一种基于双基点的系统评价指标赋权法

指标权系数的赋值决定了系统综合评价的合理性和正确性 .为了提高指标权系数赋值的准确性 ,给出了一种利用双基点计算指标权重的方法 .通过引入一个偏好系数以表征决策者对双基点的信赖程度 ,以带偏好系数的待评方案距双基点的加权距离的平方和为优化目标 ,建立了新的数学模型 ,并以优化理论为依据 ,给出了求解模型的精确解 .应用实例表明了该方法的有效性 .     

模糊多目标群决策方法在军事辅助决策中的运用

决策者用语言变量评价目标权系数和定性目标,对定量目标进行无量纲化处理,并统一表示成不规则四边形模糊数的形式.经集结确定各决策对各方案的模糊评价指标,然后用乐观系数准则确定各决策对各方案的排序指标.最后用平均值法集结所有决策者对各方案的排序指标,并确定出群体对各方案的排序.     

vague集的多指标决策问题的优劣点法

就vague集的多指标决策问题,引入了方案集的最优点和最劣点的概念,由此给出了vague集多指标决策问题的优劣点法.首先计算各方案的各单指标对理想vague值[1,1]的投影和距离,从而建立评价矩阵,然后根据评价矩阵的各单一指标对应数值的大小得到方案的最优点和最劣点,通过计算各方案到最优点和最劣点的距离以及接近度对方案进行排序,得出最优方案.该方法简单实用,所需信息少,实例验证了该方法的有效性.     

考虑缺失属性的多属性群决策方法

针对决策者在不完全属性集下进行评价的多属性群决策问题,考虑对缺失属性上的评价进行估计,提出了一种基于决策者相似度的多属性群决策方法.给定决策者在不完全属性集上的评价矩阵,构造同时考虑交叉属性个数和评价值的决策者相似度,基于此相似度对缺失属性评价进行估计,进而利用加权平均法对完整的评价矩阵进行集结得到每个决策者的方案排序.在此基础上,以决策者方案排序与总体方案排序差异最小为目标,构建优化模型以产生最优的总体方案排序.运用所提方法解决江苏省常州市一家高速列车制造企业的战略项目评价问题,验证了该方法的有效性与应用性.     

多指标决策广义双基点法

在多指标决策双基点法中,指出了已有方法的不足,基于靠近理想点和远离负理想点这两个基准,本文定义了一种新的相对贴近度的计算公式,由此给出了一种广义双基点法.     

基于累积前景理论的考虑群体参考点的多指标风险决策方法

针对考虑多个决策者给出不同的指标期望的多指标风险决策问题,提出一种基于累积前景理论的决策分析方法。在本文中,将决策者给出的指标期望视为参照点,通过构建基于参照点的价值矩阵和权重矩阵,进而构建前景决策矩阵,并基于前景决策矩阵来计算每个方案的综合前景值,然后依据综合前景值的大小对所有方案进行排序。最后,通过一个算例说明了该方法的可行性和有效性。     

构建不确定语言型多属性决策的投影模型

研究不确定语言型多属性决策评价结果与决策者对方案的偏好信息之间存在偏差的问题.通过建立与区间型语言标度对应的术语指标矩阵,及方案综合属性值与决策者主观偏好值之间的投影模型,确定属性的权重,然后运用加权法得到方案的综合属性值,利用已有的可能度矩阵排序公式得到决策方案的排序.构建了一种基于方案综合属性质与决策者主观偏好值之间的投影模型,通过算例对该方法的实用性和有效性进行了证明.     

