模糊数双指标排序的结构元方法

针对现有模糊数排序存在的一些问题,提出了双指标的模糊数排序方法。给出了模糊数隶属函数与其单调变换函数相互转化方法。定义波动数与特征数两个指标,利用这两个指标对模糊数进行排序,并给出了排序原则。该方法可以对各种模糊数进行排序,通过该排序原则常能够简化计算,同时,一定程度上能够弥补一些排序方法不能反映模糊数"波动"情况的问题。通过算例对比分析,本文的方法求解简单,并具有广泛适用性。     

基于新精确函数的区间直觉模糊多属性决策方法

基于区间直觉模糊数隶属度和非隶属度构成的二维几何图形特征给出区间直觉模糊数精确函数的新定义,并将其作为区间直觉模糊数的排序指标,区间直觉模糊数的精确函数值越大,则区间直觉模糊数就越大,进而提出一种权重信息不完全确定的区间直觉模糊多属性决策方法.通过算例分析说明所提出排序指标的有效性和决策方法的可行性.     

D.C.隶属函数模糊集及其应用(II)——D.C.隶属函数模糊集的万能逼近性

本文是D.C.隶属函数模糊集及其应用系列研究的第二部分。指出在实际问题中普遍选用的三角形、半三角形、梯形、半梯形、高斯型、柯西型、S形、Z形、π形隶属函数模糊集等均为D.C.隶属函数模糊集,建立了D.C.隶属函数模糊集对模糊集的万有逼近性。探讨了D.C.隶属函数模糊集与模糊数之间的关系,给出了用D.C.隶属函数模糊集逼近模糊数的-εC e llina逼近形式,得到模糊数与D.C.函数之间的一个对应算子,指出了用模糊数表示D.C.函数的问题。     

全系数模糊两层线性规划

利用结构元方法定义一种模糊数排序准则,对模糊系数(目标函数与约束条件中系数为有界模糊数情形)的隶属函数为非单调函数的情形,给出将全系数模糊两层线性规划等价转化为经典的线性规划的方法,并证明了其合理性.与其它方法相比较,该方法不仅约束条件少,而且运算方法简便.最后,将本文的方法运用到数值算例中,进一步表明该提法的有效性和广泛性.     

基于决策者风险偏好的直觉模糊数排序方法

决策者的风险偏好对决策有着重要的影响。本文通过引入反映决策者风险偏好程度的风险参数,基于直觉模糊数的隶属度、非隶属度和犹豫度,定义排序直觉模糊数的含风险参数的得分函数,并结合直觉模糊加权平均算子给出了一种属性值为直觉模糊数的多属性决策方法。通过算例阐明该方法的可行性和有效性。     

模糊合作博弈Shapley值的模糊结构元表示

将模糊数学理论应用到合作博弈中,用精确的数学表达式来表示实际生活中的模糊事件,又将模糊结构元理论应用到模糊合作博弈中,将模型中的模糊数用模糊结构元表示,以往基于扩张原理的模糊Shapley值的隶属函数非常复杂,本文给出其求解方法,使其得到解析表达.通过一个算例,来说明该模型的具体应用,与支付函数用区间数表示等研究方法相比较,该模型不仅保证了隶属函数的连续性,还给出区间上每个取值的隶属度,可以为管理者提供更精确的信息.     

基于三角形区间二型模糊数的综合评价模型

为解决评价过程中分类等级的边界不确定性问题,将二型模糊集引入到模糊综合评价模型中.利用分类等级的边界限值,分别构造三角形二型区间模糊数的上、下隶属函数,在此基础上由观测数据构建相应的区间值模糊评价矩阵,结合指标权重合成得到区间值综合评价向量,最后利用区间数排序的可能度方法得到评价对象的等级隶属向量并给出评价结果.实例分析表明了该模型的有效性。     

区间直觉模糊数的新得分函数及其在多属性决策中的应用

通过对区间直觉模糊数的犹豫区间进行讨论,提出了区间直觉模糊数的新得分函数和精确函数,并讨论新的得分函数具有的性质,在此基础上给出了区间直觉模糊数的一种新的排序方法.进而,结合区间直觉模糊加权平均算子给出了属性值为区间直觉模糊数的多属性决策方法,并通过算例阐明该方法的可行性和有效性.     

