基于区间犹豫模糊集的VIKOR广告方案决策

为帮助企业选择合适的广告方案,因此将区间犹豫模糊集的VIKOR方法用于广告方案评选中。为充分考虑专家意见,引入区间犹豫模糊集,并将其与VIKOR方法相结合,形成一种科学有效的决策方法。文末以例子说明了方法的可行性。     

基于区间毕达哥拉斯模糊相似测度的多属性群决策方法研究

毕达哥拉斯模糊集是对模糊集和直觉模糊集的推广,本文在实型广义加权平均算子的基础上提出一种新的测度——区间毕达哥拉斯模糊余弦相似测度,并提出了一种基于该相似测度的多属性决策方法。首先,介绍区间毕达哥拉斯模糊数的概念以及运算法则;其次提出了区间毕达哥拉斯模糊有序加权余弦相似测度,分析了其单调性、有界性等相关性质以及其多种推广形式,并将其运用至多属性群决策问题中。最后,将这种群决策方法运用在推选优秀学生的方案选择中,通过改变参数及权重向量取值的方法得到不同集结结果,体现了本文提出的方法的有效性及可行性。     

基于云模型的TOPSIS决策方法研究

针对区间型多准则决策问题,本文提出一种基于云模型的TOPSIS决策方法。首先,提出将普通的区间型数值转化为能体现模糊性与随机性信息的云决策矩阵;接着,给出了正负云理想云的确定方法以及云模型的距离测度公式,在此基础上发展了一种云TOPSIS方法;最后,通过人才选择的算例验证了该方法的有效性和可行性。     

多属性决策方法及其在节能减排中的应用研究

通过对直觉模糊数的区间化处理,把直觉模糊多属性决策转化为属性值为区间数的多属性决策.基于决策者的主观偏好值和客观偏好值的总偏差最小化原则得到属性权重,从而给出了一种直觉模糊多属性决策方法.最后利用所提出的方法对企业的节能减排效果进行了评价研究.     

基于VIKOR法输电线路工程全过程机械化施工方案群决策研究

实施输电线路工程全过程机械化施工,设计和制定一个合理和完善的施工方案是一基本要求和前提条件,而这一施工方案的制定需要综合评判各种因素影响,由于这些影响因素在实际中往往又是定性的、难以量化分析的,这一问题给方案评判工作带来很大的困难。本文基于VIKOR法原理,结合全过程机械化施工的施工目标等要求,构建了全过程机械化施工方案的评价指标,对指标体系中的各种定性因素按照区间数、直觉模糊数和语言变量等进行了有效处理,确定了各评价指标权重系数,并利用专家小组的知识与经验,提出了输电线路工程全过程机械化施工方案的群决策方法,通过工程实例分析,也说明本文提出的方法是可信和合理的。     

n-直觉多边形模糊有序加权距离算子及其在绿色供应商评价中的应用

n-直觉多边形模糊集(n-intuitionistic polygonal fuzzy sets)是三角形直觉模糊集(TIFS)和梯形直觉模糊集(TRIFS)的扩展形式。针对决策信息为n-直觉多边形模糊集的多属性决策问题,提出了一种基于有序加权距离测度的多属性决策方法。在分析现有n-直觉多边形模糊集距离测度方法不足的基础上,首先概括了n-IPFSs的概念和运算法则;其次提出了n-直觉多边形模糊有序加权距离算子(n-IPFOWD),并给出了该测度的权重确定方法;最后提出了一种基于n-直觉多边形模糊有序加权距离算子(n-IPFOWD)的多属性决策方法,并用绿色供应商评价实例验证了该方法的可行性和有效性。     

区间规划在证券投资组合问题中的运用

本文基于区间规划的方法研究了摩擦市场的投资组合选择问题。文中把风险证券的收益率、投资风险及证券的流动性用区间数来描述,并结合绝对偏差风险函数的思想建立了一种关于区间数的证券投资组合选择模型。最后利用区间数的两种序关系将所提出的模糊线性规划问题转化为普通的参数线性规划问题进而求其解。     

基于IVHF-TODIM的高岭土增白工艺综合评价

为对广东某高岭土矿所可能选用的浮选法、磁选法、化学法等三种增白工艺进行综合评价,在区间犹豫模糊背景下提出一种改进的IVHF-TODIM多属性决策方法。首先,结合决策者在进行高岭土选矿时犹豫模糊的状态,提出在区间犹豫模糊环境下进行决策,其次由于决策者的有限心理特征本文提出运用TODIM决策方法来进行选矿。最终结果表明:浮选法为该高岭土矿最佳的增白工艺,同时IVHF-TODIM方法用于高岭土增白工艺决策是可行的。     

