考虑决策者风险偏好的区间直觉模糊多属性群决策方法

提出了一种考虑决策者风险偏好且属性权重信息不完全的区间直觉模糊数多属性群决策方法。同时考虑相似度和接近度,确定每一属性的决策者权重。为了考虑决策者风险偏好对决策结果的影响和避免区间直觉模糊矩阵的渐进性,引入了决策者风险偏好系数,将集结后的综合决策矩阵转换成区间数矩阵。然后,为了客观地求出属性权重信息不完全环境下属性的权重,构建了基于区间直觉模糊交叉熵的属性权重目标规划模型,该模型不仅考虑了评价值的偏差,也强调了评价值自身的可信度。最后,通过研发项目选择问题的实例分析说明了所提方法的合理性和优越性。     

A chi-square method for obtaining a priority vector from multiplicative and fuzzy preference relations

Decision makers (DMs)’ preferences on decision alternatives are often characterized by multiplicative or fuzzy preference relations. This paper proposes a chi-square method (CSM) for obtaining a priority vector from multiplicative and fuzzy preference relations. The proposed CSM can be used to obtain a priority vector from either a multiplicative preference relation (i.e. a pairwise comparison matrix) or a fuzzy preference relation or a group of multiplicative preference relations or a group of fuzzy preference relations or their mixtures. Theorems and algorithm about the CSM are developed. Three numerical examples are examined to illustrate the applications of the CSM and its advantages.     

区间直觉模糊判断矩阵群决策中的逆判问题

研究了区间直觉模糊判断矩阵的群决策问题.定义了两种区间直觉模糊集相似度公式,给出两种与决策群体意见一致性程度最高的理想区间直觉模糊判断矩阵构造优化方法.利用矩阵对不同专家判断矩阵中相同位置元素的一致性进行分析,并对不同专家的判断信息进行整体相似程度分析,最后通过算例说明了该方法的有效性和实用性.     

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.     

Modeling subjective evaluation for fuzzy group multicriteria decision making

This paper presents a new fuzzy multicriteria decision making (MCDM) approach for evaluating decision alternatives involving subjective judgements made by a group of decision makers. A pairwise comparison process is used to help individual decision makers make comparative judgements, and a linguistic rating method is used for making absolute judgements. A hierarchical weighting method is developed to assess the weights of a large number of evaluation criteria by pairwise comparisons. To reflect the inherent imprecision of subjective judgements, individual assessments are aggregated as a group assessment using triangular fuzzy numbers. To obtain a cardinal preference value for each decision alternative, a new fuzzy MCDM algorithm is developed by extending the concept of the degree of optimality to incorporate criteria weights in the distance measurement. An empirical study of aircraft selection is presented to illustrate the effectiveness of the approach.     

Fuzzy AHP中的一种加权群体决策方法

考虑在一些群体决策问题中,由于决策者个人的经验、才知、权力等因素的不同,因而拥有不同决策权重的情况,提出Fuzzy环境中层次分析法的一种加权群体决策模型,同时还给出由Fuzzy判断矩阵计算被比较元素的Fuzzy排序权重的几种方法,并给出一个应用该群体决策模型的例子。     

直觉模糊多属性的意见集中排序法

研究了属性权重信息不完全确定,属性值为直觉模糊集的多属性决策问题。首先根据直觉模糊数的得分函数和精确函数对决策矩阵中的评价值比较大小,进而按属性集中的每个属性对方案排成线性序;然后通过计算赋权模糊优先矩阵确定方案的优属度,建立规划模型确定属性的权重;再利用加权算术算子对方案集结,得到专家对方案的排序,从而得到一种新的意见集中排序的决策方法。数值实例说明该方法的有效性和实用性,可为解决直觉模糊多属性决策提供新方法     

基于OWGA算子的偏好信息集结法及其在群决策中的应用

研究了决策者的偏好信息以不同形式给出的群决策问题。首先，利用不同偏好信息之间的转换公式，将偏好次序型、效用值型、互反判断矩阵型三种偏好信息一致化为模糊互补判断矩阵型的偏好信息；其次。利用加权几何平均（OWGA）算子对一致化后的决策信息进行集结。再对方案的加权几何平均优势度进行综合集结，并以此进行方案的排序，提出了基于OWGA算子的群决策方法，该方法具有操作简便和计算量少的特点。最后。通过实例说明方法的可行性和实用性。     

基于新直觉模糊相似度的大规模群决策方法

研究了群体规模较大情况下基于直觉模糊评价信息的决策方法。提出一种新的直觉模糊相似度公式,研究了一种新的直觉模糊聚类方法,设计了一种基于新的聚类方法的核心决策者权重确定方法。实例分析说明了该方法的合理性和可行性。     

