method based on IT2FSs is applied in a real case from Ping An Bank. Through rigorous sensitivity and comparative analyses conducted within the real-world context of SCF credit risk assessments, the proposed model’s theoretical robustness and practical applicability are emphatically validated.

供应链金融(SCF)中的信用风险评估是帮助企业确定合适的融资方案、降低融资成本、提高资金利用效率以及降低债务违约风险的关键程序。多标准团体决策(MCGDM)作为一种系统化的评估工具,被广泛应用于定性和定量标准的评估。然而,传统的 MCGDM 框架在处理风险信息高度不确定、决策者(DM)和标准之间权重不一致以及复杂和非线性风险感知等情况时表现出局限性。为解决这些局限性,本文介绍了一种分析模型,该模型综合了区间-2 型模糊集(IT2FSs)、累积前景理论(CPT)和 TODIM(葡萄牙语 "交互式多标准决策 "的缩写)方法,用于评估 SCF 中的信贷风险。首先,利用 IT2FSs 来表示 DM 风险评估信息中的高度不确定性。其次,利用骰子相似性确定 DM 的权重。然后,我们设法改进 "通过标准间相关性确定标准重要性"(CRITIC)方法,解决其局限性,并进一步将其与贝叶斯最佳-最差法(BBWM)相结合,为标准的综合权重提供一个稳健的计算框架。最后,将基于 IT2FSs 的 CPT-TODIM 方法应用于平安银行的实际案例中。通过在 SCF 信贷风险评估的实际背景下进行严格的敏感性和比较分析,所提出模型的理论稳健性和实际适用性得到了有力的验证。
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引用次数: 0
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International Journal of Fuzzy Systems
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