基于模糊时间序列的预测模型——以上证指数为例

模糊理论使用语义变量本身所蕴含的特性,能减少处理问题时的不确定性所带来的困扰,被广泛的应用于各种领域的研究。首先回顾了基于模糊理论的模糊时间序列定义,对现有的模糊时间序列模型进行分析;在此基础上提出了一种新的模糊时间序列预测方法,以上证指数为对象进行了拟合。从结果看,新的基于模糊时间序列预测方法在MSN、平均误差(%)和标准误差(%)等指标上要优于现有的的预测方法。     

建筑工程中的模糊网络计划优化方法

本文在FNET基础上结合PERT网络优化方法,提出一种模糊网络计划优化方法。 一、模糊网络工作持续时间表示及模糊数确定 在传统施工计划中,时间一般用精确值表示。精确时间是一个单一的数值,而实际上时间往往是一个可能的时间范围。根据时间完成的可能性,时间共有4种表示:精确时间、区间时间、无下界时间和无上界时间。 由于建筑施工活动中存在许多的不确定性因素。因而工作持续时间具有模糊性,使得最终的工期也具有模糊性。这类于作时间可用模糊数表示,见图1。     

Real options valuation of an IoT based healthcare device with interval Type-2 fuzzy numbers

Medical devices have attained increasing importance in diagnosis and treatment in the healthcare system in recent years due to technological advancements. Along with this progress, the IoT property in these devices reduces the supply time, which is very critical in increasing their up periods. Often, certain medical devices used in treatment require a major portion of the investment. Such huge capital goods are generally irreversible investments. Depending on the strategic view and irreversibility, medical device assessment requires a sophisticated evaluation method called real options. However, there are many ambiguities during the investment period and while gathering the data for the evaluation. Interval type-2 fuzzy logic fits like a glove for such vague conditions and lack of data. In this study, an interval type-2 fuzzy set integrated real option analysis is offered for medical treatment device evaluation. Possibilistic mean value and possibilistic variance of interval type-2 fuzzy sets are found for the purpose of the real options analysis. This offered method is applied to a real case in a healthcare system. Sensitivity analysis provides many different viewpoints. At the end of the procedure, the institution decides whether to perform the investment now or one year later.     

A supplier evaluation model based on customer demand in blockchain tracing anti-counterfeiting platform project management

This study assesses supplier selection at the beginning of project management to establish an evaluation system corresponding to blockchain tracing anti-counterfeiting platforms (BTAP). First, this paper determines 20 evaluation criteria from the four dimensions of platform overview, core technology, application support, and operations management. On this basis, multi-criteria decision making (MCDM) based on customer needs is proposed, which consists of three main steps. First, quality function deployment (QFD) and the best and worst method (BWM) are used to evaluate the four dimensions of the BTAP and specific evaluation criteria from the perspective of customers to obtain the criteria weight. Then, this method uses the extended Vlse Kriterjumska Optimizacija I Kompromisno Resenje (VIKOR) approach to sort the alternatives. Finally, the improved decision making trial and evaluation laboratory (DEMATEL) method is used to analyse the relationships between the 20 criteria in the four dimensions. The feasibility and effectiveness of this method are verified by an example. According to the sensitivity analysis and comparative analysis, the results show that this method can evaluate blockchain anti-counterfeiting enterprises. The main conclusions are as follows: the core technology is the most important factor influencing the choice of a BTAP project, and the role of application support in evaluation cannot be ignored.     

Extracting cover sets from free fuzzy sorting data

Assignment of items to multiple categories requires suitable statistical methods. The present paper provides a new approach to solve this task. The concept of fuzzy sets is extended to cover sets (sets of overlapping clusters) in a simple manner introducing a vector of item membership sums. The application of the new concept is exemplified by modifying the fuzzy cluster analysis algorithm of Kaufman and Rousseeuw (Finding groups in data: an introduction to cluster analysis, 1990) to cover set cluster analysis appropriately. Wide equivalence of the numerical problems is demonstrated from Lagrange multipliers and Karush-Kuhn-Tucker conditions. Additionally, some extensions are introduced to the algorithm to improve its behavior for suboptimal large or small numbers of clusters. The adapted algorithm in most cases reproduces single sortings for correct numbers of clusters. Two applications to empirical free fuzzy sorting data sets are provided. Limitations of the algorithm are discussed.     

