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。     

一种基于散度的模糊数排序方法

引用一种距离测度及模糊数的权重面积,建立了一种基于散度的模糊数排序指标.新的排序指标不仅引入了两个参考对象,即两个模糊数的极大和极小(M),(N),同时还考虑了模糊数本身的影响和决策者的决策态度.排序方法不仅计算简单、易于操作,而且还具有良好的性质.算例分析表明本文所提出的排序方法在一定程度上克服了现有方法的缺陷.     

基于质心和破碎度的模糊数排序

定义模糊数的破碎度概念,并且结合模糊数的质心给出模糊数排序的新指标。这种排序指标能够在一定程度上克服已有排序方法的某些缺陷,并且有效地实现各种模糊数的排序;文章最后通过算例分析,证明基于质心和破碎度的模糊数排序是完全可行的。     

梯形直觉模糊数排序方法及在多属性决策中应用

基于梯形直觉模糊数的值和模糊度两个特征,一类梯形直觉模糊数的排序方法被研究.首先,给出了梯形直觉模糊数的定义、运算法则和截集.其次,定义了梯形直觉模糊数关于隶属度和非隶属度的值和模糊度,以及值的指标和模糊度的指标.最后,给出了梯形直觉模糊数的排序方法,并将其应用到属性值为梯形直觉模糊数的多属性决策问题中.     

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.     

一种基于模糊数中心的模糊数排序方法

模糊数的排序法在决策及其它模糊应用系统的研究中起着非常重要的作用，众多学者提出了很多模糊数的排序方法，Cheng和Chu提出两种与模糊数中心有关的排序指标。但这两种方法都有明显的缺陷。本文构造了新的排序指标，能有效地实现各种模糊数的排序，最后用实例与前两种排序指标进行比较，体现出新指标的优越性。     

三角模糊数互补判断矩阵的一种排序方法及其在项目投资决策中的应用

引入Y ager第三指标将模糊数非模糊化,将专家判断矩阵中的三角模糊数转化成精确数,再利用精确数互补判断矩阵的排序方法进行排序.并通过实例说明了方法的可行性和有效性.     

证券组合的模糊规划模型

通过结构元方法定义了一种模糊数排序准则,利用模糊约束将Markowitz投资组舍模型转化为模糊线性规划模型,并利用模糊数来描述证券的期望收益率和风险损失率,建立模糊数模糊证券投资组合模型.最后,利用定义的模糊数排序准则把模糊数规划问题转化为经典的线性规划问题,然后再对该模型进行求解,并通过算例阐述了该方法的有效性.     

梯形模糊数的有序表示及中心平均排序方法

模糊数的排序在决策分析和优化问题中占有十分重要的地位,而一般模糊数均可近似分解为若干分片小梯形的叠加形式,故梯形模糊数的排序问题至关重要!本文首先引入等距分片方法对梯形模糊数实施纵向分割,进而获得梯形模糊数的有序表示。其次,依中心平均加权准则改进梯形模糊数的横向和纵向中心坐标公式,并提出新的指标排序准则。最后,通过实例分析考证了新的排序方法的有效性。     

三角模糊数互补判断矩阵排序的目标规划法

针对决策者以三角模糊数互补判断矩阵形式给出的多目标决策问题.给出三角模糊数加性一致性互补判断矩阵的判定定理.利用该定理基于最小偏差建立一个目标规划模型而解得三角模糊数互补判断矩阵的权重向量,从而使用三角模糊数排序公式对方案排序,提出了基于目标规划的三角模糊数互补判断矩阵排序法.最后,将模型与方法应用于项目投资决策中.     

Ranking generalized fuzzy numbers in fuzzy decision making based on the left and right transfer coefficients and areas

Although a number of recent studies have proposed ranking fuzzy numbers based on the deviation degree, most of them have exhibited several shortcomings associated with non-discriminative and counter-intuitive problems. In fact, none of the existing deviation degree methods has guaranteed consistencies between the ranking of fuzzy numbers and that of their images under all situations. They have also ignored decision maker’s attitude toward risk, which significantly influences final ranking result. To overcome the above-mentioned drawbacks, this study proposes a new approach for ranking fuzzy numbers that ensures full consideration for all information of fuzzy numbers. Accordingly, an overall ranking index is obtained by the integration of the information from the left and the right (LR) areas between fuzzy numbers, the centroid points of fuzzy numbers and the decision maker’s attitude toward risk. This new method is efficient for evaluating generalized fuzzy numbers and distinguishing symmetric fuzzy numbers. It also overcomes the shortcomings of the existing approaches based on deviation degree. Several numerical examples are provided to illustrate the superiority of the proposed approach. Lastly, a new fuzzy MCDM approach for generalized fuzzy numbers is proposed based on the proposed ranking approach and the concept of generalized fuzzy numbers. The proposed fuzzy MCDM approach does not require the normalization process and thus avoids the loss of information results from transforming generalized fuzzy numbers to normal form.     

