基于Vague集的TOPSIS

针对应用广泛的TOPS IS方法的特征,研究在V ague集中应用TOPS IS的方法.首先用记分函数来确定理想解和负理想解,在此基础上建立了基于V ague集的TOPS IS的决策方法,并通过实际算例验证了此方法的有效性.     

优化布点的TOPSIS法研究

针对多目标系统的优化布点问题,尝试提出用改进的TOPS IS法研究水质环境监测点位的优化,并建立改进的TOPS IS法优化模型.实例分析表明,该方法充分利用了优化矩阵的信息,优选结果符合客观实际,可用于环境监测点位的优化布点.     

基于TOPSIS法的城市间宏观经济效益差异的比较研究

提供了基于多目标决策中的TOPS IS法的城市间宏观经济效益差异的一种评价比较方法及一套指标体系.并实证分析了呼和浩特市、包头市近五年来的宏观经济效益,得到了与实际相吻合的结果.     

基于模糊模式识别的动态联盟伙伴选择模型

在基于模糊模式识别的伙伴选择方法中,将TOPS IS方法借助于一个多属性决策问题的“理想解”和“负理想解”去排序的思想应用到其伙伴选择过程中,根据贴近度的公理化定义对TOPS IS方法作以修改,构造出“相对”贴近度,以此折衷地衡量动态联盟合作伙伴的优劣。避免了传统模糊模式识别单纯利用“绝对”贴近度所造成的结果均一化、可比性差的现象,从一定程度上减少了因贴近度选用的不同而造成的结果差异。从某种意义上来说,该种方法可以作为TOPS IS方法的拓展,是本文具有新意之处。     

基于“奖优罚劣”的区间数多指标决策的TOPSIS方法

针对区间数多指标系统的决策特点,对指标数据初始化处理时,利用"奖优罚劣"原则,提出了一种易于计算且实用的[-1,1]线性变换算子,然后定义正、负理想方案,给出了区间数多指标决策问题的TOPS IS方法.该模型为区间数多指标决策提供了一种科学、实用的方法,并利用现有的实例来证实此方法的科学性与可行性.     

BBTOPSIS方法在Vague集中的应用

TOPS IS法得到了广泛的应用,由于V ague集对处理不确定信息、不精确信息的优势,V ague集在决策领域应用越来越多,BBTOPS IS在处理非基数信息具有优势,因此在V ague集的环境下研究BBTOPS IS有其必要性,首先定义了接受指数,然后讨论了V ague集的BBTOPS IS,并给出具体的算例.     

基于直觉模糊集的突发事件应急预案评估

提出了一种基于直觉模糊集理论的突发事件应急预案评价的新方法.首先,定义了直觉模糊集和两个直觉模糊集之间的距离;然后,建立了应急预案评价指标体系,给出了直觉模糊正理想点和负理想点的概念,提出了基于直觉模糊集和TOPS IS法的应急预案评估方法.最后通过实例说明了该方法的有效性.     

IS/IT项目选择决策的灰色逼近理想解排序法

IS/IT项目选择决策是一个多属性决策问题.针对传统逼近理想解排序法(TOPSIS)在确定属性权重系数上的缺陷,并考虑到在实际IS/IT项目选择决策过程中部分决策信息的不足,提出了基于灰色TOPSIS改进算法.算法运用区间灰数表达指标权重和指标评价值,定义备择项目与正、负理想解的灰色关联度,依此计算各备则项目的贴近度并实现最终排序.仿真实例验证了该方法的合理和有效性.     

基于VAGUE集多准则决策的模糊TOPSIS方法

给出了V AGUE集之间距离的新定义,在此基础上,提出一个基于V AGUE集多准则决策的模糊TOP-S IS方法,它为决策系统提供了一个有用的工具,并通过例子阐明本文方法的有效性.     

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.     

对使用主成分进行绩效指标值排名的讨论

对《基于数据的Goodgrant基金最优投资策略》一文使用主成分分析进行综合评价,对候选学校绩效指标值排名进行了探讨。首先,综合前人研究与本题实际,指出使用主成分分析进行综合评价存在的争议与不足;然后,分别建立TOPSIS模型和综合评价模型对候选学校的绩效指标值进行排名,并对不同方法得到的结果进行对比。结果表明,TOPSIS模型和综合评价模型得到的排名具有高度一致性,前50名重合率达98%,而与主成分分析综合评价的重合率仅有6%,说明使用TOPSIS等传统评价模型对候选学校绩效指标值进行排名更合适。     

