基于相对熵的多粒度语言信息的多属性群决策方法

针对具有多粒度语言评价信息的多属性群决策问题,提出了一种基于二元语义信息处理和相对熵的群决策方法。该方法首先给出了多粒度语言评价信息一致化为由基本语言评价集表示的相同粒度二元语义信息的方法,然后对于属性权重信息不完全的情形,建立了基于相对熵的多目标规划模型获得相应的属性权重,并利用二元语义的集结算子对语言评价信息进行加权集成,从而获得各个决策方案的排序和择优结果;最后给出一个实例分析,说明了该方法的有效性和可行性。     

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。     

基于二元语义的纯语义多属性群决策方法

考虑属性评估值、专家权重以及属性权重均以语义信息形式给出的纯语义多属性群决策问题,提出了一种基于二元语义的纯语义多属性群决策方法.采用二元语义表达模型处理专家给出的纯语义评估信息;提出了融合加权调和平均算子和有序加权平均算子的二元语义混合调和平均(2-tuple hybrid harmonic average,2THHA)算子,解决属性评估值以及属性权重的群决策问题.针对传统灰色关联分析方法仅考虑单一参考序列以及没有考虑属性间非线性补偿关系的问题,提出了改进灰色关联分析,同时考虑正理想参考序列和负理想参考序列计算候选方案在每个属性点上的灰色关联度;考虑属性间的非线性补偿,得到了方案的总体评估值.所提方法最后被应用于产品服务系统配置方案选择问题.     

基于二元语义TAC积分算子的语言型多属性群决策方法

针对属性之间存在模糊关联的语言型多属性群决策问题,给出了二元语义TAC(Two-Additive Choquet)积分算子的定义,分析和证明了算子的有关性质,并提出了相应的决策方法。该方法首先将各专家提供的语言短语形式的属性权重信息、属性关联信息与属性评价信息转化为二元语义形式,然后利用二元语义TAC积分算子将转化后的属性相关信息集结为各专家的方案评价值,并进一步集结专家意见获得方案的综合评价值,从而确定其排序。最后,通过实例分析和方法比较说明了所给方法的有效性和优点。研究结果表明,该方法具有属性关联刻画细致、计算过程简单且无信息损失、决策结果可解释性强等优点,为求解属性之间存在模糊关联的语言型多属性群决策问题提供了一种新的途径。     

基于两粒度语言评价信息的双边匹配决策

针对基于两粒度语言评价信息的双边匹配问题,提出了一种了基于二元语义信息处理的决策方法。在该方法中,首先将两粒度语言评价信息转化为两粒度二元语义信息;考虑以每个主体满意度最大为目标,运用广义二元语义加权平均算子构建了多目标优化模型;进一步地,运用二元语义算术平均算子将多目标优化模型转化为双目标优化模型;根据二元语义的自身特点将双目标优化模型转化为单目标优化模型,进而进行求解来得到匹配方案。最后,给出一个算例说明所提供方法的有效性。     

一种基于残缺语言判断矩阵的群决策方法

本文针对具有残缺语言判断矩阵形式方案偏好信息的群决策问题,提出了一种决策分析方法.首先,阐述了二元语义的概念,并提出了一种扩展的二元语义有序加权平均(ETOWA)算子;然后,采用ETOWA算子集结具有残缺语言判断矩阵形式的方案偏好信息,可计算出每个方案优于其他方案的总体偏好程度,进而可得到所有方案的排序结果.最后,通过给出一个算例说明了本文提出方法的可行性和实用性.     

基于直觉模糊集的多属性决策方法

对基于直觉模糊信息的多属性决策问题进行了研究,引入了直觉模糊数的得分函数、直觉模糊正理想点和负理想点,然后给出了基于TOPSIS的多属性决策方法,通过计算各备选方案的得分向量与直觉模糊负理想点得分向量之间的距离来确定各备选方案的综合评价指数,进而判断方案的优劣次序.最后,通过一个具体的实例分析说明了该方法的有效性与具体应用过程.     

