决策信息为区间二元语义的双向专家权重确定方法

针对区间二元语义多属性群决策中的专家客观赋权问题,提出了一种区间二元语义群决策的双向专家权重确定方法。首先设置专家初始权重,通过专家个体与群体决策矩阵的偏差距离计算专家的偏离权重,再通过专家的直觉模糊熵计算专家的模糊熵权重,结合偏离权重和模糊熵权重,经过多次迭代后得到稳定的专家双向权重。该权重既反映了专家偏好信息与群体偏好信息的一致性,同时也反映了专家对决策问题的了解程度。最后,实例验证了该算法的可行性与有效性。     

基于二元语义的不确定多属性群决策方法

为了解决专家权重、指标权重均未知的混合型多属性群决策问题,提出基于二元语义的多属性群决策方法。首先,针对专家权重问题,提出了运用判断矩阵导出向量进行聚类分析,并综合类间、类内权重得出结果。在此基础上,集结多名专家意见得到每个方案的二元语义矩阵,根据离差最大化原理,客观确定属性的指标权重,求出每个方案的值。根据数值大小比较排序;最后以多种型号导弹作为算例,用所提出方法对混合型多属性决策问题进行求解,结果表明该方法易于操作且可为决策者提供丰富的决策信息。     

用区间直觉模糊集方法对属性权重未知的群求解其多属性决策

本文首先提出群区间直觉模糊有序加权几何(groupinterval-valuedintuitionistic fuzzy orderedweighted geometric,GIVIFOWG)算子和群区间直觉模糊有序加权平均(group interval-valued intuitionistic fuzzy ordered weighted averaging,GIVIFOWA)算子.利用GIVIFOWG算子或GIVIFOWA算子聚集群的决策矩阵以获得方案在属性上的综合区间直觉模糊决策矩阵(collectiveinterval-valuedintuitionistic fuzzy decision-matrix,CIVIFDM).然后定义了一个考虑犹豫度的区间直觉模糊熵(interval-valuedintuitionistic fuzzyentropy,IVIFE);通过熵衡量每个属性所含的信息来求解属性权重.最后,提出基于可能度的接近理想解的区间排序法(interval technique for order preference by similarity to an ideal solution,ITOPSIS)和区间得分函数法.在ITOPSIS法中,依据区间距离公式计算候选方案和理想方案的属性加权区间距离,进而采用ITOPSIS准则对各方案进行排序;在区间得分函数法中,算出CIVIFDM中各方案的得分值以及精确值,然后利用区间得分准则对各方案进行排序.实验结果验证了决策方法的有效性和可行性.     

一种权重信息完全未知的多属性决策方法

针对权重信息完全未知的多属性决策问题,利用超立方体分割的思想,给出数值模拟决策方法.该方法易于在计算机上实现,并且利用该方法得到的评价结果客观可靠,不具有主观随意性.最后,给出一个实际应用的例子.     

一种基于二元语义信息处理的多属性群决策方法

为了解决评价信息为语言信息的多属性群决策问题,结合VIKOR方法,提出一种基于二元语义信息处理的多属性群决策方法．该方法使用二元语义信息集结算子获得决策群组的决策信息,通过最大化群效用和最小化个体遗憾来获得决策者满意的折衷方案．该方法计算简单,便于理解,可有效避免信息的丢失和扭曲,并可克服理想解方法不能反映出各方案与正负理想解的接近程度的不足．最后,算例计算结果表明了该方法的有效性．     

基于二元语义多属性决策方法的购房评估研究

提出了一种基于二元语义的购房评估方法。在经过调查分析基础上,构建了购房评估模型,将二元语义多属性决策方法应用于购房评估中,为购房者提供参考决策。最后通过实例说明了该方法运用于购房评估的具体过程,并验证了其合理性和可操作性。     

多属性群决策中一种基于主观偏好确定属性权重的方法

提出一种多属性群决策中同时考虑专家群体对属性主观赋权的偏好和决策者对决策重要性认识的偏好来确定属性权重的方法,能够兼容专家实数型、区间型和语言型等类型的属性权重赋值.首先建立标准属性重要差异矩阵以实现专家对属性赋权的优劣比较和差异规范,定义统一的决策者偏好映射对其进行调整;然后求解各矩阵的排序向量以量化属性的相对重要程度,并与专家权重聚合得到属性权重向量;最后给出了方法的具体步骤,并通过算例说明了该方法的具体应用.     

专家权重完全未知的区间直觉不确定语言多属性群决策方法

针对专家权重信息完全未知且属性值为区间直觉不确定语言数的模糊多属性群决策问题,提出一种基于混合权重信息及决策者风险态度的群决策分析方法。在定义区间直觉不确定语言数差异度的基础上,分别利用专家在方案评价值上的贴近度以及方案排序上的一致度来计算两类专家权重,并基于均衡度得到专家的客观综合权重。进而通过融合专家客观综合权重以及基于相似度的个体综合评价值权重,提出一种混合加权集结方法,从而得到方案的群体综合评价值,并通过定义带有风险态度因子的期望值与精确函数实现对方案的比较和排序。最后,通过实例分析证明所提方法的有效性和合理性。     

二元语义粗算子及其语言多属性决策中的应用

针对语言多属性决策,提出一种基于二元语言信息处理和粗糙集理论的求解方法。利用规范的语言评价信息建立决策信息表,提出了一种由属性依赖度和信息度来形成属性客观权重的方法,通过二元语义集成算子计算属性的主观权;根据决策者的偏好,将各属性的主客观权重信息集成,得到属性综合权重;将规范化的语言评价信息转化为二元语言形式,并与已有的信息集结算子合成,得到二语义粗算子;举例说明二元语义粗算子的应用。     

