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基于Hausdorff距离的模糊数互补判断矩阵排序研究 总被引:4,自引:1,他引:3
基于Bonissone近似计算、Hausdorff距离和模糊折衷型决策方法,给出带有梯形模糊数互补判断矩阵的一种排序方法。同时给出精确值、三角模糊数的互补判断矩阵转化为梯形模糊数互补判断矩阵的方法,因此本文方法同样适合于处理精确值、三角模糊数的互补判断矩阵的排序问题。最后用算例说明了计算过程。 相似文献
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基于模糊熵的直觉模糊多属性群决策方法 总被引:1,自引:0,他引:1
针对专家权重未知、专家判断信息以直觉模糊集给出的多属性群决策问题,提出了一种新的决策方法.通过定义直觉模糊集的模糊熵计算专家判断信息的模糊程度,进而确定每位专家的权重.然后定义直觉模糊集的模糊交叉熵确定备选方案距理想方案和负理想方案的距离,再根据加权算术算子集结专家的判断信息,得到方案的排序.最后,通过一个实例分析验证了方法的有效性. 相似文献
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D. -F. Li 《Fuzzy Optimization and Decision Making》2007,6(3):237-254
The aim of this paper is to develop a new fuzzy closeness (FC) methodology for multi-attribute decision making (MADM) in fuzzy
environments, which is an important research field in decision science and operations research. The TOPSIS method based on
an aggregating function representing “closeness to the ideal solution” is one of the well-known MADM methods. However, while
the highest ranked alternative by the TOPSIS method is the best in terms of its ranking index, this does not mean that it
is always the closest to the ideal solution. Furthermore, the TOPSIS method presumes crisp data while fuzziness is inherent
in decision data and decision making processes, so that fuzzy ratings using linguistic variables are better suited for assessing
decision alternatives. In this paper, a new FC method for MADM under fuzzy environments is developed by introducing a multi-attribute
ranking index based on the particular measure of closeness to the ideal solution, which is developed from the fuzzy weighted
Minkowski distance used as an aggregating function in a compromise programming method. The FC method of compromise ranking
determines a compromise solution, providing a maximum “group utility” for the “majority” and a minimum individual regret for
the “opponent”. A real example of a personnel selection problem is examined to demonstrate the implementation process of the
method proposed in this paper. 相似文献
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In this paper, a new method for comparing fuzzy numbers based on a fuzzy probabilistic preference relation is introduced. The ranking order of fuzzy numbers with the weighted confidence level is derived from the pairwise comparison matrix based on 0.5-transitivity of the fuzzy probabilistic preference relation. The main difference between the proposed method and existing ones is that the comparison result between two fuzzy numbers is expressed as a fuzzy set instead of a crisp one. As such, the ranking order of n fuzzy numbers provides more information on the uncertainty level of the comparison. Illustrated by comparative examples, the proposed method overcomes certain unreasonable (due to the violation of the inequality properties) and indiscriminative problems exhibited by some existing methods. More importantly, the proposed method is able to provide decision makers with the probability of making errors when a crisp ranking order is obtained. The proposed method is also able to provide a probability-based explanation for conflicts among the comparison results provided by some existing methods using a proper ranking order, which ensures that ties of alternatives can be broken. 相似文献
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基于Hausdauff度量的模糊TOPSIS方法研究 总被引:4,自引:0,他引:4
针对模糊多属性决策中的模糊 TOPSIS方法 ,提出了一种基于 Hausdauff度量的模糊 TOPSIS方法 .首先由模糊极大集与模糊极小集确定模糊多属性决策问题的理想解与负理想解 ,进而由 Hausdauff度量获得不同备选方案到理想解与负理想解的距离及其贴近度 ,根据贴近度指标对方案进行排序 ,为决策者提供决策支持 .最后以 L-R梯形模糊数为例进行了实例研究 . 相似文献
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近年来,多属性决策问题一直是广大学者研究的重点,然而基于ELECTRE方法的区间犹豫模糊多属性决策问题的研究并不多见。因此,结合区间犹豫模糊集的信息表达优势和ELECTRE方法的思想,提出了一种区间犹豫模糊ELECTRE(IVHF ELECTRE)多属性决策新方法。首先构造了区间犹豫模糊决策矩阵,引入得分函数和可能度的概念,构造属性优势集和属性劣势集。然后通过设定阈值得到综合优先判定矩阵,从而得到各方案间的优先顺序。为了进一步得到各方案的整体排序,引入TOPSIS方法,通过计算各方案与正负理想点的相对距离来构造综合优先矩阵,从而得到各方案的总体排序。最后通过具体实例验证了该方法的可行性和合理性。 相似文献
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In this paper, we proposed a defuzzification using minimizer of the distance between the two fuzzy numbers. Then, we obtain the nearest point with respect to a fuzzy numbers and by considering the nearest point, we can present a ranking method for the fuzzy numbers. Also we give two new properties for ordering. Theorems and remarks are proposed for existence and uniqueness of the nearest point. The method is illustrated by numerical examples and compared with other methods. 相似文献
