基于期望值-混合熵的区间概率模糊随机多准则决策方法

定义了区间概率模糊随机变量及其期望值和混合熵.针对准则权重确知并且准则值为区间概率模糊随机变量的多准则决策问题,提出一种基于期望值-混合熵的决策方法.该方法首先给出了区间概率模糊随机变量的期望值-混合熵度量;然后基于此度量建立优化模型,通过计算得到各方案的期望值-混合熵区间;再采用可能度的方法得到方案集的排序.最后通过实例说明了该方法的有效性和可行性.     

基于期望值-混合熵的区间概率模糊随机多准则决策方法

定义了区间概率模糊随机变量及其期望值和混合熵.针对准则权重确知并且准则值为区间概率模糊随机变量的多准则决策问题,提出一种基于期望值-混合熵的决策方法.该方法首先给出了区间概率模糊随机变量的期望值-混合熵度量;然后基于此度量建立优化模型,通过计算得到各方案的期望值-混合熵区间;再采用可能度的方法得到方案集的排序.最后通过实例说明了该方法的有效性和可行性.

     

基于概率信息不完备的群决策模型

针对犹豫模糊元中元素发生的概率信息不完备的群决策问题,提出一种基于最优化模型和一致性调整算法的群决策模型。该模型首先引入了概率不完备犹豫模糊偏好关系（PIHFPR）、概率不完备犹豫模糊偏好关系的期望一致性以及概率不完备犹豫模糊偏好关系的满意加性期望一致性等概念;其次,以PIHFPR和排序权重向量间的偏差最小化作为目标函数,构建线性最优化模型计算得到PIHFPR中不完备的概率信息;随后,通过提出的加权概率不完备犹豫模糊偏好关系集成算子确定综合的PIHFPR,同时设计一种群体一致性调整算法,不仅使得调整后的PIHFPR具有满意加性期望一致性,还可以计算方案的排序权重。最后,将群决策模型应用于区块链的选择实例中。实验结果表明,决策结果合理可靠,且更能反映实际决策情况。     

基于决策熵的不完备信息系统的知识约简方法

为有效地解决不完备信息系统的知识约简,得到更优的决策规则集,研究了基于容差关系的决策熵在不完备信息系统中能客观反映决策规则的决策能力,提出了一种基于决策熵的不完备知识约简方法.该方法基于决策熵的思想,考虑了决策规则可信度和对象覆盖度,同时引入了容差关系,以决策熵的属性重要性度量为启发信息进行知识约简,最终得到确定的规则集.仿真实验结果表明了该算法的可行性.     

基于加权α优势关系的多属性决策排序方法*

α优势粗糙集模型利用简单平均法赋权多个阈值α的排序结果,忽视数据集本身信息,导致不同数据集的排序质量差异性较大.针对此问题,文中提出基于加权α优势关系的优势度排序方法.首先运用α优势粗糙集方法详细分析决策对象.在此基础上,为了克服α主观赋权导致多属性决策排序结果中“并列”决策现象存在的不足,依据排序结果采用2种准则赋权α,并融合2种准则下所有对象的综合优势度,进一步细化排序结果.最后在具体算例中对比分析其它排序方法,验证文中方法的可行性和有效性.     

基于改进的可能度优势关系的排序方法研究

优势关系是解决多属性决策问题的一种重要方法,也是研究不完备区间值信息系统的一种重要方法。在不完备区间值信息系统排序方法的研究中,针对属性过多从而可能引起排序失效的问题,提出了一种改进的可能度优势关系排序方法。这种新的排序方法把区间值优势关系和可能度相结合来改进经典优势关系定义的不足,并给出了平均综合优势度的定义。最后,通过与 ∝-β优势关系和容差优势关系等排序方法在具体算例中的比较分析,证明了改进的可能度优势关系排序方法不仅能够解决不完备区间值信息系统中的问题,而且能够使排序结果更合理、有效。     

基于粒计算的犹豫模糊多准则决策方法*

提出基于粒计算的犹豫模糊多准则决策方法.给出各个准则下对应的犹豫模糊集中犹豫模糊元的大于可能度定义,并构造相应准则下的加性一致的模糊偏好矩阵.根据各准则的模糊偏好矩阵对应的预序熵及预序粒结构相似度确定属性的权重,对各个准则下模糊偏好矩阵的排序向量加权平均得到最终的排序向量.文中方法以评价数据序信息量及准则序与整体之间的关系确定准则权重,通过计算加权两两比较下的排序向量得到最终的排序决策结果.最后运用实例验证算法的有效性及可行性.     