基于改进得分函数的直觉梯形模糊数群体多属性决策方法

为考虑群体多属性决策问题中决策人的风险偏好,在直觉梯形模糊数的基础上,利用连续区间有序加权平均算子对直觉梯形模糊数进行化简,使其转换成直觉模糊数。并基于此提出了一种全新的得分函数。从而得到了一种全新的群体多属性决策方法,将其应用于具体算例中,给出了该方法的具体步骤并证明了有效性。     

基于模糊聚类的不同形式偏好信息的集结方法研究

针对具有5种不同形式偏好信息的群决策问题给出了一种分析方法.当专家给出的偏好信息是模糊互补判断矩阵、区间值、正互反矩阵、序关系值以及效用值时,首先把不同形式的偏好信息转化为模糊互补判断矩阵,然后,再根据模糊互补判断矩阵得出每个专家的方案排序值,据此对专家进行模糊聚类,根据聚类结果确定专家的权重,进而进行信息合成和方案选优,并用算例进行了验证.     

A new multiple attribute group decision making method in intuitionistic fuzzy setting

The multiple attribute group decision making (MAGDM) problem with intuitionistic fuzzy information investigated in this paper is very useful for solving complicated decision problems under uncertain circumstances. Since experts have their own characteristics, they are familiar with some of the attributes, but not others, the weights of the decision makers to different attributes should be different. We derive the weights of the decision makers by aggregating the individual intuitionistic fuzzy decision matrices into a collective intuitionistic fuzzy decision matrix. The expert has a big weight if his evaluation value is close to the mean value and has a small weight if his evaluation value is far from the mean value. For the incomplete attribute weight information, we establish some optimization models to determine the attribute weights. Furthermore, we develop several algorithms for ranking alternatives under different situations, and then extend the developed models and algorithms to the MAGDM problem with interval-valued intuitionistic fuzzy information. Numerical results finally illustrate the practicality and efficiency of our new algorithms.     

A group decision making model based on goal programming with fuzzy hierarchy: an application to regional forest planning

In this paper a multi-criteria group decision making model is presented in which there is a heterogeneity among the decision makers due to their different expertise and/or their different level of political control. The relative importance of the decision makers in the group is handled in a soft manner using fuzzy relations. We suppose that each decision maker has his/her preferred solution, obtained by applying any of the techniques of distance-based multi-objective programming [compromise, goal programming (GP), goal programming with fuzzy hierarchy, etc.]. These solutions are used as aspiration levels in a group GP model in which the differences between the unwanted deviations are interpreted in terms of the degree of achievement of the relative importance amongst the group members. In this way, a group GP model with fuzzy hierarchy (Group-GPFH) is constructed. The solution for this model is proposed as a collective decision. To show the applicability of our proposal, a regional forest planning problem is addressed. The objective is to determine tree species composition in order to improve the values achieved by Pan-European indicators for sustainable forest management. This problem involves stakeholders with competing interests and different preference schemes for the aforementioned indicators. The application of our proposal to this problem allows us to be able to comfortably address all these issues. The results obtained are consistent with the preferences of each stakeholder and their hierarchy within the group.     

基于偏好和动态直觉的产学研合作伙伴选择群决策分析

针对产学研合作伙伴选择的有限理性和偏好特性,基于直觉模糊多属性决策理论和相对熵理论,构建产学研合作伙伴选择群决策模型。运用动态直觉模糊加权几何算子(DIFWG)集成合作伙伴不同时段的个人准则决策矩阵,实现对产学研合作伙伴持续性的评价;运用直觉模糊有序加权平均算子(IFOWA)集成不同决策者的决策矩阵和偏好矩阵,并利用决策者对合作伙伴的主观偏好与对合作伙伴各准则的客观评价之间差距的极小化,基于加权平均思想,求取评价准则的客观权重;然后,引入相对熵求取评价对象理想的最优权重解,依据该解对各合作伙伴进行排序并选择;最终通过实证研究说明了该方法的有效性和可行性,充分利用直觉模糊理论,实现了产学研合作伙伴的“群偏好—多时段—群决策”的全面评价。     

Extension of the VIKOR method for group decision making with interval-valued intuitionistic fuzzy information

The aim of this paper is to extend the VIKOR method for multiple attribute group decision making in interval-valued intuitionistic fuzzy environment, in which all the preference information provided by the decision-makers is presented as interval-valued intuitionistic fuzzy decision matrices where each of the elements is characterized by interval-valued intuitionistic fuzzy number, and the information about attribute weights is partially known, which is an important research field in decision science and operation research. First, we use the interval-valued intuitionistic fuzzy hybrid geometric operator to aggregate all individual interval-valued intuitionistic fuzzy decision matrices provided by the decision-makers into the collective interval-valued intuitionistic fuzzy decision matrix, and then we use the score function to calculate the score of each attribute value and construct the score matrix of the collective interval-valued intuitionistic fuzzy decision matrix. From the score matrix and the given attribute weight information, we establish an optimization model to determine the weights of attributes, and then determine the interval-valued intuitionistic positive-ideal solution and interval-valued intuitionistic negative-ideal solution. We use the different distances to calculate the particular measure of closeness of each alternative to the interval-valued intuitionistic positive-ideal solution. According to values of the particular measure, we rank the alternatives and then select the most desirable one(s). Finally, a numerical example is used to illustrate the applicability of the proposed approach.     