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The multiple criteria decision making (MCDM) methods VIKOR and TOPSIS are all based on an aggregating function representing “closeness to the ideal”, which originated in the compromise programming method. The VIKOR method of compromise ranking determines a compromise solution, providing a maximum “group utility” for the “majority” and a minimum of an “individual regret” for the “opponent”, which is an effective tool in multi-criteria decision making, particularly in a situation where the decision maker is not able, or does not know to express his/her preference at the beginning of system design. The TOPSIS method determines a solution with the shortest distance to the ideal solution and the greatest distance from the negative-ideal solution, but it does not consider the relative importance of these distances. And, the hesitant fuzzy set is a very useful tool to deal with uncertainty, which can be accurately and perfectly described in terms of the opinions of decision makers. In this paper, we develop the E-VIKOR method and TOPSIS method to solve the MCDM problems with hesitant fuzzy set information. Firstly, the hesitant fuzzy set information and corresponding concepts are described, and the basic essential of the VIKOR method is introduced. Then, the problem on multiple attribute decision marking is described, and the principles and steps of the proposed E-VIKOR method and TOPSIS method are presented. Finally, a numerical example illustrates an application of the E-VIKOR method, and the result by the TOPSIS method is compared. 相似文献
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《佛山科学技术学院》2014,6(4):489-504
Based on the feature of interval-valued intuitionistic fuzzy multi-attribute decision-making, in this thesis, a mentality parameter is used to reflect the decision makers’ risk attitude in determining of both a membership degree and a non-membership degree. Besides, with the mentality parameter, a new score function and accuracy function are proposed, which integrate the membership degree, the non-membership degree and the hesitancy degree into one index. Furthermore, to compare two interval-valued intuitionistic fuzzy numbers, a new ranking method is generated with the score function and accuracy function. Finally, a multi-attribute decision method under interval-valued intuitionistic fuzzy environment is developed in a linear weighted average operator. And promising numerical results show that this method is available. 相似文献
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为解决复杂条件下的模糊多属性群体决策问题,利用模糊距离的概念,提出了模糊距离折中比值法(FCRM)。在FCRM中,属性权重和定性属性评估值由语言变量和三角模糊数描述,并用模糊距离度量模糊数之间的距离。FCRM的决策原则是所选择的最优解在尽可能地贴近正理想解的同时尽可能地远离负理想解,同时充分考虑多个决策者的主观态度。文中详细阐述了FCRM的决策过程,通过实例将其应用于军事航线优选问题并与其他相关方法进行了比较分析,证实了该方法的有效性。 相似文献
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基于泛性模糊数的VIKOR方法研究 总被引:2,自引:0,他引:2
建立了一种泛性模糊数可比较的度量,对决策信息通常为泛性模糊数的决策问题进行加工和扩展,提出了基于泛性模糊数不确定信息的VIKOR决策方法,实现了属性为泛性模糊数的多属性群决策及信息融合的目的. 相似文献
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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. 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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针对权值是区间数且指标值以三角模糊数形式给出的模糊多属性决策问题,基于格序决策的理论,提出一种新的格序决策办法.方法通过计算梯形模糊数的中心将TOPSIS方法推广到了模糊数的领域,进而给出一种新的方案排序方法. 相似文献
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In this paper we focus on preference and decision data gathered during a computer-supported information market game in which 35 students participated during seven consecutive trading sessions. The participants’ individual preferences on the market shares are collected to calculate a collective preference ranking using the Borda social choice method. Comparing this preference ranking to the shares’ actual market ranking resulting from the participants’ trading, we find a statistically significant difference between both rankings. As the preferences established by market behavior cannot be adequately explained through a social choice rule, we propose an alternative explanation based on the herd behavior phenomenon where traders imitate the most successful trader in the market. Using a decision analysis technique based on fuzzy relations, we study the participants’ rankings of the best share in the market during 7 weeks and compare the most successful trader to the other traders. The results from our analysis show that a substantial number of traders is indeed following the market leader. 相似文献