D.C.隶属函数模糊集及其应用(Ⅱ)--D.C.隶属函数模糊集的万能逼近性

本文是D．C．隶属函数模糊集及其应用系列研究的第二部分。指出在实际问题中普遍选用的三角形、半三角形、梯形、半梯形、高斯型、柯西型、S形、Z形、π形隶属函数模糊集等均为D．C．隶属函数模糊集，建立了D．C．隶属函数模糊集对模糊集的万有逼近性。探讨了D．C．隶属函数模糊集与模糊数之间的关系，给出了用D．C．隶属函数模糊集逼近模糊数的e-Cellina逼近形式，得到模糊数与D．C．函数之间的一个对应算子，指出了用模糊数表示D．C．函数的问题。     

模糊数排序的可能度方法

由两个模糊数的隶属函数确定三个面积,据此建立一个对模糊数进行大小比较的可能度计算公式.公式表达式非常简洁,同时还具有传递性、互补性等诸多良好的性质,因而具有很强的实用性和可操作性.对给定的一组模糊数,先利用两两比较的结果建立一个可能度矩阵,同时给出基于可能度矩阵的模糊数排序算法.最后给出一个排序算法的实例.     

模糊运算和模糊有限元静力控制方程的求解

根据模糊数的区间形式表达和区间运算的性质,给出了模糊数和模糊变量的运算规则.据此并依据区间有限元理论,提出了结构模糊有限元静力控制方程的几种求解方法.方法可根据输入模糊数的隶属函数,给出结构响应量的可能性分布.且计算量小,易于实施.算例分析说明了方法是实用和可行的.     

Editorial

Linear programming problems with fuzzy parameters are formulated by fuzzy functions. The ambiguity considered here is not randomness, but fuzziness which is associated with the lack of a sharp transition from membership to nonmembership. Parameters on constraint and objective functions are given by fuzzy numbers. In this paper, our object is the formulation of a fuzzy linear programming problem to obtain a reasonable solution under consideration of the ambiguity of parameters. This fuzzy linear programming problem with fuzzy numbers can be regarded as a model of decision problems where human estimation is influential.     

Ranking fuzzy numbers with integral value

Ranking fuzzy numbers is important in decision making. Since very often the alternatives are evaluated by fuzzy numbers in a vague environment, a comparison between these fuzzy numbers is indeed a comparison between alternatives. This paper proposes a method of ranking fuzzy numbers with integral value. The method, which is independent of the type of membership functions used and the normality of the functions, can rank more than two fuzzy numbers simultaneously. It is relatively simple in computation, especially in ranking triangular and trapezoidal fuzzy numbers. Further, an index of optimism is used to reflect the decision maker's optimistic attitude. Discussion on comparative advantages is included.     

An interactive fuzzy satisficing method for multiobjective 0–1 programming problems with fuzzy numbers through genetic algorithms with double strings

In this paper, by considering the experts' vague or fuzzy understanding of the nature of the parameters in the problem-formulation process, multiobjective 0–1 programming problems involving fuzzy numbers are formulated. Using the a-level sets of fuzzy numbers, the corresponding nonfuzzy α-programming problem is introduced. The fuzzy goals of the decision maker (DM) for the objective functions are quantified by eliciting the corresponding linear membership functions. Through the introduction of an extended Pareto optimality concept, if the DM specifies the degree α and the reference membership values, the corresponding extended Pareto optimal solution can be obtained by solving the augmented minimax problems through genetic algorithms with double strings. Then an interactive fuzzy satisficing method for deriving a satisficing solution for the DM efficiently from an extended Pareto optimal solution set is presented. An illustrative numerical example is provided to demonstrate the feasibility and efficiency of the proposed method.     