基于综合云的不确定语言多准则群决策方法

针对准则权重已知、决策者权重未知、准则值为不确定语言的多准则群决策问题,提出一种基于云模型的决策方法.该方法首先将不确定语言值转化为综合云;然后采用生成浮动云的方法进行偏好集结,并通过计算＂不确定度＂和＂决策者偏差度＂求得决策者权重;最后引入Hamming距离求得贴近度大小,通过比较得到方案集的排序.实例分析表明了该方法的有效性和可行性.     

基于聚类离散化和变精度邻域熵的属性约简

针对传感网采集数据的不完备性,利用数据本身特点,通过定义类簇指标,提出基于改进K-means聚类算法的数据离散化方法,以减小噪声、孤立点和不完备数据集对决策识别结果产生的影响;然后,通过引入互信息熵的属性重要度度量和变精度修正系数,提出基于互信息熵的变精度邻域粗糙集属性约简启发式算法,整合变精度和邻域粗糙集的优势,在减小约简算法计算复杂度的同时提高决策系统识别精度.仿真结果表明了算法在提高决策系统识别精度和降低其计算复杂度方面的有效性,模拟环境测试进一步验证了其工程适用性.     

基于直觉模糊云模型的TOPSIS多属性决策方法

针对属性值为直觉模糊数的多属性决策问题,提出了一种基于直觉模糊云模型的TOPSIS多属性决策方法。首先,利用直觉模糊云对备选方案的各个属性值进行描述,计算其数字特征——期望、熵和超熵;然后,构造各数字特征的决策矩阵,获得其对应的正、负理想解;最后,计算各数字特征与正、负理想解间的距离,进而获得综合贴近度,对备选方案进行排序,获得最优决策结果,并通过具体数值实例验证方法的合理性和有效性。     

基于符号距离和交叉熵的概率犹豫模糊多属性决策方法

针对概率犹豫模糊环境下属性权重完全未知的多属性决策问题,提出基于符号距离和交叉熵的多属性决策方法.首先,定义用于测量决策者犹豫程度的3种概率犹豫模糊元的犹豫度:数值犹豫度,信息不完全度和总犹豫度,基于3种犹豫度提出概率犹豫模糊符号距离;然后,为了避免人为添加元素,定义调和概率犹豫模糊元,并结合信息不完全度提出概率犹豫模糊元的交叉熵;最后,根据概率犹豫模糊元的符号距离和交叉熵构建多属性决策模型,并通过算例验证了该模型的有效性和合理性.     

A novel weight‐derived method and its application in graduate students' physical health assessment

In multiattribute decision making, the analytic network process (ANP) is an important methodology to derive the subjective weights of attributes when the dependence and feedback relations exist between attributes, and the number of attributes should be no more than seven in a comparison matrix. To reduce the dimensions of attributes, we propose a hybrid hesitant fuzzy linguistic factor analysis method to cluster the attributes into main factors. The method takes multiple forms of decision‐making information into consideration, such as single linguistic terms, hesitant fuzzy linguistic terms, and numeric values. Meanwhile, the objective weights of the main factors are obtained as well. As for the subjective weights of main factors, the incomplete probabilistic linguistic ANP is developed after improving the incomplete probabilistic linguistic preference relation with multiplicative consistency. At last, the final weights of the main factors are calculated by combining the objective and subjective weights. A questionnaire survey about assessing the weights of the main factors influencing graduate students' physical health is designed to explain the application of the proposed methodology. To sum up, the main importance and contributions of this study are as following: (1) developing a hybrid hesitant fuzzy linguistic factor analysis method and incomplete probabilistic linguistic ANP, (2) proposing a novel weight‐derived method from both objective and subjective perspectives, and (3) applying it to graduate students' physical health assessment.     

基于广义扩展优势关系的粗糙决策分析方法

针对信息不完全的偏好多属性决策问题,给出一种基于拓展粗糙集的决策分析方法．首先提出广义扩展优势关系的概念;然后在广义扩展优势关系下得到知识的粗糙近似,给出分类决策规则．对比分析证明,扩展优势关系和有限扩展优势关系都是广义扩展优势关系的特例．最后通过一个实例验证了所提出方法的可行性和有效性．     

基于直觉模糊数的信息不完全的多准则规划方法

定义了直觉模糊数和直觉梯形模糊数及其期望值.针对权系数信息不完全确定和准则值为直觉梯形模糊数的多准则决策问题,提出了信息不完全确定的直觉梯形模糊多准则决策的规划方法.该方法利用权系数的不完全信息构造方案集综合期望值的最优线性规划模型,求解该模型得到各准则的最优权系数,进而得到各方案综合期望值的区间数.利用区间数可能度法对其进行比较,得到整个方案集的排序.实例分析说明了该方法的有效性和可行性.     

