共查询到20条相似文献，搜索用时 0 毫秒
1.
This paper focuses on the aggregation operations in the group decision‐making model based on the concept of majority opinion. The weighted‐selective aggregated majority‐OWA (WSAM‐OWA) operator is proposed as an extension of the SAM‐OWA operator, where the reliability of information sources is considered in the formulation. The WSAM‐OWA operator is generalized to the quantified WSAM‐OWA operator by including the concept of linguistic quantifier, mainly for the group fusion strategy. The QWSAM‐IOWA operator, with an ordering step, is introduced to the individual fusion strategy. The proposed aggregation operators are then implemented for the case of alternative scheme of heterogeneous group decision analysis. The heterogeneous group includes the consensus of experts with respect to each specific criterion. The exhaustive multicriteria group decision‐making model under the linguistic domain, which consists of two‐stage aggregation processes, is developed in order to fuse the experts’ judgments and to aggregate the criteria. The model provides greater flexibility when analyzing the decision alternatives with a tolerance that considers the majority of experts and the attitudinal character of experts. A selection of investment problem is given to demonstrate the applicability of the developed model. 相似文献
2.
This paper introduces a new type of behavioral ordered weighted averaging (BOWA) operator, to incorporate decision maker’s gains and losses behavior tendency into the information aggregation process. The main characteristic of this BOWA operator is that it considers behavioral weights and ordered weights in the same formulation. We further provide a calculation method of the behavioral weights, in which various psychological preferences of different attribute types of the decision maker can be expressed intuitively. In addition, we discuss some particular cases of BOWA operator and its main properties. Finally, a numerical example is used to illustrate the use of the proposed method. 相似文献
3.
作为直觉模糊集的推广形式,毕达哥拉斯模糊数能更好地刻画现实中的不确定性,此外在某些问题上,方案的属性之间往往具有优先关系,针对此类信息的集成问题,将毕达哥拉斯模糊数与优先集成算子相结合,提出了毕达哥拉斯模糊优先集成算子,包括毕达哥拉斯模糊优先加权平均算子和毕达哥拉斯模糊优先加权几何算子,并讨论了这些算子的性质。在此基础上,提出了毕达哥拉斯模糊优先集成算子的多属性决策方法,最后将其应用于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。 相似文献
4.
Ordered weighted average (OWA) operator provides a parameterized class of mean type operators between the minimum and the maximum. It is an important tool that can reflect the strategy of a decision maker for decision-making problems. In this study, the idea of obtaining the stress function from OWA weights has been put forward to generalize and characterize OWA weights. The main idea in this paper is mainly constructed on the basis that, generally, stress functions can be constructed using a mixture of constant and linear components. So, we can consider the stress function as a piecewise linear function. For obtaining stress functions as piecewise linear functions, we present a clustering-based approach for OWA weight generalization. This generalization is made using the DBSCAN algorithm as the learning method of a stress function associated with known OWA weights. In the learning process, the whole data set is divided into clusters, and then linear functions are obtained via a least squares estimator. 相似文献
5.
6.
This work discusses some new types of bipolar preferences and defines conservativeness of them. Then, the study defines parameterized fuzzy measures and proposes three methods to generate them. Based on given conservativeness preference and some special evaluation functions, we majorly discuss preference leveled evaluation functions method to construct fuzzy measures, allowing the generated fuzzy measures to effectively embody the preferences of decision makers. We also present some related properties such as different types of monotonicities, with three numerical instances showing the applicability of the proposed methods. 相似文献
7.
Byeong Seok Ahn Ronald R. Yager 《International Transactions in Operational Research》2014,21(2):247-262
A decision making under uncertainty (DMUU) prevails at the outset and often evolves into a decision making under partial uncertainty as information on the states of nature, for example, a probability distribution, is advanced. Many methods have emerged for solving the DMUU problems, which includes the classical decision criteria and the domain criterion. Yager (1988) introduced a new approach, the so‐called ordered weighted averaging (OWA) as a viable method for solving the DMUU problems. The OWA weights to be used in the aggregation are generated under the degree of optimism provided by a decision maker and then combined with the reordered payoffs to produce aggregated payoffs for each strategy. The reordering process, one of the characterizing features of the OWA method, enables us to perform various types of aggregations including maximax, maximin, and Hurwicz‐α index in conjunction with the generated weights. The OWA method obviously extends the Hurwicz approach by taking into account the tradeoffs among the entire payoffs while the Hurwicz approach considers a tradeoff only between the two extremes, the maximum and the minimum payoffs. In this paper, we examine the features of the OWA method in light of Milnor's set of requirements for reasonable decision criteria, thus providing a solid methodological foundation for the DMUU. The OWA method can also be used to solve a group DMUU problem by exploiting individual decision results in the situation when the use of a fuzzy majority is advocated. 相似文献
8.