基于DFNN的金融股指预测及金融非线性系统辨识研究

针对证券市场内部结构的复杂性、外部因素的多变性,本文采用动态模糊神经网络（DFNN）进行金融股指预测。DFNN能够实现在线学习,并且参数估计与结构辨识同时进行;同时采用误差下降率（ERR）修剪技术,保证网络拓扑结构不会持续增长,避免了过拟合及过训练现象,确保了DFNN的泛化能力。本文以上证指数为例．通过与同样以高斯函数作为传递函数的RBF算法预测结果的比较和分析．表明DFNN预测上证指数的偏差较小,预测的方向准确性较高。通过DFNN模型提取的模糊规则对金融系统运行模式进行分析．为研究金融非线性系统辨识提供了启发性思路。     

基于L-R型模糊DEA的模型改进

本文首先介绍了前人建立的模糊DEA模型,考虑这些模型存在人为提高效率值、未充分利用模糊信息、计算量过大的问题,建立了一个新的L-R DEA模型,该模型基于α-截集的模糊数变换,将指标集分解为多个指标子集,分别对每个子集进行效率计算,最后应用熵值法确定决策单元(DMU)的最终效率值。该方法可以对DMU进行充分排序,扩大了DEA的应用领域。文末通过一算例来说明新模型的有效性。     

第三方物流供应商选择的模糊决策方法

提出了一套第三方物流供应商选择的评价指标体系,并运用模糊决策和层次分析法解决了第三方物流供应商的选择问题,最后给出了一个实际算例.     

Epidemic forecasting with a new fuzzy regression equation

The traditional fuzzy regression model involves two solving processes. First, the extension principle is used to derive the membership function of extrapolated values, and then, attempts are made to include every collected value with a membership degree of at least h in the fuzzy regression interval. However, the membership function of extrapolated values is sometimes highly complex, and it is difficult to determine the h value, i.e., the degree of fit between the input values and the extrapolative fuzzy output values, when the information obtained from the collected data is insufficient. To solve this problem, we proposed a simplified fuzzy regression equation based on Carlsson and Fullér’s possibilistic mean and variance method and used it for modeling the constraints and objective function of a fuzzy regression model without determining the membership function of extrapolative values and the value of h. Finally, we demonstrated the application of our model in forecasting pneumonia mortality. Thus, we verified the effectiveness of the proposed model and confirmed the potential benefits of our approach, in which the forecasting error is very small.     

A fuzzy service quality based approach for performance evaluation of educational units

Educational unites try to make efficient use of resources to achieve higher service qualities. This paper presents a new hybrid fuzzy approach based on service quality for performance evaluation of educational units. The proposed approach includes four stages: firstly, evaluation and gap analysis of students' expectations and perceptions in the service quality of educational units are performed based on fuzzy SERVQUAL questionnaires. In the second stage, the corresponding weights of dimensions and sub-dimensions of SERVQUAL are determined via fuzzy AHP method. In the third stage, the educational units are ranked using fuzzy TOPSIS method with respect to service quality sub-dimensions. Finally, a fuzzy DEA method is applied to determine the efficiency of educational units based on their service quality ratings. The approach is used for performance evaluation of eight higher education institutes in Iran. The proposed approach increased the accuracy of performance evaluation of educational units based on service quality.     

Ranking by competence using a fuzzy approach

New approaches in economics of education consider the concept of competence to bringing in the multidimensional feature of labour force quality. Competence includes knowledge, skills, behaviours and attitudes. Each individual suits the combination of these elements to perform a task. Thus, different combinations expectedly lead to different task performance inside an occupation. In this paper, we focus on individual heterogeneity in homogeneous job requirements context. In sequel, we establish a theorem on unit simplex that potentially provides a ranking system by competence. The system is designed under a grade of membership framework, with a technique based on fuzzy sets theory. Under certain conditions, the referred theorem allows a mapping of individual multidimensional feature into the interval [0, 1]. So, the greater the value along this interval, the more competent the individual. An empirical study of a banking sector activity illustrates our research.     

Distribution-free Approximate Methods for Constructing Confidence Intervals for Quantiles

Quantile estimation is important for a wide range of applications. While point estimates based on one or two order statistics are common, constructing confidence intervals around them, however, is a more difficult problem. This paper has two goals. First, it surveys the numerous distribution-free methods for constructing approximate confidence intervals for quantiles. These techniques can be divided roughly into four categories: using a pivotal quantity, resampling, interpolation, and empirical likelihood methods. Second, a method based on the pivotal quantity that has received limited attention in the past is extended. Comprehensive simulation studies are used to compare performance across methods. The proposed method is simple and performs similarly to linear interpolation methods and a smoothed empirical likelihood method. While the proposed method has slightly wider interval widths, it can be calculated for more extreme quantiles even when there are few observations.