犹豫直觉模糊偏好关系积性一致性及其在群决策中的应用

模糊偏好关系在群决策中得到了广泛研究,针对犹豫直觉模糊集既能反映决策者偏好和非偏好的信息,又能描述其犹豫心理的特点,提出了犹豫直觉模糊偏好关系及其积性一致性的定义。为了修复不一致的犹豫直觉模糊偏好关系,先构建积性一致性指标,然后提出两种修复方法。最后,将犹豫直觉模糊偏好关系应用到群决策中,通过实例和比较说明了两种修复方法的有效性和合理性。     

不同直觉偏好信息下的多属性决策方法

研究了属性值为实数且决策者对属性的偏好信息以直觉判断矩阵或残缺直觉判断矩阵给出的模糊多属性决策问题.首先介绍了直觉判断矩阵、一致性直觉判断矩阵、残缺直觉判断矩阵、一致性残缺直觉判断矩阵等概念,而后分别考虑关于直觉判断矩阵和残缺直觉判断矩阵的多属性决策问题,接着建立了基于直觉判断矩阵和残缺直觉判断矩阵的多属性群决策模型,通过求解这些模型获得属性的权重.进而给出了不同直觉偏好信息下的多属性决策方法.最后通过一个例子说明了该方法的可行性和实用性.     

Extension of the TOPSIS method for decision making problems under interval-valued intuitionistic fuzzy environment

TOPSIS is one of the well-known methods for multiple attribute decision making (MADM). In this paper, we extend the TOPSIS method to solve multiple attribute group decision making (MAGDM) problems in interval-valued intuitionistic fuzzy environment in which all the preference information provided by the decision-makers is presented as interval-valued intuitionistic fuzzy decision matrices where each of the elements is characterized by interval-valued intuitionistic fuzzy number (IVIFNs), and the information about attribute weights is partially known. First, we use the interval-valued intuitionistic fuzzy hybrid geometric (IIFHG) operator to aggregate all individual interval-valued intuitionistic fuzzy decision matrices provided by the decision-makers into the collective interval-valued intuitionistic fuzzy decision matrix, and then we use the score function to calculate the score of each attribute value and construct the score matrix of the collective interval-valued intuitionistic fuzzy decision matrix. From the score matrix and the given attribute weight information, we establish an optimization model to determine the weights of attributes, and construct the weighted collective interval-valued intuitionistic fuzzy decision matrix, and then determine the interval-valued intuitionistic positive-ideal solution and interval-valued intuitionistic negative-ideal solution. Based on different distance definitions, we calculate the relative closeness of each alternative to the interval-valued intuitionistic positive-ideal solution and rank the alternatives according to the relative closeness to the interval-valued intuitionistic positive-ideal solution and select the most desirable one(s). Finally, an example is used to illustrate the applicability of the proposed approach.     

基于群体冲突的模糊偏好关系大群体决策方法

在大群体决策中,针对每一个决策者都有一个关于决策方案的模糊偏好关系的决策问题,提出了一种基于冲突的模糊偏好关系大群体决策方法。该方法首先考虑了复杂大群体的偏好差异,对决策者偏好进行聚类分析,形成不同的聚集,然后通过熵权法确定聚集的权重,集结成大群体模糊偏好关系,再对聚集内及聚集间进行冲突分析,通过一个迭代算子进行冲突消解,以达到一定冲突范围内的群体模糊偏好关系。最后通过一个算例说明了方法的有效性。     

Generalized hesitant fuzzy synergetic weighted distance measures and their application to multiple criteria decision-making

The purpose of this paper is to present a generalized hesitant fuzzy synergetic weighted distance (GHFSWD) measure, which is based on the generalized hesitant fuzzy weighted distance (GHFWD) measure and the generalized hesitant fuzzy ordered weighted distance (GHFOWD) measure proposed by Xu and Xia [Z. Xu, M. Xia, Distance and similarity measures for hesitant fuzzy sets, Inf. Sci. 181 (2011) 2128–2138.], and investigate its some desirable properties and special cases. The GHFSWD measure not only generalizes both the GHFWD and GHFOWD measures as well as the common hesitant fuzzy distance measures, but also reflects the importance degrees of both the given individual distances and their ordered positions. Then, based on the defined notions of positive ideal hesitant fuzzy set and negative ideal hesitant fuzzy set, we utilize the proposed GHFSWD measure to develop a method for multiple criteria decision making with hesitant fuzzy information. The method is flexible because it allows decision makers to provide preference with hesitancy and determine different decision results by choosing different decision strategies. Finally, a numerical example is provided to illustrate the feasibility and practicality of the proposed method.