Multi-criteria group decision making with incomplete hesitant fuzzy preference relations

In order to simulate the hesitancy and uncertainty associated with impression or vagueness, a decision maker may give her/his judgments by means of hesitant fuzzy preference relations in the process of decision making. The study of their consistency becomes a very important aspect to avoid a misleading solution. This paper defines the concept of additive consistent hesitant fuzzy preference relations. The characterizations of additive consistent hesitant fuzzy preference relations are studied in detail. Owing to the limitations of the experts’ professional knowledge and experience, the provided preferences in a hesitant fuzzy preference relation are usually incomplete. Consequently, this paper introduces the concepts of incomplete hesitant fuzzy preference relation, acceptable incomplete hesitant fuzzy preference relation, and additive consistent incomplete hesitant fuzzy preference relation. Then, two estimation procedures are developed to estimate the missing information in an expert's incomplete hesitant fuzzy preference relation. The first procedure is used to construct an additive consistent hesitant fuzzy preference relation from the lowest possible number, (n − 1), of pairwise comparisons. The second one is designed for the estimation of missing elements of the acceptable incomplete hesitant fuzzy preference relations with more known judgments. Moreover, an algorithm is given to solve the multi-criteria group decision making problem with incomplete hesitant fuzzy preference relations. Finally, a numerical example is provided to illustrate the solution processes of the developed algorithm and to verify its effectiveness and practicality.     

基于熵权直觉模糊拓展MULTIMOORA的机场消防站选址评价模型

针对准则权重信息完全未知的直觉模糊多准则群决策问题,考虑经典直觉模糊熵公式及MULTIMOORA模型的不足,提出新的直觉模糊熵公式以充分刻画直觉模糊数的直觉信息和模糊信息,定义占优凸组合运算,拓展解决多准则决策问题的MULTIMOORA模型以适用于解决多准则群决策问题.在以直觉模糊数为信息输入的系统框架内,构建机场消防站选址评价指标体系,进而提出基于熵权直觉模糊拓展MULTIMOORA的机场消防站选址评价模型.案例分析表明了所提出方法的可行性,为机场消防站选址评价提供了一种新思路.     

Extensions of dominance-based rough set approach in incomplete information system

As one of the useful extensions of classical rough set approach, the dominance-based rough set approach has been successfully applied into multi-criteria decision problems. However, the traditional dominance-based rough set approach is only suitable for the condition attributes, which are positively related with classification analysis. To solve this problem, we propose an extension of the dominance-based rough set approach in incomplete information system by assuming the condition attributes, which are not only positively but also negatively related with classification analysis. Furthermore, by considering the existence of unknown values in incomplete information system, we present the concept of valued dominance relation, which shows the probability of an object is dominating another one with respect to the condition attributes. By using the valued dominance relation, the fuzzy dominance-based rough set models are also studied. A numerical example is employed to substantiate the conceptual arguments. The text was submitted in English by the authors.     

A De Novo multi-approaches multi-criteria decision making technique with an application in performance evaluation of material handling device

Application of multiple conventional approaches to a particular multi-criteria decision making (MCDM) problem often suffers rank reversal giving rise to confusion and ambiguity in appropriate decision making. To eradicate the confusion, this paper proposes a De Novo multi-approaches multi-criteria decision making method namely Technique of Precise Order Preference (TPOP). The TPOP first examines the inconsistency in the ranking order of the alternatives of a MCDM problem by using multiple conventional approaches. If inconsistency/rank reversal in ranking order of the alternatives exists then TPOP, using advanced version of entropy weighting method introduced in this research work, measures weights of the final selection values of conventional approaches. Subsequently, TPOP based on these weights and final selection values computes precise selection indices (PSI) that determines accurate ranking order for the alternatives. The proposed technique is illustrated by two real life examples on material handling device (MHD) ranking and selection problems. The first example is initially solved using five conventional integrated fuzzy multi-criteria decision making techniques (FMCDMs) whereas the second example is taken from previous researchers’ works. The results obtained using TPOP justify the validity, applicability and requirements of the proposed technique. The study shows that the proposed multi-approaches, multi-criteria decision making technique can be a useful and effective model in MCDM.     

基于情绪感知的语言多属性决策方法

情绪对于决策有着重要影响,由于缺乏有效的决策者情绪状态获取方法,当前考虑情绪状态的语言多属性决策方法研究偏少.随着物联网技术的发展,通过可穿戴传感器能够便捷地获取决策者脑电信号,进而可感知其情绪状态.为此,研究基于情绪感知的语言多属性决策方法.首先,提出基于SVM概率输出模型的情绪感知方法,根据决策者脑电信号,实时、准确感知决策者情绪状态的概率分布;其次,提出基于云模型的语言评价定量化方法,一方面考虑语言评价的模糊性与随机性,另一方面考虑决策者情绪状态因素,定量化语言评价;然后,提出基于前景理论的方案排序方法,在情绪泛化假设下,根据综合前景值将各方案排序;最后,通过实例验证该决策方法的可行性和有效性.