基于用户选择的产品属性权重确定及对TOPSIS法的改进

传统TOPSIS评价法具有权重主观确定、更新方案产生逆序等不足,本文尝试基于用户参与选择的客观行为数据和产品属性数据以改进属性权重的确定;通过极差变换以改进初始矩阵的标准化及最优点、最差点的确定,以消除TOPSIS法评价结果可能出现的逆序问题。最后,基于改进后的TOPSIS法应用于不同品牌手机综合评价的实例验证,结果显示改进后的TOPSIS评价法更具可行性及有效性。     

线性无量纲化方法对比及反向指标正向化方法

线性无量纲化方法的对比及反向指标的正向化方法都是综合评价的重要研究内容。从指标差异信息的角度,以TOPSIS、基于街区距离的TOPSIS和线性加权综合法为例,基于理论推导和实证分析对比了常用的线性无量纲化方法,并提出了两种反向指标正向化方法。研究发现,对于线性加权综合法和TOPSIS,不同线性无量纲化方法下同一指标归一化极差的不同是导致排序结果存在差异的关键因素;本文提出的反向指标正向化方法,不仅可以保证正向化前后TOPSIS、基于街区距离的TOPSIS的评价值不变,也可以实现反向指标正向化后线性加权综合法与基于街区距离的TOPSIS在排序目的上的等效性。最后,本文提出了线性无量纲化方法和反向指标正向化方法的应用建议。     

The interval-valued fuzzy TOPSIS method and experimental analysis

The purpose of this paper is to extend the TOPSIS method based on interval-valued fuzzy sets in decision analysis. Hwang and Yoon developed the technique for order preference by similarity to ideal solution (TOPSIS) in 1981. TOPSIS has been widely used to rank the preference order of alternatives and determine the optimal choice. Considering the fact that it is difficult to precisely attach the numerical measures to the relative importance of the attributes and to the impacts of the alternatives on these attributes in some cases, therefore, the TOPSIS method has been extended for interval-valued fuzzy data in this paper. In addition, a comprehensive experimental analysis to observe the interval-valued fuzzy TOPSIS results yielded by different distance measures is presented. A comparative analysis of interval-valued fuzzy TOPSIS rankings from each distance measure is illustrated with discussions on consistency rates, contradiction rates, and average Spearman correlation coefficients. Finally, a second-order regression model is provided to highlight the effects of the number of alternatives, the number of attributes, and distance measures on average Spearmen correlation coefficients.     

A method for group decision-making based on determining weights of decision makers using TOPSIS

In general, weights of decision makers (DMs) play a very important role in multiple attribute group decision-making (MAGDM), how to measure the weights of DMs is an interesting research topic. This paper presents a new approach for determining weights of DMs in group decision environment based on an extended TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution) method. We define the positive ideal solution as the average of group decision. The negative ideal solution includes two parts: left and right negative ideal solution, which are the minimum and maximum matrixes of group decision, respectively. We give an example to illustrate the developed approach. Finally, the advantages and disadvantages of this study are also compared.     

Extension of VIKOR method for decision making problem based on hesitant fuzzy set

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.     

模糊多属性决策的直觉模糊集方法

基于直觉模糊集理论,提出了一种新的TOPSIS方法来研究模糊多属性决策问题。首先,根据直觉模糊集的几何意义,定义了两个直觉模糊集之间的距离,且每个备选方案的评价值用直觉模糊值表示;然后,根据TOPSIS原理,通过计算备选方案到直觉模糊正理想解和负理想解的距离,来确定备选方案的综合评价指数,以此判断方案的优劣次序。最后,通过一个具体实例说明该方法的有效性和具体应用过程。     

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.     

两两比较的TOPSIS法

TOPSIS法是一种常用的多目标决策方法,它以正负理想解作为统一的参照基准来比较方案的优劣.并不适用于常见的两两比较的决策行为.运用最小二乘法解决判别一致性问题,从而建立了两两比较的TOPSIS法,并进行了实例分析.     

一种基于不同粒度且属性权重未知的群决策方法

针对具有不同粒度语言评价矩阵和属性未知的群决策问题,给出了一种基于二元语义和TOPSIS算法的群决策方法。在该方法中,首先给出了不同粒度语言评价矩阵一致化为由基本语言评价集表示的二元语义信息的方法;然后引入TOPSIS的方法,结合二元语义形式计算规则,确定未知的属性客观权重,利用二元语义集结算子,得到单个决策者对方案的评价值;再通过T-OWA算子对各决策者给出的评价信息进行集结和方案选优;最后给出了一个算例。