We consider the multicriteria decision-making (MCDM) problems where there exists a prioritization relationship over the criteria. We introduce the concept of the priority degree. Then we give three kinds of prioritized aggregation operators based on the priority degrees: the prioritized averaging operator with the priority degrees, the prioritized scoring operator with the priority degrees, and the prioritized ordered weighted averaging operator with the priority degrees. Some desired properties of these prioritized aggregation operators are also investigated. The priority degree plays an important role in the prioritized MCDM problems. We also investigate how to select a proper priority degree according to the giving decision information. By using an illustrative example, we show that the prioritized aggregation operators based on the priority degrees provide the decision-makers more choices and they are more flexible in the process of decision-making. 相似文献
9.
Regular Increasing Monotone (RIM) quantifier and Ordered Weighted Function are important counterparts of discrete ordered weighted averaging operators. Some important characteristics such as entropy, Moment, and Step/Hurwicz degree have already been proposed and studied by several researchers. The main propose of this paper is to put the concepts of entropy, Moment, and Step/Hurwicz degree for RIM quantifier into a continuous environment. Some well‐defined representative families of RIM quantifiers are also presented. The metric spaces of RIM quantifiers are discussed. 相似文献
10.
We present the fuzzy generalized ordered weighted averaging (FGOWA) operator. It is an extension of the GOWA operator for uncertain situations where the available information is given in the form of fuzzy numbers. This generalization includes a wide range of mean operators such as the fuzzy average (FA), the fuzzy OWA (FOWA), and the fuzzy generalized mean (FGM). We also develop a further generalization by using quasi-arithmetic means that we call the quasi-FOWA operator. The article ends with an illustrative example where we apply the new approach in the selection of strategies. 相似文献
11.
Considering the distributed structural characteristics of arguments to be aggregated, we propose a new type of aggregation operator, called induced cluster-based ordered weighted averaging (OWA; abbreviated as cluster-IOWA) operator, in this article. The main characteristic of the cluster-IOWA operator is that the arguments are aggregated by local clusters, and the order-inducing variable is used for representing a particular characteristic with respect to a local cluster. The cluster-OWA operator is commutativity, idempotence, and boundedness. We then discuss two important issues with respect to the cluster-IOWA operator. The order-inducing variables are determined by considering the overall reliability of the local cluster. Based on this, the position weighting vector of the local clusters is designed by taking into account both the reliability measures and the decision maker's preference. Finally, a numerical example, regarding the performance evaluation of middle managers carried out by a group of participants, is developed to illustrate the application and validity of the cluster-IOWA operator. 相似文献
12.
Ronald R. Yager 《Applied Intelligence》2004,21(3):277-288
Our goal is to provide some tools, based on soft computing aggregation methods, useful in the two fundamental steps in case base reasoning, matching the target and the cases and fusing the information provided by the relevant cases. To aid in the first step we introduce a methodology for matching the target and cases which uses a hierarchical representation of the target object. We also introduce a method for fusing the information provided by relevant retrieved cases. This approach is based upon the nearest neighbor principle and uses the induced ordered weighted averaging operator as the basic aggregation operator. A procedure for learning the weights is described. 相似文献
13.
14.
Amar Oukil 《国际智能系统杂志》2019,34(5):947-965
Cross-efficiency (CE) evaluation is an extension of data envelopment analysis (DEA) used for fully ranking decision-making units (DMUs). The ranking process is normally performed on the matrix of CE scores. An ultimate efficiency score is computed for each DMU through an adequate amalgamation process. The preference ranking approach can be seen as an amalgamation technique based on the rank orders of the CE scores. In this paper, we review this approach by putting more emphasis on the aggregation aspect. We highlight the zero vote issue and we show that the latter has been neglected in the extant aggregation procedures. Consequently, we develop two ordered weighted averaging (OWA)-based procedures that attempt to meet effectively the requirements of an aggregation mechanism while exploiting the positive properties of the preference-ranking approach. The merits of the proposed procedures are evaluated on a sample of manufacturing systems by considering, for OWA weights generation, different OWA models with different orness degrees. 相似文献
15.
