多指标综合评价方法的改进

由决策方案在理想方案上的投影及其到理想方案的距离还不能完全确定决策方案的优劣,应该综合考虑决策方案与理想方案的相似系数及其到理想方案的距离.     

联系向量距离与灰色关联度结合的理想解法

针对传统理想解法采用欧氏距离计算的缺陷,提出了联系向量距离与灰色关联度结合的理想解法.首先将理想点与负理想点均视为确定不确定系统中相互对立的集合,计算各待决策方案与理想解和负理想解的联系向量距离;然后采用灰色关联度方法计算各待决策方案与理想解和负理想解之间的相似程度;其次通过定义新的综合距离和综合距离贴近度构建联系向量距离与灰色关联度结合的理想解法.该方法在有效地解决传统理想解法缺陷的基础上,还包含了待决策方案在趋势上的差异性,同时综合距离在权重分配上充分考虑了决策者的偏好或者专家意见,使评价结果更加有效.最后采用算例验证了该方法的可行性和有效性.     

基于不同距离的TOPSIS油田开发方案组合优选

为了衡量TOPSIS方法中不同距离函数对油田开发最优决策方案的影响,综合考虑技术、经济和效益等指标,分别采用曼哈顿距离、欧式距离、切比雪夫距离和垂面距离来探究油田开发方案排序之间的差异性.同时,为解决这种差异,通过计算4种距离函数下方案排序对理想开发方案的隶属度,并采用加权组合决策的方法对4种优选结果进行综合决策.     

基于Hausdauff度量的模糊TOPSIS方法研究

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 .     

三元区间数型多属性决策正交投影模型及其在边坡支护方案评价中的应用

针对用TOPSIS法进行三元区间数型多属性决策的不足,用各方案到"理想方案"的"垂面"距离代替TOPSIS法中的欧氏距离,提出一种三元区间数型多属性决策正交投影模型.模型将"理想方案"平移至坐标原点后,转换为0向量,只用平移后的"负理想方案"计算各方案到"理想方案"的"垂面"距离,根据距离最小原则排序得到最终决策结果.通过一个边坡支护方案评价的例子进行了计算分析,并与用其他方法得到的结果进行了对比,说明了模型的有效性.     

一种基于前景理论和改进TOPSIS的模糊随机多准则决策方法及其应用

针对准则值为区间灰数直觉模糊数、准则权系数部分已知以及自然状态出现概率为灰数的多准则决策问题,提出一种结合前景理论和改进TOPSIS的决策方法。该方法首先定义了灰色直觉模糊数的前景价值函数和概率权重函数,并利用前景理论构建出前景决策矩阵;接着从两个方面对传统TOPSIS决策方法进行改进:(1)过定义方案间综合差异的概念,采用离差最大化思想,建立平均综合差异最大化规划模型,给出了一种兼顾主客观权重信息确定准则权系数的新方法;(2)用灰关联替换备选方案与正负理想方案的距离,据此刻画了各方案与正负理想方案的贴近度。进而利用改进TOPSIS决策方法中的综合贴近度对方案进行了排序。最后通过实例验证了该方法的有效性。     

基于直觉梯形模糊偏好相似度的多属性决策方法

在解决模糊多属性决策问题中,相似度是一种有效的方法.针对已有的相似度的不足,构造了一种新的两个矢量之间的相似度,证明其满足相似度的性质,并把它应用解决直觉梯形模糊偏好多属性决策问题.方法用语言值的直觉梯形模糊数来表示决策方案的信息,通过计算每个决策方案的期望矢量,与正理想方案和负理想方案的期望矢量的相对相似度,并由相对相似度大小来排列决策方案.最后用一案例来讨论方法的可行性,数值结果表明方法计算简单,实用性强.     

基于vague集投影及距离的模糊多指标决策

提出了两个vague值投影和距离的概念,并在此基础上给出了考虑指标权重影响的基于vague集投影和距离的多指标模糊决策方法.在这个方法中,利用候选方案在理想方案上的投影和距离来求出最佳方案.此方法与文献现有的应用vague集相似度度量进行决策的方法相比较,新方法更加合理且弥补了现有方法的不足之处.最后对实例进行分析计算,算例验证了该方法的有效性.     

基于TOPSIS的模糊群体多属性决策方法

针对模糊群体多属性决策问题,给出一种基于理想点法(TOPSIS)的多属性决策方法.方法先用三角模糊数的形式表示专家评价值的模糊性和不确定性,而后考虑了专家在不同评价属性中的重要程度和意见的相似度,并将专家意见进行集结得到专家群体关于方案集的模糊决策矩阵,最后定义了三角模糊数形式的正负理想方案,通过计算各方案与正负理想方案的距离以及各方案与理想点的相对接近度,最终确定最优方案.通过实例分析说明了该方法的可行性和有效性.     