基于DEA-AHP的物流系统绩效评价研究

本文在建立物流系统综合评价层次模型的基础上,基于数据包络分析方法无法考虑决策者偏好及层次分析方法主观性影响过大的缺陷,提出了DEA和AHP相结合的方法对物流系统绩效进行了评价,不同于以往文献对两种方法结合的研究从本质上没有体现决策者偏好的问题,本文提出的方法首先使用AHP方法求出各一层指标的权重,再分别对每个一层指标下的因素使用DEA方法求出各系统的相对效率值,最后将各指标权重和相对效率值结合求出各物流系统的整体效率值并进行排序,该方法在有效地结合两种方法优点的同时,很好的弥补了两种方法的不足,最终的实例分析体现了该方法的适用性和可操作性。     

基于熵权多目标决策的方案评估方法研究

给出了一种基于熵权多目标决策的方案评估方法.该方法在只有判断矩阵而没有专家权重的情况下,对有多个评价指标的方案评估问题,通过多对象关于多指标的评价矩阵的熵权计算,对多个合理方案进行优选评估,得出了可信度较高的优选方案,并将这一方法应用在航空装备维修费效益评价实例中.     

一种新型风险型多指标决策方法研究

传统的风险型多指标决策模型没有考虑决策者对风险的态度,而决策者对风险的态度会影响决策的结果,针对这一问题文章在累积前景理论与灰色关联方法的基础上,提出一种考虑决策者风险偏好的风险型多指标决策的方法.该方法首先利用极差化法对风险决策矩阵进行规范化处理,并在此基础上构造出最优与最劣方案;然后利用累积前景理论与灰色关联方法构建前景值函数,并给出利用灰色关联思想确定指标权重的方法与步骤;最终求出各个方案的综合前景值并进行排序选优.通过某电信运营商对管道资源建设方案选择的实例分析说明了方法的可行性与有效性.     

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.     

淘汰式变权TOPSIS法

传统的TOPSIS法不能直接用于常见的淘汰选优的实际决策.提出淘汰式变权TOPSIS法,通过逐步淘汰明显较劣方案,调整符合决策人偏好的权重,可以更好地反映实际决策行为.实例分析表明该法是简单实用的.     

Ranking DMUs by using interval DEA cross efficiency matrix with acceptability analysis

Cross efficiency method is an extension of data envelopment analysis (DEA), and has been widely used for ranking performance of decision making units (DMUs). To eliminate the non-uniqueness of cross efficiency scores, the aggressive and benevolent strategies have been proposed as secondary goals to determine the unique cross efficiency score. The current paper aims to propose an alternative strategy which does not consider the preference of the decision maker in choosing aggressive or benevolent strategy. Instead, the paper considers all possible weight sets in weight space when computing the cross efficiency and each DMU is given an interval cross efficiency. By using the stochastic multicriteria acceptability analysis (SMAA-2) method, all DMUs in the interval cross efficiency matrix (CEM) could be fully ranked according to the acceptability indices. A numerical example about efficiency evaluation to seven academic departments in a university is illustrated.     

逆向物流企业绩效决策与判定——基于主因子TOPSIS和DEA超效率模型的分析

逆向物流企业绩效不但关系到供需双方企业在经济全球化和资源短缺状态下的市场竞争能力,而且也关系到双方企业环境社会责任的管理水平。TOPSIS分析法作为一种离散的多目标决策方法,已经在许多领域取得了成功的应用,本文采用主成分分析和TOPSIS(逼近理想解法)相结合的组合评价法,建立了逆向物流企业绩效评价与决策的主因子TOPSIS模型,在此基础上运用DEA超效率模型,对样本逆向物流企业的经营效率进行分析,并对分析结果进行相对效率判定。研究结果表明,样本逆向物流企业的经营效率总体上较高,但各逆向物流企业存在不同的效率问题,也存在不同的改进方向。     

Ranking decision making units by imposing a minimum weight restriction in the data envelopment analysis

It has been widely recognized that data envelopment analysis (DEA) lacks discrimination power to distinguish between DEA efficient units. This paper proposes a new methodology for ranking decision making units (DMUs). The new methodology ranks DMUs by imposing an appropriate minimum weight restriction on all inputs and outputs, which is decided by a decision maker (DM) or an assessor in terms of the solutions to a series of linear programming (LP) models that are specially constructed to determine a maximin weight for each DEA efficient unit. The DM can decide how many DMUs to be retained as DEA efficient in final efficiency ranking according to the requirement of real applications, which provides flexibility for DEA ranking. Three numerical examples are investigated using the proposed ranking methodology to illustrate its power in discriminating between DMUs, particularly DEA efficient units.     

