Pythagorean犹豫模糊交叉熵的多属性决策方法

针对决策信息为Pythagorean犹豫模糊数的多属性群决策问题,提出一种基于Pythagorean犹豫模糊交叉熵的多属性群决策方法。引入Pythagorean犹豫模糊交叉熵的概念。以Pythagorean犹豫模糊交叉熵作为决策信息差异程度的度量,提出专家权重和属性权重的确定模型。提出一种基于Pythagorean犹豫模糊熵的TOPSIS方法,并通过光伏电站选址案例说明了该方法的可行性和有效性。     

Pythagorean模糊对称交叉熵及加权投影的多属性决策

在Pythagorean模糊多属性决策问题中,以欧式距离等距离测度为基础计算各备选方案与正、负理想解的距离,可能产生与正理想解距离更近的待选方案却与负理想解的距离也更近,导致所得方案排序结果并不能真实反映各备选方案的优劣程度.为有效克服决策结果的逆序问题,提出满足对称性、有界性的Pythagorean模糊对称交叉熵,进...     

基于三角Pythagorean模糊集的多准则决策方法

Pythagorean模糊集在直觉模糊集的基础上扩大了适用范围,三角模糊数在决策过程中可以保留决策者较多的不确定信息.鉴于此,首先提出三角Pythagorean模糊集的定义及其欧氏距离表示;然后定义三角Pythagorean模糊加权平均(TPFWA)算子、广义三角Pythagorean模糊加权平均(GTPFWA)算子、三角Pythagorean模糊加权几何(TPFWG)算子和广义三角Pythagorean模糊加权几何(GTPFWG)算子,并对算子所满足的幂等性、有界性和单调性予以证明;最后通过一个医药代表选择的多准则决策问题和灵敏度分析验证所提出算子的合理性和有效性.     

加型Pythagorean模糊偏好关系的多属性决策方法

考虑Pythagorean模糊偏好关系的多属性决策问题,提出了加性Pythagorean模糊偏好关系的多属性决策方法.基于加性一致性Pythagorean模糊偏好关系提出一种新的Pythagorean模糊权重确定模型.给出了可接受加性一致性Pythagorean模糊偏好关系的定义,并针对不满足可接受加性一致性的Pyth...     

基于云模型的Pythagorean模糊语言多属性决策方法

针对专家给出的属性值为Pythagorean模糊语言且专家权重与属性权重均未知的多属性决策问题进行了研究,提出一种基于云模型的多属性决策方法。首先,根据Pythagorean模糊语言决策信息的距离熵计算得到属性权重;其次,计算决策矩阵间的距离从而得到各决策专家权重;再次,构建Pythagorean模糊云模型决策矩阵并利用专家权重和属性权重进行信息集结;最后,基于TOPSIS方法求取正、负理想解,依据理想解计算各方案贴近度并据此对各备选方案进行排序选择。案例分析表明,该方法优化了复杂环境下的决策,避免了决策信息的丢失,能够较好解决决策信息的不确定性和决策过程的随机性,具有一定的可行性和有效性。     

基于粗糙集的直觉模糊TOPSIS多属性决策方法

针对属性值为直觉模糊信息且属性权重完全未知的多属性决策问题,提出了一种基于粗糙集的直觉模糊TOPSIS多属性决策方法.首先给出了直觉模糊信息的正、负理想点的求法,根据属性值与理想点的贴近度和给定的阈值求得判断矩阵,再根据判断矩阵对属性约简,确定各属性的权重,最后依据TOPSIS思想计算各方案与理想点的加权贴近度,得到方案的排序,并通过算例的分析比较验证了此方法的有效性.     

区间Pythagorean犹豫模糊连续熵及灰色关联度的应用

区间Pythagorean犹豫模糊集,可以更加全面完整地描述决策者给出的决策结果,因此它是一个表示不确定现象的强有力的工具.针对模糊信息下的决策问题,提出了一种基于区间Pythagorean犹豫模糊连续熵的多属性决策方法.提出连续区间Pythagorean犹豫模糊有序加权平均(CIPHFOWA)算子,并提出了区间Pyt...     

区间Pythagorean模糊交互式多准则决策模型

研究了权重信息部分已知,评价信息为区间Pythagorean模糊数的交互式多准则决策问题。利用区间Pythagorean模糊数得分函数,计算各方案的加权得分向量在Pythagorean模糊正理想点和Pythagorean模糊负理想点上的投影,构建基于方案满意度最大的非线性规划准则权重确定模型。根据决策者的主观偏好并结合现有客观信息建立单目标规划模型,通过对方案满意度的给定与修正来实现交互决策。通过算例说明模型及方法的可行性和有效性。     

