首页 | 本学科首页   官方微博 | 高级检索  
相似文献
 共查询到16条相似文献,搜索用时 156 毫秒
1.
研究了具有模糊偏好信息的模糊多属性决策问题.提出一种结合主观偏好信息与客观信息的综合特征向量方法.主观偏好信息由决策方案的模糊偏好互补矩阵和属性权重的两两比较互反矩阵组成,客观信息由客观决策矩阵组成.给出了求解模糊多属性决策问题的最小二乘偏差估计方法.通过建立二次规划模型决定属性权重向量,并对方案进行排序.最后,给出了使用该方法的数值例子.  相似文献   

2.
基于方案偏好和部分权重信息的模糊多属性决策方法   总被引:4,自引:0,他引:4  
研究了只有部分权重信息且决策者对方案的偏好信息以三角模糊数互反判断矩阵形式给出的模糊多属性决策问题.首先为得到属性权重,给出一种结合主观模糊偏好信息和客观决策信息的极小化极大偏差模型;然后,运用加性加权法求出各方案的模糊综合属性值,并利用已有的三角模糊数排序公式求得决策方案的排序;最后,通过算例说明了该方法的可行性和有效性.  相似文献   

3.
群决策中两类判断矩阵的一种集成方法   总被引:18,自引:1,他引:17  
研究群决策中不同偏好信息形式的集成方法。根据多个决策者给出关于方案的两类偏好信息-Fuzzy判断矩阵和AHP判断矩阵,建立了能够集成这两类偏好信息的最优化模型,通过求解该模型可直接得到每个方案的参考排序列,并使方案的排序结果最大程度地反映每个决策者的偏好。  相似文献   

4.
对方案有偏好的Vague集互补判断矩阵决策法   总被引:2,自引:1,他引:1       下载免费PDF全文
研究指标权重信息未知且对方案有偏好的Vague集多属性决策问题。首先将决策信息和偏好信息的Vague值转化为模糊值,进一步将偏好信息转化为互补判断矩阵,从而建立目标规划模型,通过求解该模型得各指标的权重,并通过求解各方案综合属性值对方案进行排序和择优。最后给出算例。  相似文献   

5.
群决策中两类三端点区间数判断矩阵的集结方法   总被引:4,自引:0,他引:4  
研究群决策过程中三端点区间数互反判断矩阵和三端点区间数互补判断矩阵的集结. 采用OWA(Ordered weighted averaging) 方法将决策者的偏好信息集结为两个三端点区间数判断矩阵. 基于三端点区间数判断矩阵的完全一致性概念, 建立三端点区间数判断矩阵的权重求解模型. 根据群决策背景下专家群最大一致的目标, 建立求解专家群体偏好权重的模型. 在第二阶段建立群偏好权重分布范围估计模型, 最后通过可能度方法以排定各方案的最终优劣顺序.  相似文献   

6.
刘卫锋  何霞 《计算机工程》2012,38(10):141-143
针对多属性群决策问题,提出一种两阶段决策分析方法。通过分析积型模糊一致性判断矩阵和模糊判断矩阵的排序向量之间的偏差,建立并求解一个规划模型,得到专家模糊判断矩阵的排序向量。由最小化专家模糊判断矩阵的排序向量与专家群组排序向量的偏差,再次建立并求解一个规划模型,得到反映专家群组偏好的排序向量,从而得出基于模糊判断矩阵的两阶段群决策方法。通过2个算例说明了该方法的可行性与有效性。  相似文献   

7.
结合投资决策的特点给出了一种模糊多属性决策方法,其中属性偏好信息以若干个互补判断矩阵形式给出,属性值为梯形模糊数。该方法能充分挖掘判断矩阵的特征信息,给出了互补判断矩阵相似度和属性优势度的定义,从而确定专家权重和属性权重。基于梯形模糊数外接圆的圆心与原点之间所形成的矩形面积来对模糊数排序的方法对方案进行排序和择优。通过对服务行业的项目评估问题说明该方法是求解模糊多属性决策问题的一种有效的工具,并且操作简便、易于在计算机上实现。  相似文献   

8.
对属性权重信息不完全、属性值和决策者对方案的偏好信息均以直觉模糊数表示的多属性决策问题提出一种决策方法。首先根据决策者对方案的偏好信息建立多目标规划模型,求出属性权重,接着利用觉模糊加权算术平均算子求出方案的综合属性值,由直觉模糊数的得分函数和精确函数确定方案的排序,最后通过实例证明了该方法的实用性和有效性。  相似文献   

9.
三角模糊数互补判断矩阵排序的最小方差法   总被引:2,自引:0,他引:2  
研究偏好信息为三角模糊数互补判断矩阵形式给出的方案排序方法.根据三角模糊数互补判断矩阵完全一致性的概念,建立了一个基于最小方差的非线性规划模型.通过求解该模型,得到三角模糊数互补判断矩阵的权重向量,并利用三角模糊数排序公式对决策方案进行排序.最后通过算例分析表明了所提出的方法是可行而有效的.  相似文献   

10.
群决策中两类不确定偏好信息的集结方法研究   总被引:6,自引:1,他引:6  
朱建军 《控制与决策》2006,21(8):889-892
研究区间数互反判断矩阵和区间数互补判断矩阵的集结,采用UOWA算子将决策者的偏好信息集结为区间数互反判断矩阵和互补判断矩阵两种形式,结合决策者给出的允许偏差,定义群满意度隶属函数,建立求解群偏好一致程度最大化的权重模型.为解决模型存在多组最优解问题,在第2阶段建立群偏好权重分布范围估计模型,研究模型所具有的性质,最后通过区间数比较的可能度方法排定各方案的最终优劣顺序。  相似文献   

