基于语言分布评估的DEMATEL及在可持续循环伙伴关键因素分析中的应用

决策试验和评估实验室(DEMATEL)方法,用以对相互影响的因素间的关系进行分析,确定关键因素.针对直接影响关系矩阵在给出时存在残缺判断以及元素为语言分布评估的情况,文章提出语言分布评估的DEMATEL方法,并将其运用于具有不同语义的语言术语集中.首先,文章定义了语言标度下的语言分布评估相似度公式,使用语言分布评估加权平均算子(DTWA)来对专家所提供的决策信息的残缺元素进行补全.其次,文章采用语言术语集的数值标度,将专家的语言分布评估决策信息进行数值化.综合所有专家的数值化决策信息进行DEMATEL过程,获取各因素的重要性权重,得到因素之间的影响关系,并将因素分为两组:原因组和效果组.最后,文章将所提出的方法应用于可持续循环伙伴选择的实际案例.     

基于模糊语言评估和GIOWA算子的多属性群决策方法

研究了方案的属性评估信息以模糊语言形式给出的多属性群决策问题,定义了一种模糊语言评估标度并给出其相应的三角模糊数表达方式.利用广义的导出有序加权平均(GIOWA)算子,对专家所给出的对应于各方案的属性评估信息进行了集结,并提出了一种基于模糊语言评估和GIOWA算子的多属性群决策方法.最后进行了实例分析.     

基于模糊语言判断矩阵和FIOWA算子的有限方案决策法

定义一种模糊的导出有序加权平均(FIOWA)算子，给出方案之间比较的模糊语言标度。运用模糊语言标度构造出模糊语言判断矩阵，并提出一种基于模糊语言判断矩阵和FIOWA算子的有限方案决策方法。该法利用FIOWA算子对模糊语言信息进行集结，并利用已有的三角模糊数排序公式求得决策方案的排序。     

无标度网络模型的局部笛卡尔积北大核心

本文首先给出网络的最小控制集的概念,先从理论上用笛卡尔积算法引入对两个网络的最小控制集做笛卡尔积算法的具体方式,进而给出对两个网络中的最小控制集做笛卡尔积的过程,并说明所得笛卡尔积网络的拓扑结构.最后,例举两个网络模型来解释做笛卡尔积运算的方式,并计算了笛卡尔积网络的平均度,进而验证了笛卡尔积网络的无标度性.     

标度指数不大于2的无标度网络的若干性质

将标度指数不大于2的无标度网络称为亚标度网络.通过引入度秩函数研究了亚标度网络的最大度、平均度以及拓扑结构的非均匀性,通过与标度指数大于2的无标度网络对比,揭示了亚标度网络若干特殊性质.     

构建不确定语言型多属性决策的投影模型

研究不确定语言型多属性决策评价结果与决策者对方案的偏好信息之间存在偏差的问题.通过建立与区间型语言标度对应的术语指标矩阵,及方案综合属性值与决策者主观偏好值之间的投影模型,确定属性的权重,然后运用加权法得到方案的综合属性值,利用已有的可能度矩阵排序公式得到决策方案的排序.构建了一种基于方案综合属性质与决策者主观偏好值之间的投影模型,通过算例对该方法的实用性和有效性进行了证明.     

水质评价涉及的因子权重与定权方法

水质评价涉及属性权与熵权两种权重.熵权表征因子的分类能力,由因子的隶属度向量通过计算信息熵确定.属性权表征因子重要性程度的差异,用途是使不同因子的隶属度具有"可比性",但定权方法众说不一.指出,因子重要性程度差异源于因子属性与因子取值无关,并且表征这种差异等同于对因子接重要性排序.AHP的比例标度判断矩阵为因子排序提供了合理的数据条件,但基于"一致性检验"的特征根排序法受到质疑;FAHP也因没有彻底摆脱"一致性",所以建立的排序方法有局限性.为此,通过标度变换将比例标度转化为评分标度,利用评分标度的可加性把判断矩阵中由评分标度确定的因子的序关系转化为因子排序.由此建立不受"一致性"干扰的定权方法.     

直觉不确定纯语言信息的集结方法及在群决策中的应用

直觉不确定纯语言标度变量是直觉模糊数和不确定纯语言标度变量的拓展,本文定义了直觉不确定纯语言标度变量的运算法则,提出了一些基于直觉不确定纯语言评估标度及其运算法则的信息集结算子,在此基础上,给出了一种专家权重、属性权重及属性值均以语言标度形式给出的直觉不确定纯语言信息的集结方法,并将此方法应用到群决策中,通过实例分析说明了该方法的有效性和可行性。     

探讨几类具有完全图核心的网络模型的优美性

在复杂网络研究中,人们需要建立网络模型,无标度图就是这样的一种网络模型.我们发现具有完全图核心的网络模型可以演变成无标度图.具有完全图核心的几种网络模型的优美性得到研究.     