基于Vague集的多准则模糊决策方法

基于Vague集的模糊决策方法的提出，为决策系统提供了一个有用的工具．对模糊条件下的多准则决策问题，指出了采用Vague集进行多准则模糊决策的现有计分函数的不足，根据Vague集的性质提出了二种新的方案选择方法：记分函数法和加权记分函数法。对已有的Vgue集记分函数法进行了分析与联系，并通过例子阐明本文方法的有效性和优越性．     

区间直觉模糊连续交叉熵及其多属性决策方法

在区间直觉模糊（IVIF）环境下,利用连续有序加权平均（COWA）算子定义了一种新的区间直觉模糊数间的交叉熵,即区间直觉模糊连续交叉熵。依据提出的区间直觉模糊连续交叉熵定义了直觉模糊数间的连续交叉熵距离。基于TOPSIS的思想得到备选方案与理想方案的加权距离,并且计算备选方案与理想方案的相对贴近度,依据相对贴近度选择最优方案。其中,针对属性权重信息不完全确定条件下的决策问题,提出了以区间直觉模糊连续交叉熵最大为准则的规划模型;针对属性权重信息完全未知的情况,根据交叉熵理论确定属性权重向量。实验结果验证了新的决策方法的可行性和有效性。     

Pythagorean fuzzy TOPSIS for multicriteria group decision-making with unknown weight information through entropy measure

In this study, a new technique for order preference by similarity to ideal solution (TOPSIS)-based methodology is proposed to solve multicriteria group decision-making problems within Pythagorean fuzzy environment, where the information about weights of both the decision makers (DMs) and criteria are completely unknown. Initially, generalized distance measure for Pythagorean fuzzy sets (PFSs) is defined and used to initiate a new Pythagorean fuzzy entropy measure for computing weights of the criteria. In the decision-making process, at first, weights of DMs are computed using TOPSIS through the geometric distance model. Then, weights of the criteria are determined using the entropy weight model through the newly defined entropy measure for PFSs. Based on the evaluated criteria weights, TOPSIS is further applied to obtain the score value of alternatives corresponding to each decision matrix. Finally, the score values of the alternatives are aggregated with the calculated DMs’ weights to obtain the final ranking of the alternatives to avoid the loss of information, unlike other existing methods. Several numerical examples are considered, solved, and compared with the existing methods.     

The new extension of TOPSIS method for multiple criteria decision making with hesitant Pythagorean fuzzy sets

Pythagorean fuzzy sets (PFSs) as a new generalization of fuzzy sets (FSs) can handle uncertain information more flexibly in the process of decision making. In our real life, we also may encounter a hesitant fuzzy environment. In view of the effective tool of hesitant fuzzy sets (HFSs) for expressing the hesitant situation, we introduce HFSs into PFSs and extend the existing research work of PFSs. Concretely speaking, this paper considers that the membership degree and the non-membership degree of PFSs are expressed as hesitant fuzzy elements. First, we propose a new concept of hesitant Pythagorean fuzzy sets (HPFSs) by combining PFSs with HFSs. It provides a new semantic interpretation for our evaluation. Meanwhile, the properties and the operators of HPFSs are studied in detail. For the sake of application, we focus on investigating the normalization method and the distance measures of HPFSs in advance. Then, we explore the application of HPFSs to multi-criteria decision making (MCDM) by employing the technique for order preference by similarity to ideal solution (TOPSIS) method. A new extension of TOPSIS method is further designed in the context of MCDM with HPFSs. Finally, an example of the energy project selection is presented to elaborate on the performance of our approach.     

A ranking function based on principal‐value Pythagorean fuzzy set in multicriteria decision making

Multicriteria decision making (MCDM) has been attracting attention in recent years. There are two essential directions in the research territory, one direction is the research of representation of evaluation information and another is the construction of ranking function. In this paper, we consider some nonstandard fuzzy sets, intuitionistic, and interval‐valued fuzzy sets. Especially, the Pythagorean membership grade and Pythagorean fuzzy set receive much attention. Then, to reflect the importance of principal value, we shall propose the principal‐value Pythagorean fuzzy number (p‐PFN) and principal‐value Pythagorean fuzzy set. Furthermore, a novel ranking function is constructed to select the ideal alternative(s) based on p‐PFNs in MCDM. Finally, an investment strategy decision‐making problem is proposed to reveal the availability and practicability of the function under the new environment.     