11.
Multiple attribute decision making (MADM) problems are the most encountered problems in decision making. Fuzziness is inherent in decision making process and linguistic variables are well suited to assessing an alternative on qualitative attributes using fuzzy rating. A few techniques in MADM assess the weights of attributes based on preference information on alternatives. But they are not practical any more when the set of all paired comparison judgments from decision makers (DMs) on attributes are not crisp and also we have to deal with fuzzy decision matrix. This paper investigates the generation of a possibilistic model for multidimensional analysis of preference (LINMAP). The model assesses the fuzzy weights as well as locating the ideal solution with fuzzy decision making preference on attributes and fuzzy decision matrix. All of the information is assumed as triangular fuzzy numbers (TFNs). This method is developed in group decision making environments and formulates the problem as a possibilistic programming with multiple objectives.  相似文献   

12.
In multiple attribute decision making (MADM), hesitant fuzzy sets (HFSs) are powerful tools for expressing uncertain and vague information. Recently, MADM problems with hesitant fuzzy information have attracted increasing attention, and many MADM methods have been developed. However, only a limited amount of research has considered MADM problems that simultaneously determine attribute weights and decision-maker (DM) preferences. Therefore, we propose an MADM approach for such problems under a hesitant fuzzy environment. First, we derive extended distance and correlation coefficient measures for HFSs that are more reasonable and effective when the DM preferences are considered. We then apply the extended distance measure to subjective and objective preference information to determine attribute weights, and use these to calculate the weighted correlation coefficient between the ideal choice and each alternative. Further, we determine the ranking order of all alternatives, from which it is easy to identify the best choice. Finally, we present an example that demonstrates the practicality of the proposed approach.  相似文献   

13.
Based on the additive multi-attribute value model for multiple attribute decision making (MADM) problems, the paper investigates how the set of attribute weights (or weight-set thereafter) is determined according to the preference orders of alternatives given by decision makers. The weight-set is a bounded convex polyhedron and can be written as a convex combination of the extreme points. We give the sufficient and necessary conditions for the weight-set to be not empty and present the structures of the weight-set for satisfying the preference orders of alternatives. A method is also proposed to determine the weight-set. The structure of the weight-set is used to determine the interval of weights for every attribute in the decision analysis and to judge whether there exists a positive weight in the weight-set. The research results are applied to several MADM problems such as the geometric additive multi-attribute value model and the MADM problem with cone structure  相似文献   

14.
方案偏好已知的三角模糊数型多属性决策方法   总被引:2,自引:0,他引:2       下载免费PDF全文
龚艳冰 《控制与决策》2012,27(2):281-285
研究决策者对方案偏好已知、属性值以三角模糊数形式给出且属性权重信息不能完全确知的多属性决策问题.提出了基于模糊比例值的决策方法和基于模糊偏差度的决策方法,这两种方法首先建立一个线性规划模型,通过求解该模型获得属性权重;然后,基于三角模糊数两两比较的可能度公式及三角模糊数排序公式,对决策方案进行排序和择优;最后,通过实例验证了方法的可行性和有效性.  相似文献   

15.
This article proposes an approach to handle multi-attribute decision making (MADM) problems under the interval-valued intuitionistic fuzzy environment, in which both assessments of alternatives on attributes (hereafter, referred to as attribute values) and attribute weights are provided as interval-valued intuitionistic fuzzy numbers (IVIFNs). The notion of relative closeness is extended to interval values to accommodate IVIFN decision data, and fractional programming models are developed based on the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method to determine a relative closeness interval where attribute weights are independently determined for each alternative. By employing a series of optimization models, a quadratic program is established for obtaining a unified attribute weight vector, whereby the individual IVIFN attribute values are aggregated into relative closeness intervals to the ideal solution for final ranking. An illustrative supplier selection problem is employed to demonstrate how to apply the proposed procedure.  相似文献   

16.
Interval utility values, interval fuzzy preference relations, and interval multiplicative preference relations are three common uncertain-preference formats used by decision-makers to provide their preference information in the process of decision making under fuzziness. This paper is devoted in investigating multiple-attribute group-decision-making problems where the attribute values are not precisely known but the value ranges can be obtained, and the decision-makers provide their preference information over attributes by three different uncertain-preference formats i.e., 1) interval utility values; 2) interval fuzzy preference relations; and 3) interval multiplicative preference relations. We first utilize some functions to normalize the uncertain decision matrix and then transform it into an expected decision matrix. We establish a goal-programming model to integrate the expected decision matrix and all three different uncertain-preference formats from which the attribute weights and the overall attribute values of alternatives can be obtained. Then, we use the derived overall attribute values to get the ranking of the given alternatives and to select the best one(s). The model not only can reflect both the subjective considerations of all decision-makers and the objective information but also can avoid losing and distorting the given objective and subjective decision information in the process of information integration. Furthermore, we establish some models to solve the multiple-attribute group-decision-making problems with three different preference formats: 1) utility values; 2) fuzzy preference relations; and 3) multiplicative preference relations. Finally, we illustrate the applicability and effectiveness of the developed models with two practical examples.  相似文献   

设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号