The interval efficiency based on the optimistic and pessimistic points of view

Data envelopment analysis (DEA) is a data-oriented approach for evaluating the performances of a set of peer entities called decision-making units (DMUs), whose performance is determined based on multiple measures. The traditional DEA, which is based on the concept of efficiency frontier (output frontier), determines the best efficiency score that can be assigned to each DMU. Based on these scores, DMUs are classified into DEA-efficient (optimistic efficient) or DEA-non-efficient (optimistic non-efficient) units, and the DEA-efficient DMUs determine the efficiency frontier. There is a comparable approach which uses the concept of inefficiency frontier (input frontier) for determining the worst relative efficiency score that can be assigned to each DMU. DMUs on the inefficiency frontier are specified as DEA-inefficient or pessimistic inefficient, and those that do not lie on the inefficient frontier, are declared to be DEA-non-inefficient or pessimistic non-inefficient. In this paper, we argue that both relative efficiencies should be considered simultaneously, and any approach that considers only one of them will be biased. For measuring the overall performance of the DMUs, we propose to integrate both efficiencies in the form of an interval, and we call the proposed DEA models for efficiency measurement the bounded DEA models. In this way, the efficiency interval provides the decision maker with all the possible values of efficiency, which reflect various perspectives. A numerical example is presented to illustrate the application of the proposed DEA models.     

Congestion analysis in DEA inputs under weight restrictions

Data envelopment analysis (DEA), which is used to determine the efficiency of a decision-making unit (DMU), is able to recognize the amount of input congestion. Moreover, the relative importance of inputs and outputs can be incorporated into DEA models by weight restrictions. These restrictions or a priori weights are introduced by the decision maker and lead to changes in models and efficiency interpretation. In this paper, we present an approach to determine the value of congestion in inputs under the weight restrictions. Some discussions show how weight restrictions can affect the congestion amount.     

A DEA game model approach to supply chain efficiency

Data envelopment analysis (DEA) is a useful method to evaluate the relative efficiency of peer decision making units (DMUs). Based upon the definitions of supply chain efficiency, we investigate the efficiency game between two supply chain members. It is shown that there exist numerous Nash equilibriums efficiency plans for the supplier and the manufacturer with respect to their efficiency functions. A bargaining model is then proposed to analyze the supplier and manufacturer's decision process and to determine the best efficiency plan strategy. DEA efficiency for supply chain operations is studied for the central control and the decentralized control cases. The current study is illustrated with a numerical example.     

Decision risk analysis for an interval TOPSIS method

TOPSIS is a multi-attribute decision making (MADM) technique for ranking and selection of a number of externally determined alternatives through distance measures. When the collected data for each criterion is interval and the risk attitude for a decision maker is unknown, we present a new TOPSIS method for normalizing the collected data and ranking the alternatives. The results show that the decision maker with different risk attitude ranks the different alternatives.     

Performance evaluation: An integrated method using data envelopment analysis and fuzzy preference relations

In a multi-attribute decision-making (MADM) context, the decision maker needs to provide his preferences over a set of decision alternatives and constructs a preference relation and then use the derived priority vector of the preference to rank various alternatives. This paper proposes an integrated approach to rate decision alternatives using data envelopment analysis and preference relations. This proposed approach includes three stages. First, pairwise efficiency scores are computed using two DEA models: the CCR model and the proposed cross-evaluation DEA model. Second, the pairwise efficiency scores are then utilized to construct the fuzzy preference relation and the consistent fuzzy preference relation. Third, by use of the row wise summation technique, we yield a priority vector, which is used for ranking decision-making units (DMUs). For the case of a single output and a single input, the preference relation can be directly obtained from the original sample data. The proposed approach is validated by two numerical examples.