在多属性决策过程中经常会用到聚合算子,有序加权平均聚合(OWA)算子是最常用的聚合算子之一,通常用于聚合确切的数值.然而,现实世界部分信息的不确定性以及决策者对一些信息的模糊性,使得部分信息不能用确切的数值表示,从而导致OWA算子及其扩展算子向着多元化发展.对此,给出一种语言型混合有序加权平均聚合(LHOWA)算子,同时研究该算子所应具备的一些基本性质,并给出一种基于该算子的语言型信息聚合方法,用于多属性决策过程中模糊信息的聚合.最后,通过一个煤矿安全评价的算例对所提出方法的优越性进行了验证. 相似文献
16.
In the application of Z‐number, how to generate Z‐number is a significant and open issue. In this paper, we proposed a method of generating Z‐number based on the OWA weights using maximum entropy considering the attitude (preference) of the decision maker. Some numerical examples are used to illustrate the effectiveness of the proposed method. Results show that the attitude (preference) of the decision maker can give an optimal possibility distribution of the reliability for Z‐number using maximum entropy. 相似文献
17.
针对专家之间具有优先关系时直觉乘法偏好关系下群体共识决策问题,提出一种基于优先集成算子的直觉乘法偏好关系共识方法。为了有效集结专家偏好信息,提出直觉乘法优先加权平均(IMPWA)算子和直觉乘法优先加权几何(IMPWG)算子,并研究其相关性质;定义直觉乘法偏好关系的共识度和接近度概念,据此完成非共识偏好信息的识别和修正,构建一种迭代共识算法。案例表明该方法的可行性和有效性。 相似文献
18.
J.-R. Chang T.-H. Ho C.-H. Cheng A.-P. Chen 《Soft Computing - A Fusion of Foundations, Methodologies and Applications》2006,10(7):543-554
Obtaining relative weights in MCDM problems is a very important issue. The Ordered Weighted Averaging (OWA) aggregation operators
have been extensively adopted to assign the relative weights of numerous criteria. However, previous aggregation operators
(including OWA) are independent of aggregation situations. To solve the problem, this study proposes a new aggregation model
– dynamic fuzzy OWA based on situation model, which can modify the associated dynamic weight based on the aggregation situation
and can work like a “magnifying lens” to enlarge the most important attribute dependent on minimal information, or can obtain
equal attribute weights based on maximal information. Two examples are adopted in this paper for comparison and showing the
effects under different weights. 相似文献
19.
In this paper, we develop an interactive method for multiple attribute group decision making under fuzzy environment. The method can be used in situations where the information about attribute weights is partly known, the weights of decision makers are expressed in exact numerical values or triangular fuzzy numbers, and the attribute values are triangular fuzzy numbers. The method transforms fuzzy decision matrices into their expected decision matrices, constructs the corresponding normalized expected decision matrices by two simple formulas, and then aggregates these normalized expected decision matrices into a complex decision matrix. Moreover, the decision makers are asked to provide their preferences gradually in the course of interactions. By solving linear programming models, the method diminishes the given alternative set gradually, and finally finds the most preferred alternative. By using the method, the decision makers can provide and modify their preference information gradually in the process of decision making so as to make the decision result more reasonable. The method can not only reflect the importance of the given arguments and the ordered positions of the arguments, but also relieve the influence of unfair arguments on the decision result. Finally, a practical problem is used to illustrate the developed method. 相似文献
20.
Hamacher product is a t‐norm and Hamacher sum is a t‐conorm. They are good alternatives to algebraic product and algebraic sum, respectively. Nevertheless, it seems that most of the existing hesitant fuzzy aggregation operators are based on the algebraic operations. In this paper, we utilize Hamacher operations to develop some Pythagorean hesitant fuzzy aggregation operators: Pythagorean hesitant fuzzy Hamacher weighted average (PHFHWA) operator, Pythagorean hesitant fuzzy Hamacher weighted geometric (PHFHWG) operator, Pythagorean hesitant fuzzy Hamacher ordered weighted average (PHFHOWA) operator, Pythagorean hesitant fuzzy Hamacher ordered weighted geometric (PHFHOWG) operator, Pythagorean hesitant fuzzy Hamacher induced ordered weighted average (PHFHIOWA) operator, Pythagorean hesitant fuzzy Hamacher induced ordered weighted geometric (PHFHIOWG) operator, Pythagorean hesitant fuzzy Hamacher induced correlated aggregation operators, Pythagorean hesitant fuzzy Hamacher prioritized aggregation operators, and Pythagorean hesitant fuzzy Hamacher power aggregation operators. The special cases of these proposed operators are studied. Then, we have utilized these operators to develop some approaches to solve the Pythagorean hesitant fuzzy multiple attribute decision making problems. Finally, a practical example for green supplier selections in green supply chain management is given to verify the developed approach and to demonstrate its practicality and effectiveness. 相似文献