基于区间值直觉模糊集的TOPSIS多属性决策

基于区间值直觉模糊集,提出了一种新的TOPSIS模糊多属性决策方法。首先介绍区间直觉模糊集的概念,定义了两个区间值直觉模糊集之间的距离;然后根据TOPSIS方法的原理,定义了两个区间值直觉模糊集的接近系数,通过计算备选方案到区间值直觉模糊正理想解和负理想解的距离来确定接近系数,从而判断备选方案的优劣次序。最后,通过一个具体实例来说明这种方法的有效性和具体计算过程。     

模糊多属性格序决策的冗余指标的消除策略

在模糊多属性决策中,属性权重的确定对于整个评价工作有十分重要的意义.如果评价属性数量过多,指标间的相关性将影响评价的科学性和公平性.本文建立了评价值为梯形模糊数的"相似"概念和模糊相似评价模型,并基于格序决策的理论,得到了一种新的模糊格序决策方法.结合传统的TOPSIS方法,通过计算将各方案的属性值的中心进行加权后与正负理想中心的贴近度的大小,实现备选方案的格序化排序.实例分析的结果表明:方法合理、易行.     

Extension of the LINMAP for multiattribute decision making under Atanassov’s intuitionistic fuzzy environment

The aim of this article is further extending the linear programming techniques for multidimensional analysis of preference (LINMAP) to develop a new methodology for solving multiattribute decision making (MADM) problems under Atanassov’s intuitionistic fuzzy (IF) environments. The LINMAP only can deal with MADM problems in crisp environments. However, fuzziness is inherent in decision data and decision making processes. In this methodology, Atanassov’s IF sets are used to describe fuzziness in decision information and decision making processes by means of an Atanassov’s IF decision matrix. A Euclidean distance is proposed to measure the difference between Atanassov’s IF sets. Consistency and inconsistency indices are defined on the basis of preferences between alternatives given by the decision maker. Each alternative is assessed on the basis of its distance to an Atanassov’s IF positive ideal solution (IFPIS) which is unknown a prior. The Atanassov’s IFPIS and the weights of attributes are then estimated using a new linear programming model based upon the consistency and inconsistency indices defined. Finally, the distance of each alternative to the Atanassov’s IFPIS can be calculated to determine the ranking order of all alternatives. A numerical example is examined to demonstrate the implementation process of this methodology. Also it has been proved that the methodology proposed in this article can deal with MADM problems under not only Atanassov’s IF environments but also both fuzzy and crisp environments.     

一种基于相关系数矩阵的TOPSIS决策方法

在多属性决策分析中,传统的TOPSIS法是基于欧氏距离来计算各方案到正负理想点的距离,但欧氏距离没有考虑各属性之间的相关性;从这一角度出发,将相关系数矩阵与欧式距离结合,从而弥补了欧氏距离的不足,最后进行了实例分析.     

A fuzzy closeness approach to fuzzy multi-attribute decision making

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.     

Correlation coefficient of dual hesitant fuzzy sets and its application to multiple attribute decision making

A dual hesitant fuzzy set (DHFS) consists of two parts, that is, the membership hesitancy function and the nonmembership hesitancy function, supporting a more exemplary and flexible access to assign values for each element in the domain, and can handle two kinds of hesitancy in this situation. It can be considered as a powerful tool to express uncertain information in the process of group decision making. Therefore, we propose a correlation coefficient between DHFSs as a new extension of existing correlation coefficients for hesitant fuzzy sets and intuitionistic fuzzy sets and apply it to multiple attribute decision making under dual hesitant fuzzy environments. Through the weighted correlation coefficient between each alternative and the ideal alternative, the ranking order of all alternatives can be determined and the best alternative can be easily identified as well. Finally, a practical example of investment alternatives is given to demonstrate the practicality and effectiveness of the developed approach.     

Weight determination for consistently ranking alternatives in multiple criteria decision analysis

One of the most difficult tasks in multiple criteria decision analysis (MCDA) is determining the weights of individual criteria so that all alternatives can be compared based on the aggregate performance of all criteria. This problem can be transformed into the compromise programming of seeking alternatives with a shorter distance to the ideal or a longer distance to the anti-ideal despite the rankings based on the two distance measures possibly not being the same. In order to obtain consistent rankings, this paper proposes a measure of relative distance, which involves the calculation of the relative position of an alternative between the anti-ideal and the ideal for ranking. In this case, minimizing the distance to the ideal is equivalent to maximizing the distance to the anti-ideal, so the rankings obtained from the two criteria are the same. An example is used to discuss the advantages and disadvantages of the proposed method, and the results are compared with those obtained from the TOPSIS method.     

基于距离测度及支持度的混合评价信息供应商选择决策

新零售模式的推进迫使企业不断加强供应链节点上的供应商选择优化,是企业新时代发展面临的新课题。针对供应商选择优化提出一种基于距离测度及支持度的群决策方法,根据不同类型属性评价信息的距离测度定义,得到不同决策者之间关于单一属性指标的相互支持度,从而确定单个供应商各属性的群体综合评价值,并采用灰关联法对备选供应商进行排序择优。     

基于理想灰关联距离的课程评估方法

综合灰色系统理论、理想解法和欧氏距离,提出了一种新的基于理想关联距离度的课程评估方法,给出了建立评估模型的基本步骤.定量处理的指标,经过理想化、标准化后,定义关联数,由此计算关联距离度.通过灰色关联距离度,建立了一种接近最优方案远离最差方案的评估模型.并通过学院近期的课程评估实例分析,验证了该方法的准确性和可行性.