毕达哥拉斯模糊Heronian算子的多属性决策方法

针对毕达哥拉斯模糊环境下的多属性决策问题中的信息集成问题,其中方案的属性间相互关联相互影响,提出了一种毕达哥拉斯模糊Heronian算子的多属性决策方法。首先将毕达哥拉斯模糊数与Heronian算子相结合,提出了毕达哥拉斯模糊Heronian算子和毕达哥拉斯模糊加权Heronian算子,并讨论了这些算子的性质,给出了相应的证明。在此基础上,提出了基于毕达哥拉斯模糊Heronian算子的多属性决策方法;最后将其应用到国内四家航空公司服务质量评价中,说明了该算子的有效性和可行性。     

Portfolio selection as a multicriteria group decision making in Pythagorean fuzzy environment with GRA and FAHP framework

The popularity of mutual funds, which are necessarily portfolios, has been drawing more attention from the people of India over the last three decades. In a mutual fund, one can invest his or her money in the securities of different sectors traded mainly in the stock exchange markets to get expected return bearing tolerable risks. A mutual fund other than the passive mutual funds is directed by an active fund manager. The performance of a mutual fund depends on the shares and securities of different companies it contains and the fund manager's performance also. Risk and return can be measured based on different criteria. Before investment, one should select such a mutual fund that can fulfill his or her anticipation as much as possible within the risk–return skeleton. Therefore the selection of a mutual fund is rigorously a multicriteria decision making. This paper has considered five open-ended, large-cap, direct, suspended sales mutual funds for the research work. In the first stage, we have shortlisted the more crucial criteria comparatively from a list of criteria with the help of the Fuzzy Analytic Hierarchy Process. In the second stage, we have applied the Pythagorean fuzzy Gray Relation Analysis approach to determine the weights of shortlisted criteria as well as the rank of those mutual funds. Finally, a comparison has been drawn between the present model and the Pythagorean fuzzy Interactive Multicriteria Decision-Making model, to show the effectiveness of the present model.     

毕达哥拉斯犹豫模糊集成算子及其决策应用

针对毕达哥拉斯犹豫模糊多属性决策中,集成算子的重要作用以及集成算子不完善的情况,较为系统地研究了毕达哥拉斯犹豫模糊集成算子。为此,在毕达哥拉斯模糊数的运算和运算法则基础上,定义了毕达哥拉斯犹豫模糊有序加权平均算子（PHFOWA）、广义有序加权平均算子（GPHFOWA）和混合平均算子（PHFHA）,以及毕达哥拉斯犹豫模糊有序加权几何平均算子（PHFOWG）、广义有序加权几何平均算子（GPHFOWG）和混合几何平均算子（PHFHG）,并结合数学归纳法,分别给出了它们的计算公式,讨论了它们的有界性、单调性和置换不变性等性质。建立了基于毕达哥拉斯犹豫模糊集成算子的多属性决策方法,并应用算例和相关方法比较说明了决策方法的可行性与有效性。     

Pythagorean fuzzy TODIM approach to multi-criteria decision making

Recently, the TODIM (an acronym in Portuguese for Interactive Multi-criteria Decision Making) approach, which can characterize the decision makers’ psychological behaviours under risk, has been introduced to handle multi-criteria decision making (MCDM) problems. Moreover, Pythagorean fuzzy set is an effective tool for depicting uncertainty of the MCDM problems. In this paper, based on the prospect theory, we first extend the TODIM approach to solve the MCDM problems with Pythagorean fuzzy information. Then, we conduct simulation tests to analyze how the risk attitudes of the decision makers exert the influence on the results of MCDM under uncertainty. Finally, a case study on selecting the governor of Asian Infrastructure Investment Bank is made to show the applicability of the proposed approach.     

On Pythagorean fuzzy decision making using soft likelihood functions

Multicriteria decision making (MCDM) is to select the optimal candidate which has the best quality from a finite set of alternatives with multiple criteria. One important component of MCDM is to express the evaluation information, and the other one is to aggregate the evaluation results associated with different criteria. For the former, Pythagorean fuzzy set (PFS) is employed to represent uncertain information in this paper, and for the latter, the soft likelihood function developed by Yager is used. To address MCDM issues from a new perspective, the likelihood function of PFS is first proposed in this study and, to improve some of its limitations, the ordered weighted averaging (OWA)-based soft likelihood function is defined, which introduces the attitudinal characteristic to identify decision makers' subjective preferences. In addition, the defined soft likelihood function of PFS is extended by weighted OWA operator considering the importance weight of the argument. Several illustrative cases are provided based on the presented (weighted) OWA-based soft likelihood functions in Pythagorean fuzzy environment for MCDM problem.     

A novel aggregation method for Pythagorean fuzzy multiple attribute group decision making

As an extension of fuzzy set, a Pythagorean fuzzy set has recently been developed to model imprecise and ambiguous information in practical group decision‐making problems. The aim of this paper is to introduce a novel aggregation method for the Pythagorean fuzzy set and analyze possibilities for its application in solving multiple attribute decision‐making problems. More specifically, a new Pythagorean fuzzy aggregation operator called the Pythagorean fuzzy induced ordered weighted averaging‐weighted average (PFIOWAWA) operator is developed. This operator inherits main characteristics of both ordered weighted average operator and induced ordered weighted average to aggregate the Pythagorean fuzzy information. Some of main properties and particular cases of the PFIOWAWA operator are studied. A method based on the proposed operator for multiple attribute group decision making is developed. Finally, we present a numerical example of selection of research and development projects to illustrate applicability of the new approach in a multiple attribute group decision‐making problem.