基于修正区间模糊Shapley值信息产品供应链利益分配算法

信息产品供应链参与体面临风险差异,提出区间模糊Shapley算法分配信息产品收益以实现公平性。在收益不确定的条件下构造收益模糊值,引入区间模糊Shapley值的隶属度函数,给出确定的分配方案。综合考虑各项风险因素对利益分配的影响,采用模糊层次分析法对风险因子进行修正,以确保信息产品供应链的稳定性     

基于结构元理论的模糊联盟合作对策

仅考虑局中人参与率模糊的合作对策,称为模糊联盟合作对策。将该模型中的模糊参与率用模糊结构元表示,得到基于结构元理论的具有模糊数Choquet积分表达形式的支付函数和Shapley值的理论框架,继而定义结构元线性生成的模糊支付函数和Shapley值表达式。通过算例与区间数的方法进行对比,结果表明：基于结构元理论的模糊联盟合作对策,模型中的模糊数均由结构元线性生成,模糊数之间的四则运算转化成简单的函数表达式之间的四则运算,避免了模糊数之间运算的遍历性,运算简便。运算结果包括区间及区间上各点的隶属度,结果更加精确。     

基于Markov随机过程的动态合作博弈的模糊稳定集

利用模糊数学相关理论,对具有可转移效用的动态合作博弈的区间模糊稳定集进行了研究。首先利用Markov随机过程对动态合作联盟的结构转移进行描述,并考虑到支付函数是三角模糊数的情形,构造了在不同置信度α下的合作博弈的截集取值区域,进而结合动态联盟状态转移矩阵计算出不同时刻点的区间模糊稳定集。考虑到盟友在合作结束后需要对具体的联盟收益进行分配,利用构造的区间模糊稳定集给出了盟友可行的收益分配势值区间。最后利用实例对该方法的有效性和可行性进行了说明。     

模糊合作对策区间Shapley值法的改进及应用

模糊合作对策的收益分配是个复杂问题,受到合作方的风险承担、合作努力、市场竞争、创新贡献和资源投入等因素的影响,而且不同因素有着不同的重要性。运用区间Shapley值法对模糊合作对策的收益进行初步分配。通过将AHP-GEM法和模糊综合评价法相结合,引入收益分配的综合修正因子,对区间Shapley值法进行改进,建立了模糊合作对策利益分配的改进模型。以制造业和物流业联盟为例,说明了改进模型的实用性和可行性。     

基于多目标的随机合作博弈的模糊稳定集

利用模糊数学相关理论,针对n人随机合作博弈的多重目标的情形,对其模糊特性进行分析,构造多重目标的随机合作博弈模型,进而得到随机合作博弈的区间模糊稳定集。考虑到盟友在合作结束后需要对具体的联盟收益进行分配,应用构造的区间模糊稳定集给出确定的收益分配势值区间。使用实例对该方法的有效性和可行性进行说明。     

要素双重模糊下的合作博弈Shapley值的算法

考虑到现实应用中,局中人可能以不同的参与度参加到不同的联盟中,并且他们在合作之前不确定不同合作策略选择下的收益,则在传统合作博弈中应用模糊数学理论。基于Choquet积分,将支付函数和参与度拓展为模糊数,给出要素双重模糊下的模糊合作博弈的定义和模糊合作博弈Shapley值的定义。应用模糊结构元理论,构造了要素双重模糊下的模糊合作博弈的Shapley值,使模糊Shapley值的隶属函数得到解析表达。通过一个算例,来说明该模型的具体应用。可以看出,该研究方法和结论易掌握、推广,使模糊合作博弈理论可以更广泛地应用到现实生活中。     

具有交流结构的区间模糊联盟合作对策

当前具有模糊联盟的合作对策研究主要基于任意局中人可自由结盟的基本假设,但现实结盟活动中,局中人普遍受到资源或地位等因素的限制,其合作往往具有交流结构限制。因此,基于Choquet模糊延拓研究了具有交流结构的区间模糊多人合作对策,给出了相应区间模糊联盟平均树解,并通过公理化体系对此解进行了研究。通过供应链纵向合作创新收益分配实例应用,并与区间模糊联盟合作对策的Shapley值进行比较,说明该方法的现实有效性。     

一种区间值模糊概念格构造方法研究

在区间值模糊形式背景基础上,定义截运算以简化概念格的构造,从而得到区间值模糊概念格.文中给出了区间值模糊概念格构造算法,结合实例进行说明,最后求出了对应的模糊概念格.     

