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对属性权重信息完全未知且属性值为模糊数直觉模糊数的多属性决策问题进行了研究,定义了模糊数直觉模糊数的得分函数,进而提出了一种基于线性规划模型的模糊数直觉模糊多属性决策方法.最后通过实例对该决策途径的详细过程及有效性进行了说明. 相似文献
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吴维煊 《数学的实践与认识》2013,43(1)
属性权重和属性值都是梯形模糊数的多属性决策问题是一种带有不确定性的决策,需要作不确定性分析后才能得出结论.为此把梯形模糊数表示的属性值和属性权重先用其特征函数"均值+偏差"联系数(特征联系数)表示,再利用联系数运算法则作"加权求和"运算,不仅获得与其它决策方法相同的结果,而且借助联系数中i的不同取值考察决策对象的排序变化,方法简便易行,且具有较强适用性. 相似文献
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《数学的实践与认识》2013,(16)
公路工程评标定标问题的实质是多属性决策问题,专家对参评标书给出了各指标的区间直觉模糊属性值和属性权重的部分信息后,先定义了区间直觉模糊数的得分函数及标准得分差,进而提出了一种基于线性规划模型的区间直觉模糊多属性决策方法,最后通过实例对该决策途径的详细过程及有效性进行了说明. 相似文献
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针对属性值以模糊语言形式给出,属性权重完全未知但给出方案偏好信息的模糊多属性决策问题给出决策方法.该方法是将模糊语言给出的属性评估及方案偏好转换为梯形模糊数,通过建立一个不确定二次规划模型来确定属性的权重,基于加权平均法则来对规范化的模糊属性值及权重进行集结,利用模糊数大小比较的期望值方法来对方案进行排序和择优.最后给出一个应用实例. 相似文献
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多属性决策过程中,每个方案的属性值有时体现为由直觉模糊数所刻划的语言变量,通过定义直觉模糊数间的距离,首先提出了基于直觉模糊数的TOPSIS方法;其次,考虑到在实际问题中往往会遇到不完备直觉模糊信息的事实,提出一种将不完备直觉模糊数完备化的方法,并建立了基于不完备直觉模糊信息的TOPSIS方法,同时通过实例说明该方法的有效性以及在多属性决策中的应用. 相似文献
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基于新精确函数的区间直觉模糊多属性决策方法 总被引:1,自引:0,他引:1
基于区间直觉模糊数隶属度和非隶属度构成的二维几何图形特征给出区间直觉模糊数精确函数的新定义,并将其作为区间直觉模糊数的排序指标,区间直觉模糊数的精确函数值越大,则区间直觉模糊数就越大,进而提出一种权重信息不完全确定的区间直觉模糊多属性决策方法.通过算例分析说明所提出排序指标的有效性和决策方法的可行性. 相似文献
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研究了决策者对方案的主观偏好值以及属性值均为直觉模糊数的且属性间存在关联的多属性决策问题.利用Choquet模糊积分作为集结算子,构建了基于属性关联的M OD和SOD模型.通过求解模型获得属性的权重,进而给出了一种新的直觉模糊多属性决策方法.最后通过一个算例说明了该决策方法的有效性和可行性. 相似文献
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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. 相似文献
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梯形模糊数直觉模糊Bonferroni平均算子及其应用 总被引:1,自引:0,他引:1
本文研究决策信息为梯形模糊数直觉模糊数(TFNIFN)且属性间存在相互关联的多属性群决策(MAGDM)问题,提出一种基于梯形模糊数直觉模糊加权Bonferroni平均(TFNIFWBM)算子的决策方法.首先,介绍了TFNIFN的概念和运算法则,基于这些运算法则和Bonferroni平均(Bonferroni mean,BM)算子,定义了梯形模糊数直觉模糊Bonferroni平均算子和TFNIFWBM算子.然后,研究了这些算子的一些性质,建立基于TFNIFWBM算子的多属性群决策模型,结合排序方法进行决策.最后,将该方法应用在MAGDM中,算例结果表明了该方法的有效性与可行性. 相似文献
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Multi-person multi-attribute decision making models under intuitionistic fuzzy environment 总被引:1,自引:0,他引:1
Zeshui Xu 《Fuzzy Optimization and Decision Making》2007,6(3):221-236
Intuitionistic fuzzy numbers, each of which is characterized by the degree of membership and the degree of non-membership
of an element, are a very useful means to depict the decision information in the process of decision making. In this article,
we investigate the group decision making problems in which all the information provided by the decision makers is expressed
as intuitionistic fuzzy decision matrices where each of the elements is characterized by intuitionistic fuzzy number, and
the information about attribute weights is partially known, which may be constructed by various forms. We first use the intuitionistic
fuzzy hybrid geometric (IFHG) operator to aggregate all individual intuitionistic fuzzy decision matrices provided by the
decision makers into the collective intuitionistic fuzzy decision matrix, then we utilize the score function to calculate
the score of each attribute value and construct the score matrix of the collective intuitionistic fuzzy decision matrix. Based
on the score matrix and the given attribute weight information, we establish some optimization models to determine the weights
of attributes. Furthermore, we utilize the obtained attribute weights and the intuitionistic fuzzy weighted geometric (IFWG)
operator to fuse the intuitionistic fuzzy information in the collective intuitionistic fuzzy decision matrix to get the overall
intuitionistic fuzzy values of alternatives by which the ranking of all the given alternatives can be found. Finally, we give
an illustrative example. 相似文献
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《Applied Mathematical Modelling》2014,38(11-12):2969-2982
This paper presents a multiple attribute group decision making model based on aggregating crisp values into intuitionistic fuzzy numbers. First, each alternative is evaluated with respect to their attributes, whose values are provided by decision maker as crisp numbers. Second, to make a reasonable normalization of attribute values in the group decision making environment, a maximum grade and a minimum grade are added to the attribute values. These normalized attribute values are then aggregated (per attribute) into an induced intuitionistic fuzzy number. Each alternative is then evaluated according to the induced intuitionistic fuzzy number. To show the major technical advances in this paper, comparisons with other methods are also made. Finally, an experimental analysis for supplier selection is given to illustrate the reasonableness and efficiency of the introduced method. 相似文献
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研究了属性权重完全未知的区间直觉梯形模糊数的多属性决策问题,结合TOPSIS方法定义了相对贴近度及总贴近度公式.首先由区间直觉梯形模糊数的Hamming距离给出了每个方案的属性与正负理想解的距离,基于此,给出了相对贴近度矩阵,根据所有决策方案的综合贴近度最小化建立多目标规划模型,从而确定属性的权重值,然后根据区间直觉梯形模糊数的加权算数平均算子求出各决策方案的总贴近度,根据总贴近度的大小对方案进行排序;最后,通过实例分析说明该方法的可行性和有效性. 相似文献
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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. 相似文献
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基于联系数的属性权重未知的区间数多属性决策研究 总被引:1,自引:0,他引:1
针对一类属性权重未知且属性值用区间数表示的不确定多属性决策问题,把区间数表示的属性值转换为二元联系数,并改写成三角函数,按决策方案属性值方差确定属性权重,根据各方案属性加权综合值确定方案初排序,再通过不确定性分析方法做出最终排序.实例应用表明上述方法简明实用有效,而且能方便地开展方案排序的不确定分析. 相似文献
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Zeshui Xu 《Fuzzy Optimization and Decision Making》2010,9(3):333-357
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. 相似文献
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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. 相似文献