基于不确定信息处理的语言群决策方法

研究了具有语言偏好信息的语言群决策问题。首先提出了一种基于不确定信息处理的二元语义混合加权平均（T—HWA）算子，并对该算子的性质进行了分析，然后给出了一种基于T—HWA算子的语言群决策（TGDM）方法。最后，通过算例对新方法的有效性进行了验证。     

基于二元语义信息处理的项目群决策方法

项目群的决策是项目群管理的一个重要组成部分,是一个涉及众多不确定性因素的系统工程。本文给出项目群中子项目的评价依据,在此基础上,将二元语义信息处理的方法应用于项目群决策,给出了基于二元语义信息处理的项目群决策的算法步骤,最后通过一个算例,说明该方法的有效性和实用性。     

基于二元语义的犹豫模糊语言决策方法

当决策者在给出语言评价信息而表示出犹豫时,决策信息更适合用犹豫模糊语言术语集表达。为了减少语言决策过程中信息的丢失,得到较精准的评价结果,本文提出基于二元语义的犹豫模糊语言决策方法。首先定义了犹豫模糊二元语义集、犹豫模糊二元语义集的均值函数、方差函数及其集结算子,然后用集结算子求出各方案的综合评价值,通过犹豫模糊二元语义的均值函数和方差函数确定方案排序。最后通过实例说明了该方法的实用性和有效性。     

基于区间值直觉模糊不确定语言变量的灰色关联度分析方法

给出了区间值直觉模糊不确定语言环境下的灰色关联度分析方法。首先确定了区间值直觉模糊不确定语言正负理想解, 然后计算每个评价值与正负理想解的灰色关联度, 利用属性的权重向量, 计算方案与正负理想解的灰色关联度, 最后计算出方案的相对关联度, 并根据方案的相对关联度对方案进行排序。如果属性权重部分可知, 则需要根据与正理想解有最大的灰色关联度而与负理想解有最小的关联度的原则建立数学规划确定属性的权重。最后, 为了说明算法的可行性和有效性, 将其应用到房地产开发项目的风险评价上。 实例说明了算法的可行性和有效性。     

Projection method for multiple criteria group decision making with incomplete weight information in linguistic setting

Multiple criteria group decision making (MCGDM) problems have become a very active research field over the last decade. Many practical problems are often characterized by MCGDM. The aim of this paper is to develop a new approach for MCGDM problems with incomplete weight information in linguistic setting based on the projection method. Firstly, to reflect the reality accurately, a method to determine the weights of decision makers in linguistic setting is proposed by calculating the degree of similarity between 2-tuple linguistic decision matrix given by each decision maker and the average 2-tuple linguistic decision matrix. By using the weights of decision makers, all individual 2-tuple linguistic decision matrices are aggregated into a collective one. Then, to determine the weight vector of criteria, we establish a non-linear optimization model based on the basic ideal of the projection method, i.e., the optimal alternative should have the largest projection on the 2-tuple linguistic positive ideal solution (TLPIS). Calculate the 2-tuple linguistic projection of each alternative on the TLPIS and rank all the alternatives according to the 2-tuple linguistic projection value. Finally, an illustrative example is given to demonstrate the calculation process of the proposed method, and the validity is verified by comparing the evaluation results of the proposed method with that of the technique for order preference by similarity to ideal solution (TOPSIS) method.     

A new method for multiple criteria group decision making with incomplete weight information under linguistic environment

A new method is proposed to solve multiple criteria group decision making (MCGDM) problems, in which both the criteria values and criteria weights take the form of linguistic information, and the information about linguistic criteria weights is partly known or completely unknown. Firstly, to get reasonable decision result, instead of assigning the same weight to the decision maker (DM) for all criteria, we propose a method to determine the weight of DM with respect to each criterion under linguistic environment by calculating the similarity degree between individual 2-tuple linguistic evaluation value and the mean given by all decision makers (DMs). Secondly, for the situations where the information about the criteria weights is partly known or completely unknown, we establish optimization models to determine the criteria weights by defining 2-tuple linguistic positive ideal solution (TL-PIS), 2-tuple linguistic right negative ideal solution (TL-RNIS) and 2-tuple linguistic left negative ideal solution (TL-LNIS) of the collective 2-tuple linguistic decision matrix. Thirdly, we propose a new method to solve MCGDM problems with partly known or completely unknown linguistic weight information. Finally, an illustrative example is given to demonstrate the calculation process of the proposed method.     

Multi-attribute group decision-making with multi-granularity linguistic assessment information: An improved approach based on deviation and TOPSIS

With respect to group decision-making problems with multi-granularity linguistic assessment information, a new approach is proposed. Firstly, the computational formulae are given in order to transform and unify the multi-granularity linguistic comparison matrices. Secondly, the method of standard and mean deviation is applied to determine the unknown attribute weights, and the weights of the decision makers will be determined by using the extended TOPSIS (technique for order preference by similarity to an ideal solution) method. Finally, based on the LWAA (linguistic weighted arithmetic averaging) operator, information on the preference provided by each decision maker is aggregated into the comprehensive evaluation value of each alternative, and the most desirable alternative is selected. The proposed approach expands the research in multi-attribute group decision-making with multi-granularity linguistic assessment information by both considering the weights of the attributes and decision makers, and objective weighting for them. A numerical example is given to illustrate the practicability and usefulness of the proposed approach.     