主客观权重集成及扩展VIKOR的多属性群决策方法

针对评价信息、属性权重均为不同粒度语言短语的多属性群决策问题,提出一种基于主客观权重集成及扩展多准则协调优化解（VIKOR）的多属性群决策方法。由基本语言评价集实现对多粒度语言评价矩阵的一致化,基于同一粒度的语言决策矩阵计算群体对属性的评价偏差,基于群体评价意见的一致性原则得到属性客观权重,通过二元语义加权算术平均（T-WAA）算子得到属性主观权重,从而集成主、客观权重求得属性综合权重。集结转化后的单个评价矩阵得到群体评价矩阵及其导出矩阵,由扩展VIKOR方法,根据群效用值、个体遗憾值及综合评价值分别对方案进行排序,获得折衷方案。算例分析表明该方法的有效性与可行性。     

Grey relational analysis method for 2-tuple linguistic multiple attribute group decision making with incomplete weight information

With respect to 2-tuple linguistic multiple attribute group decision making problems with incomplete weight information, some basic concepts and operational laws of 2-tuple linguistic variables are introduced. An optimization model based on the maximizing deviation method, by which the attribute weights can be determined, is established. According to the traditional ideas of grey relational analysis (GRA), the optimal alternative(s) is determined by calculating the linguistic degree of grey relation of every alternative and 2-tuple linguistic positive ideal solution and 2-tuple linguistic negative ideal solution. It is based on the concept that the optimal alternative should have the largest degree of grey relation from positive ideal solution and the smallest degree of grey relation from the negative ideal solution. The method has exact characteristic in linguistic information processing. It avoided information distortion and losing which occur formerly in the linguistic information processing. Finally, a numerical example is used to illustrate the use of the proposed method. The result shows the approach is simple, effective and easy to calculate.     

Extension of TOPSIS method for 2-tuple linguistic multiple attribute group decision making with incomplete weight information

With respect to linguistic multiple attribute group decision making problems with incomplete weight information, a new method is proposed. In the method, the 2-tuple linguistic representation developed in recent years is used to aggregate the linguistic assessment information. In order to get the weight vector of the attribute, we establish an optimization model based on the basic ideal of traditional technique for order performance by similarity to ideal solution, by which the attribute weights can be determined. Then, the optimal alternative(s) is determined by calculating the shortest distance from the 2-tuple linguistic positive ideal solution, and on the other side, the farthest distance of the 2-tuple linguistic negative ideal solution. The method has exact characteristic in linguistic information processing. It avoided information distortion and losing, which occur formerly in the linguistic information processing. Finally, a numerical example is used to illustrate the use of the proposed method. The result shows the approach is simple, effective, and easy to calculate.     

A consensus reaching model for 2-tuple linguistic multiple attribute group decision making with incomplete weight information

The aim of this paper is to put forward a consensus reaching method for multi-attribute group decision-making (MAGDM) problems with linguistic information, in which the weight information of experts and attributes is unknown. First, some basic concepts and operational laws of 2-tuple linguistic label are introduced. Then, a grey relational analysis method and a maximising deviation method are proposed to calculate the incomplete weight information of experts and attributes respectively. To eliminate the conflict in the group, a weight-updating model is employed to derive the weights of experts based on their contribution to the consensus reaching process. After conflict elimination, the final group preference can be obtained which will give the ranking of the alternatives. The model can effectively avoid information distortion which is occurred regularly in the linguistic information processing. Finally, an illustrative example is given to illustrate the application of the proposed method and comparative analysis with the existing methods are offered to show the advantages of the proposed method.     

一种基于二元语义的混合型多属性决策方法

针对指标权重未知的混合型多属性决策问题,提出一种基于二元语义的决策方法.首先,定义了语言评价变量与三角模糊数的转化规则和二元语义之间的距离,给出了不同类型指标数据与二元语义的转化;然后,利用与正理想解灰色关联度偏差最小原理,确定了属性的指标权重,并利用二元语义加权算术平均值对方案进行排序;最后,通过应用案例说明了所提方法的决策步骤,并与TOPSIS方法进行了比较,表明了所提方法的有效性和优越性.     

A method for multiple attribute decision making with incomplete weight information in linguistic setting

The aim of this paper is to investigate the multiple attribute decision making problems with linguistic information, in which the information about attribute weights is incompletely known, and the attribute values take the form of linguistic variables. We first introduce some approaches to obtaining the weight information of attributes, and then establish an optimization model based on the ideal point of attribute values, by which the attribute weights can be determined. For the special situations where the information about attribute weights is completely unknown, we establish another optimization model. By solving this model, we get a simple and exact formula, which can be used to determine the attribute weights. We utilize the numerical weighting linguistic average (NWLA) operator to aggregate the linguistic variables corresponding to each alternative, and then rank the alternatives by means of the aggregated linguistic information. Finally, the developed method is applied to the ranking and selection of propulsion/manoeuvring system of a double-ended passenger ferry.     

基于云模型具有语言评价信息的多属性群决策研究

针对多属性群决策中具有语言评价信息偏好的表示与集结的关键问题．研究了基于云模型的决策专家个体偏好表示、偏好集结和方案优选方法．首先采用云模型表示决策者给出的自然语言评价信息,而属性和决策者权重大小则用云的语气运算表示;然后用浮动云进行偏好集结,根据云模型的相对距离进行方案的排序和优选．此方法可充分表达评价语言的模糊性和随机性,具有较大的客观性．