广义区间值模糊粗糙近似算子的构造研究

构造了一组新的广义模糊粗糙近似算子,将其拓展到区间上.在由任意的二元区间值模糊关系构成的广义近似空间中,证明了该组近似算子与区间化的广义Dubois模糊粗糙近似算子是等价的,最后在一般二元区间值模糊关系下对该组近似算子的性质进行了讨论.     

区间值模糊随机多准则决策方法

针对不完全信息的区间值模糊随机多准则决策问题,提出了两种求解方法。第一种方法利用离差最大化构建区间参数线性规划,通过区间数运算法则和定位规划求得最优准则权重向量、状态集结值区间决策矩阵与期望值区间决策矩阵,根据决策者风险偏好水平得到各方案的期望集结值从而确定排序。第二种方法将区间值模糊数决策矩阵转化为直觉模糊数决策矩阵,利用不完全的准则权重,通过规划模型求解,获取各方案在各自然状态下的加权记分函数值与加权精确函数值的区间,利用不完全的状态概率,得到各方案的记分函数期望值与精确函数期望值的区间,根据决策者风险偏好水平,求得各方案的记分函数与精确函数的期望集结值,进而确定方案的排序结果。算例分析验证了两种方法的有效性和可行性。     

Generalized interval-valued fuzzy variable precision rough sets determined by fuzzy logical operators

The fuzzy rough set model and interval-valued fuzzy rough set model have been introduced to handle databases with real values and interval values, respectively. Variable precision rough set was advanced by Ziarko to overcome the shortcomings of misclassification and/or perturbation in Pawlak rough sets. By combining fuzzy rough set and variable precision rough set, a variety of fuzzy variable precision rough sets were studied, which cannot only handle numerical data, but are also less sensitive to misclassification. However, fuzzy variable precision rough sets cannot effectively handle interval-valued data-sets. Research into interval-valued fuzzy rough sets for interval-valued fuzzy data-sets has commenced; however, variable precision problems have not been considered in interval-valued fuzzy rough sets and generalized interval-valued fuzzy rough sets based on fuzzy logical operators nor have interval-valued fuzzy sets been considered in variable precision rough sets and fuzzy variable precision rough sets. These current models are incapable of wide application, especially on misclassification and/or perturbation and on interval-valued fuzzy data-sets. In this paper, these models are generalized to a more integrative approach that not only considers interval-valued fuzzy sets, but also variable precision. First, we review generalized interval-valued fuzzy rough sets based on two fuzzy logical operators: interval-valued fuzzy triangular norms and interval-valued fuzzy residual implicators. Second, we propose generalized interval-valued fuzzy variable precision rough sets based on the above two fuzzy logical operators. Finally, we confirm that some existing models, including rough sets, fuzzy variable precision rough sets, interval-valued fuzzy rough sets, generalized fuzzy rough sets and generalized interval-valued fuzzy variable precision rough sets based on fuzzy logical operators, are special cases of the proposed models.     

Some induced intuitionistic fuzzy aggregation operators applied to multi-attribute group decision making

In this paper, we define various induced intuitionistic fuzzy aggregation operators, including induced intuitionistic fuzzy ordered weighted averaging (OWA) operator, induced intuitionistic fuzzy hybrid averaging (I-IFHA) operator, induced interval-valued intuitionistic fuzzy OWA operator, and induced interval-valued intuitionistic fuzzy hybrid averaging (I-IIFHA) operator. We also establish various properties of these operators. And then, an approach based on I-IFHA operator and intuitionistic fuzzy weighted averaging (WA) operator is developed to solve multi-attribute group decision-making (MAGDM) problems. In such problems, attribute weights and the decision makers' (DMs') weights are real numbers and attribute values provided by the DMs are intuitionistic fuzzy numbers (IFNs), and an approach based on I-IIFHA operator and interval-valued intuitionistic fuzzy WA operator is developed to solve MAGDM problems where the attribute values provided by the DMs are interval-valued IFNs. Furthermore, induced intuitionistic fuzzy hybrid geometric operator and induced interval-valued intuitionistic fuzzy hybrid geometric operator are proposed. Finally, a numerical example is presented to illustrate the developed approaches.     