Group decision making based on novel fuzzy modified TOPSIS method

The aim of this paper is to present a novel fuzzy modified technique of order preference by a similarity to ideal solution (TOPSIS) method by a group of experts, which can select the best alternative by considering both conflicting quantitative and qualitative evaluation criteria in real-life applications. The proposed method satisfies the condition of being the closest to the fuzzy positive ideal solution and also being the farthest from the fuzzy negative ideal solution with multi-judges and multi-criteria. The performance rating values of alternatives versus conflicting criteria as well as the weights of criteria are described by linguistic variables and are transformed into triangular fuzzy numbers. Then a new collective index is introduced to discriminate among alternatives in the evaluation process with respect to subjective judgment and objective information. This paper shows that the proposed fuzzy modified TOPSIS method is a suitable decision making tool for the manufacturing decisions with two examples for the robot selection and rapid prototyping process selection.     

基于二元语义评价信息的个体优势特征识别方法

本文依据群体语言评价信息特点,基于二元语义信息处理、理想点评价模型及聚类分析等方法,给出了基于二元语义评价信息并适用于层次结构的个体优势特征识别方法;对某企业的文化优势特征进行识别,演示了方法的使用过程,并说明了所提方法的可行性和有效性。从二元语义的评价信息中,本方法能够比较充分地挖掘和体现被测行为主体的个体优势特征,能够为决策者提供多种维度的决策信息。     

Multisourcing suppliers selection in service outsourcing

Multisourcing suppliers selection in service outsourcing involves selecting a supplier portfolio with a reasonable number of suppliers and better performance to cover aspiration levels of criteria. It is a specific weighted matching problem with new challenges. This paper proposes a decision method for solving this problem. In the proposed method, different formats of preference information, including numerical values, interval numbers and linguistic variables, are used to express alternative ratings. The technique for order preference by similarity to ideal solution is extended to aggregate the three formats of preference information. A bi-objective 0–1 linear programming model using the aggregated information is built to select a desired supplier portfolio, in which the objectives of minimization of suppliers number and maximization of supplier performance are involved. To solve this model, we transform it into an equivalent, and then an exact multi-objective branch-and-bound algorithm is developed to obtain Pareto-optimal solutions. In addition, a real case of an insurance company is used to illustrate the applicability of the proposed method.     

A new methodology for fuzzy multi-attribute group decision making with multi-granularity and non-homogeneous information

The aim of this paper is to develop a new methodology for solving fuzzy multi-attribute group decision making problems with non-homogeneous information, including multi-granular linguistic term sets, fuzzy numbers, interval values and real numbers. In this methodology, different distances are defined to measure differences between alternatives and the ideal solution as well as the negative ideal solution. A relative closeness method is developed by introducing the multi-attribute ranking index based on the particular measure of closeness to the IS. The proposed method determines a compromise solution for the group, providing a maximum “group utility” for the “majority” and a minimum of an individual regret for the “opponent”. The implementation process, effectiveness and feasibility of the method proposed in this paper are illustrated with a real example of the missile weapon system design project selection.     

An interactive procedure for linguistic multiple attribute decision making with incomplete weight information

In this paper, we consider the multiple attribute decision making (MADM) problems, in which the information about attribute weights is partly known and the attribute values are expressed in linguistic labels. We first define the concepts of linguistic positive ideal point, linguistic negative ideal point, and satisfactory degree of alternative. Based on these concepts, we then establish some linear programming models, through which the decision maker interacts with the analyst. Furthermore, we establish a practical interactive procedure for solving the MADM problems considered in this paper. The interactive process can be realized by giving and revising the satisfactory degrees of alternatives till an optimum satisfactory solution is achieved. Finally, a practical example is given to illustrate the developed procedure.     

Multi-criteria semantic dominance: A linguistic decision aiding technique based on incomplete preference information

A linguistic decision aiding technique for multi-criteria decision is presented. We define a relation between alternatives as multi-criteria semantic dominance (MCSD). It adopts the similar ideal of the stochastic dominance by utilizing the partial information of the decision maker’s preference, which is only ordinal or partially cardinal. The MCSD rules based on three typical types of semanteme functions are introduced and proven. By using these rules, all the alternatives under consideration are divided into two mutually exclusive sets called efficient set and inefficient set. The decision maker who has such a semanteme function will never choose the alternative from the corresponding inefficient set as the optimal one. In such a way, when we analyze the linguistic decision information, the inherent fuzziness of preference can be handled and several controversial operations of the linguistic terms can be avoided. An example is also provided to illustrate the procedure of the proposed method.