An interactive fuzzy satisficing method for structured multiobjective linear fractional programs with fuzzy numbers

In this paper, by considering the experts' vague or fuzzy understanding of the nature of the parameters in the problem formulation process, multiobjective linear fractional programming problems with block angular structure involving fuzzy numbers are formulated. Using the a-level sets of fuzzy numbers, the corresponding nonfuzzy a-multiobjective linear fractional programming problem is introduced. The fuzzy goals of the decision maker for the objective functions are quantified by eliciting the corresponding membership functions including nonlinear ones. Through the introduction of extended Pareto optimality concepts, if the decision maker specifies the degree a and the reference membership values, the corresponding extended Pareto optimal solution can be obtained by solving the minimax problems for which the Dantzig-Wolfe decomposition method and Ritter's partitioning procedure are applicable. Then a linear programming-based interactive fuzzy satisficing method with decomposition procedures for deriving a satisficing solution for the decision maker efficiently from an extended Pareto optimal solution set is presented. An illustrative numerical example is provided to demonstrate the feasibility of the proposed method.     

Interval optimization based line sampling method for fuzzy and random reliability analysis

For structural system with fuzzy variables as well as random variables, a novel algorithm for obtaining membership function of fuzzy reliability is presented on interval optimization based Line Sampling (LS) method. In the presented algorithm, the value domain of the fuzzy variables under the given membership level is firstly obtained according to their membership functions. Then, in the value domain of the fuzzy variables, bounds of reliability of the structure are obtained by the nesting analysis of the interval optimization, which is performed by modern heuristic methods, and reliability analysis, which is achieved by the LS method in the reduced space of the random variables. In this way the uncertainties of the input variables are propagated to the safety measurement of the structure, and the membership function of the fuzzy reliability is obtained. The presented algorithm not only inherits the advantage of the direct Monte Carlo method in propagating and distinguishing the fuzzy and random uncertainties, but also can improve the computational efficiency tremendously in case of acceptable precision. Several examples are used to illustrate the advantages of the presented algorithm.     

[-1,1]上同序单调函数的同序变换群与模糊数运算

定义对称区间[-1,1]上的同序单调有界函数的同序变换,利用文[1]提出的模糊数的结构元表示方法,得到模糊数四则运算的结构元表示以及模糊数运算结果的隶属函数的确定方法。在多数的模糊数运算问题中,结构元的单调变换形式是容易得到的,此时,模糊数的运算将变得非常简单。文中还给出了一个运算的实例。     

Ranking fuzzy numbers with maximizing set and minimizing set

Up to now, these are five methods of ranking n fuzzy numbers in order, but these methods contain some confusions and occasionally conflict with intuition. This paper introduces the concept of maximizing set and minimizing set to decide the ordering value of each fuzzy number and uses these values to determine the order of the n fuzzy numbers. In addition, we give a method for calculating the ordering value of each fuzzy number with triangular, trapezoidal, and two-sided drum-like shaped membership functions.     

Ranking fuzzy quantities based on the angle of the reference functions

Ordering fuzzy quantities and their comparison play a key tool in many applied models in the world and in particular decision-making procedures. However a huge number of researches is attracted to this filed but until now there is any unique accepted method to rank the fuzzy quantities. In fact, each proposed method may has some shortcoming. So we are going to present a novel method based on the angle of the reference functions to cover a wide range of fuzzy quantities by over coming the draw backs of some existing methods. In the mentioned firstly, the angle between the left and right membership functions (the reference functions) of every fuzzy set is called Angle of Fuzzy Set (AFS), and then in order to extend ranking of two fuzzy sets the angle of fuzzy sets and α-cuts is used. The method is illustrated by some numerical examples and in particular the results of ranking by the proposed method and some common and existing methods for ranking fuzzy sets is compared to verify the advantage of the new approach. In particular, based on the results of comparison of our method with well known methods which are exist in the literature, we will see that against of most existing ranking approaches, our proposed approach can rank fuzzy numbers that have the same mode and symmetric spreads. In fact, the proposed method in this paper can effectively rank symmetric fuzzy numbers as well as the effective methods which are appeared in the literature. Moreover, unlike of most existing ranking approaches, our proposed approach can rank non-normal fuzzy sets. Finally, we emphasize that the concept of fuzzy ordering is one of key role in establishing the numerical algorithms in operations research such as fuzzy primal simplex algorithms, fuzzy dual simplex algorithms and as well as discussed in the works of Ebrahimnejad and Nasseri and coworkers , , , , ,  and .