Robustness of interval-valued fuzzy inference

Since interval-valued fuzzy set intuitively addresses not only vagueness (lack of sharp class boundaries) but also a feature of uncertainty (lack of information), interval-valued fuzzy reasoning plays a vital role in intelligent systems including fuzzy control, classification, expert systems, and so on. To utilize interval-valued fuzzy inference better, it is very important to study the fundamental properties of interval-valued fuzzy inference such as robustness. In this paper, we first discuss the robustness of interval-valued fuzzy connectives. And then investigate the robustness of interval-valued fuzzy reasoning in terms of the sensitivity of interval-valued fuzzy connectives and maximum perturbation of interval-valued fuzzy sets. These results reveal that the robustness of interval-valued fuzzy reasoning is directly linked to the selection of interval-valued fuzzy connectives.     

区间直觉模糊粗糙集

将模糊粗糙集推广到区间直觉模糊粗糙集,基于区间直觉模糊等价关系和两个论域之间的一般区间直觉模糊关系,给出了区间直觉模糊粗糙集模型的不同形式,并讨论了一些相关性质。     

二型模糊集的模糊熵研究

研究基于质心的二型模糊集的模糊熵和加权模糊熵,构造了两个二型模糊集的模糊熵度量.针对二型模糊集的特殊情形,提出一种新的区间值模糊集的模糊熵度量,既弥补了现有区间值模糊集退化为普通模糊集时熵为零的不足,又克服了两个明显不同的区间值模糊集熵相等的缺点.数值实例和仿真实验表明了所提出模糊熵的合理性和实用性.     

基于蕴涵的区间值直觉模糊粗糙集

提出一种基于区间值直觉模糊蕴涵的区间值直觉模糊粗糙集模型.首先,介绍了区间值直觉模糊集、区间值直觉模糊关系和区间值直觉模糊逻辑算子的概念;然后,利用区间值直觉模糊三角模和区间值直觉模糊蕴涵,在区间值直觉模糊近似空间中定义了区间值直觉模糊集的上近似和下近似;最后,给出并证明了这些近似算子的一些性质.     

Incomplete interval-valued intuitionistic fuzzy preference relations

The aim of this paper is to investigate decision making problems with interval-valued intuitionistic fuzzy preference information, in which the preferences provided by the decision maker over alternatives are incomplete or uncertain. We define some new preference relations, including additive consistent incomplete interval-valued intuitionistic fuzzy preference relation, multiplicative consistent incomplete interval-valued intuitionistic fuzzy preference relation and acceptable incomplete interval-valued intuitionistic fuzzy preference relation. Based on the arithmetic average and the geometric mean, respectively, we give two procedures for extending the acceptable incomplete interval-valued intuitionistic fuzzy preference relations to the complete interval-valued intuitionistic fuzzy preference relations. Then, by using the interval-valued intuitionistic fuzzy averaging operator or the interval-valued intuitionistic fuzzy geometric operator, an approach is given to decision making based on the incomplete interval-valued intuitionistic fuzzy preference relation, and the developed approach is applied to a practical problem. It is worth pointing out that if the interval-valued intuitionistic fuzzy preference relation is reduced to the real-valued intuitionistic fuzzy preference relation, then all the above results are also reduced to the counterparts, which can be applied to solve the decision making problems with incomplete intuitionistic fuzzy preference information.     

基于覆盖的区间直觉模糊粗糙集

在经典的覆盖近似空间中,定义了区间直觉模糊概念的粗糙近似。通过区间直觉模糊覆盖概念,给出了一种基于区间直觉模糊覆盖的区间直觉模糊粗糙集模型。讨论了两种模型的一些相关性质。     

基于改进熵和新得分函数的区间直觉模糊多属性决策

针对决策信息为区间直觉模糊数且属性权重完全未知的多属性决策问题, 提出基于改进的区间直觉模糊熵和新得分函数的决策方法. 首先, 利用改进的区间直觉模糊熵确定属性权重; 然后, 利用区间直觉模糊加权算术平均算子集成信息, 得到各备选方案的综合属性值, 进而指出现有得分函数存在排序失效或排序不符合实际的不足, 同时给出一个新的得分函数, 并以此对方案进行排序; 最后, 通过实例表明了所提出方法的有效性.