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 .     

Comparing and ranking fuzzy numbers using ideal solutions

This paper presents a new approach for comparing and ranking fuzzy numbers in a simple manner in decision making under uncertainty. The concept of ideal solutions is sensibly used, and a distance-based similarity measure between fuzzy numbers is appropriately adopted for effectively determining the overall performance of each fuzzy number in comparing and ranking fuzzy numbers. As a result, all the available information characterizing a fuzzy number is fully utilized, and both the absolute position and the relative position of fuzzy numbers are adequately considered, resulted in consistent rankings being produced in comparing and ranking fuzzy numbers. The approach is computationally simple and its underlying concepts are logically sound and comprehensible. A comparative study is conducted on the benchmark cases in the literature that shows the proposed approach compares favorably with other approaches examined.     

基于区间粗糙直觉模糊数的多属性决策方法

研究了区间粗糙直觉模糊多属性决策。探讨了区间粗糙直觉模糊数的运算法则及其性质;定义了区间粗糙直觉模糊数的得分函数和精确函数,进而给出其排序方法;给出了区间粗糙直觉模糊数的变权算术平均和变权几何平均算子,并且建立了区间粗糙直觉模糊数的多属性决策模型;实例验证了所提出决策方法的有效性。     

Selecting IS personnel use fuzzy GDSS based on metric distance method

In this paper we propose a new approach to rank fuzzy numbers by metric distance. For showing our method is a good ranking method, we give two examples to compare with other methods. The paper also developes a computer-based group decision support system, FMCGDSS, to increase the recruiting productivity and to easily compare our method with other fuzzy number ranking methods. The FMCGDSS includes three ranking methods: intuition ranking, Lee and Li's fuzzy mean/spread and our metric distance method to help manager make better decision under fuzzy circumstance. The result indicates that the new method is coincident with the intuition ranking and the Lee and Li's fuzzy mean/spread method on each type weight.     

RM approach for ranking of generalized trapezoidal fuzzy numbers

Ranking of fuzzy numbers play an important role in decision making, optimization and forecasting etc. Fuzzy numbers must be ranked before an action is taken by a decision maker. In this paper, with the help of several counter examples, it is proved that ranking method proposed by Chen and Chen (Expert Systems with Applications 36 (3): 6833) is incorrect. The main aim of this paper is to propose a new approach for the ranking of generalized trapezoidal fuzzy numbers. The proposed ranking approach is based on rank and mode so it is named as an RM approach. The main advantage of the proposed approach is that the proposed approach provides the correct ordering of generalized and normal trapezoidal fuzzy numbers and also the proposed approach is very simple and easy to apply in the real life problems. It is shown that proposed ranking function satisfies all the reasonable properties of fuzzy quantities proposed by Wang and Kerre (Fuzzy Sets and Systems 118 (3): 375).     

Fuzzy number linear programming: A probabilistic approach (3)

In the real world there are many linear programming problems where all decision parameters are fuzzy numbers. Several approaches exist which use different ranking functions for solving these problems. Unfortunately when there exist alternative optimal solutions, usually with different fuzzy value of the objective function for these solutions, these methods can not specify a clear approach for choosing a solution. In this paper we propose a method to remove the above shortcoming in solving fuzzy number linear programming problems using the concept of expectation and variance as ranking functions.     

A simple approach to fuzzy critical path analysis in project networks

This paper develops a simple approach to critical path analysis in a project network with activity times being fuzzy numbers. The idea is based on the linear programming (LP) formulation and fuzzy number ranking method. The fuzzy critical path problem is formulated as an LP model with fuzzy coefficients of the objective function, and then on the basis of properties of linearity and additivity, the Yager’s ranking method is adopted to transform the fuzzy LP formulation to the crisp one which can be solved by using the conventional streamlined solution methods. Consequently, the critical path and total duration time can be obtained from the derived optimal solution. Moreover, in this paper we also define the most critical path and the relative path degree of criticality, which are theoretically sound and easy to use in practice. An example discussed in some previous studies illustrates that the proposed approach is able to find the most critical path, which is proved to be the same as that derived from an exhausted comparison of all possible paths. The proposed approach is very simple to apply, and it is not require knowing the explicit form of the membership functions of the fuzzy activity times.