基于直觉纯语言集结算子的多属性群决策方法

定义了直觉纯语言集及其运算法则和直觉纯语言变量的数学期望和精确函数,提出了一些基于直觉纯语言评估标度及其运算法则的信息集结算子。在此基础上,给出了一种专家权重、属性权重及属性值均以语言标度形式给出的直觉纯语言信息集结方法,并将此方法应用到多属性群决策中,实例分析表明了该方法的有效性和可行性。     

A comparative study of the numerical scales and the prioritization methods in AHP

In the analytic hierarchy process (AHP), a decision maker first gives linguistic pairwise comparisons, then obtains numerical pairwise comparisons by selecting certain numerical scale to quantify them, and finally derives a priority vector from the numerical pairwise comparisons. In particular, the validity of this decision-making tool relies on the choice of numerical scale and the design of prioritization method. By introducing a set of concepts regarding the linguistic variables and linguistic pairwise comparison matrices (LPCMs), and by defining the deviation measures of LPCMs, we present two performance measure algorithms to evaluate the numerical scales and the prioritization methods. Using these performance measure algorithms, we compare the most common numerical scales (the Saaty scale, the geometrical scale, the Ma–Zheng scale and the Salo–Hämäläinen scale) and the prioritization methods (the eigenvalue method and the logarithmic least squares method). In addition, we also discuss the parameter of the geometrical scale, develop a new prioritization method, and construct an optimization model to select the appropriate numerical scales for the AHP decision makers. The findings in this paper can help the AHP decision makers select suitable numerical scales and prioritization methods.     

Applying a direct multi-granularity linguistic and strategy-oriented aggregation approach on the assessment of supply performance

Supply performance has the active continuity behaviors, which covers the past, present and future of time horizons. Thus, supply performance possesses distinct uncertainty on individual behavior, which is inadequate to assess with quantification. This study utilizes the linguistic variable instead of numerical variable to offset the inaccuracy on quantification, and employs the fitting linguistic scale in accordance with the characteristic of supply behavior to enhance the applicability. Furthermore, the uniformity is introduced to transform the linguistic information uniformly from different scales. Finally, the linguistic ordered weighted averaging operator with maximal entropy applies in direct to aggregate the combination of linguistic information and product strategy to ensure the assessment results meeting the enterprise requirements, and then to emulate mental decision making in humans by the linguistic manner.     

Influence of aggregation and measurement scale on ranking a compromise alternative in AHP

Analytic Hierarchy Process (AHP) is one of the most popular multi-attribute decision aid methods. However, within AHP, there are several competing preference measurement scales and aggregation techniques. In this paper, we compare these possibilities using a decision problem with an inherent trade-off between two criteria. A decision-maker has to choose among three alternatives: two extremes and one compromise. Six different measurement scales described previously in the literature and the new proposed logarithmic scale are considered for applying the additive and the multiplicative aggregation techniques. The results are compared with the standard consumer choice theory. We find that with the geometric and power scales a compromise is never selected when aggregation is additive and rarely when aggregation is multiplicative, while the logarithmic scale used with the multiplicative aggregation most often selects the compromise that is desirable by consumer choice theory.     

Numerical scales generated individually for analytic hierarchy process

Because individual interpretations of the analytic hierarchy process (AHP) linguistic scale vary for each user, this study proposes a novel framework that AHP decision makers can use to generate numerical scales individually, based on the 2-tuple linguistic modeling of AHP scale problems. By using the concept of transitive calibration, individual characteristics in understanding the AHP linguistic scale are first defined. An algorithm is then proposed for detecting the individual characteristics from the linguistic pairwise comparison data that is associated with each of the AHP individual decision makers. Finally, a nonlinear programming model is proposed to generate individual numerical scales that optimally match the obtained individual characteristics. Two well-known numerical examples are re-examined using the proposed framework to demonstrate its validity.     

Projection method for multiple criteria group decision making with incomplete weight information in linguistic setting

Multiple criteria group decision making (MCGDM) problems have become a very active research field over the last decade. Many practical problems are often characterized by MCGDM. The aim of this paper is to develop a new approach for MCGDM problems with incomplete weight information in linguistic setting based on the projection method. Firstly, to reflect the reality accurately, a method to determine the weights of decision makers in linguistic setting is proposed by calculating the degree of similarity between 2-tuple linguistic decision matrix given by each decision maker and the average 2-tuple linguistic decision matrix. By using the weights of decision makers, all individual 2-tuple linguistic decision matrices are aggregated into a collective one. Then, to determine the weight vector of criteria, we establish a non-linear optimization model based on the basic ideal of the projection method, i.e., the optimal alternative should have the largest projection on the 2-tuple linguistic positive ideal solution (TLPIS). Calculate the 2-tuple linguistic projection of each alternative on the TLPIS and rank all the alternatives according to the 2-tuple linguistic projection value. Finally, an illustrative example is given to demonstrate the calculation process of the proposed method, and the validity is verified by comparing the evaluation results of the proposed method with that of the technique for order preference by similarity to ideal solution (TOPSIS) method.     

The OWA-based consensus operator under linguistic representation models using position indexes

When using linguistic approaches to solve decision problems, we need linguistic representation models. The symbolic model, the 2-tuple fuzzy linguistic representation model and the continuous linguistic model are three existing linguistic representation models based on position indexes. Together with these three linguistic models, the corresponding ordered weighted averaging operators, such as the linguistic ordered weighted averaging operator, the 2-tuple ordered weighted averaging operator and the extended ordered weighted averaging operator, have been developed, respectively. In this paper, we analyze the internal relationship among these operators, and propose a consensus operator under the continuous linguistic model (or the 2-tuple fuzzy linguistic representation model). The proposed consensus operator is based on the use of the ordered weighted averaging operator and the deviation measures. Some desired properties of the consensus operator are also presented. In particular, the consensus operator provides an alternative consensus model for group decision making. This consensus model preserves the original preference information given by the decision makers as much as possible, and supports consensus process automatically, without moderator.     

On consistency measures of linguistic preference relations

Inspired by the concept of deviation measure between two linguistic preference relations, this paper further defines the deviation measure of a linguistic preference relation to the set of consistent linguistic preference relations. Based on this, we present a consistency index of linguistic preference relations and develop a consistency measure method for linguistic preference relations. This method is performed to ensure that the decision maker is being neither random nor illogical in his or her pairwise comparisons using the linguistic label set. Using this consistency measure, we discuss how to deal with inconsistency in linguistic preference relations, and also investigate the consistency properties of collective linguistic preference relations. These results are of vital importance for group decision making with linguistic preference relations.     

An integrated model-based interactive approach to FMAGDM with incomplete preference information

Incomplete fuzzy preference relations, incomplete multiplicative preference relations, and incomplete linguistic preference relations are very useful to express decision makers’ incomplete preferences over attributes or alternatives in the process of decision making under fuzzy environments. The aim of this paper is to investigate fuzzy multiple attribute group decision making problems where the attribute values are represented in intuitionistic fuzzy numbers and the information on attribute weights is provided by decision makers by means of one or some of the different preference structures, including weak ranking, strict ranking, difference ranking, multiple ranking, interval numbers, incomplete fuzzy preference relations, incomplete multiplicative preference relations, and incomplete linguistic preference relations. We transform all individual intuitionistic fuzzy decision matrices into the interval decision matrices and construct their expected decision matrices, and then aggregate all these expected decision matrices into a collective one. We establish an integrated model by unifying the collective decision matrix and all the given different structures of incomplete weight preference information, and develop an integrated model-based approach to interacting with the decision makers so as to adjust all the inconsistent incomplete fuzzy preference relations, inconsistent incomplete linguistic preference relations and inconsistent incomplete multiplicative preference relations into the ones with acceptable consistency. The developed approach can derive the attribute weights and the ranking of the alternatives directly from the integrated model, and thus it has the following prominent characteristics: (1) it does not need to construct the complete fuzzy preference relations, complete linguistic preference relations and complete multiplicative preference relations from the incomplete fuzzy preference relations, incomplete linguistic preference relations and incomplete multiplicative preference relations, respectively; (2) it does not need to unify the different structures of incomplete preferences, and thus can simplify the calculation and avoid distorting the given preference information; and (3) it can sufficiently reflect and adjust the subjective desirability of decision makers in the process of interaction. A practical example is also provided to illustrate the developed approach.     

不确定语言环境下基于ULHGA算子的群决策方法

研究属性权重和专家权重为确定的实数,属性值为不确定语言变量的多属性群决策问题.提出了一种新的数据信息集成算子不确定语言混合几何集结(ULHGA)算子,并给出不确定语言环境下基于ULWGM算子和ULHGA算子的一种群决策方法.最后进行实例分析,说明该方法的实用性和有效性.