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Guiwu Wei - One of the best experts on this subject based on the ideXlab platform.
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models for madm with 2 tuple linguistic neutrosophic dombi Bonferroni mean operators
IEEE Access, 2019Co-Authors: Guiwu Wei, Cun Wei, Jie WangAbstract:The Dombi t-norm and Dombi t-conorm operations, which have the advantages of describing decision making information flexibly by using the general parameter, can be valid to deal with actual multiple attribute decision making (MADM) problems. Motivated by the basic Bonferroni mean (BM) operator, some novel Dombi Bonferroni mean (DBM) operators and Dombi geometric Bonferroni mean (DGBM) operators are developed to aggregate 2-tuple linguistic neutrosophic information in our manuscript. Obviously, some precious properties and numerical examples are given to testify the effective and scientific of these operators. Finally, the green supplier selection in green supply chain management is taken as an example to verify the proposed methods.
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some single valued neutrosophic Bonferroni power aggregation operators in multiple attribute decision making
Journal of Ambient Intelligence and Humanized Computing, 2019Co-Authors: Guiwu Wei, Zuopeng ZhangAbstract:In this paper, we utilize power aggregation operators and Bonferroni mean to develop some single-valued neutrosophic Bonferroni power aggregation operators and single-valued neutrosophic geometric Bonferroni power aggregation operators. The prominent characteristics of these proposed operators are studied. Then, we use the SVNWBPM and SVNWGBPM operators to solve the single-valued neutrosophic multiple attribute decision making problems. Finally, a practical example for strategic suppliers’ selection is given to verify the developed approach and to demonstrate its practicality and effectiveness.
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Models for Green Supplier Selection with Some 2-Tuple Linguistic Neutrosophic Number Bonferroni Mean Operators
viXra, 2018Co-Authors: Jiewang, Guiwu Wei, YuweiAbstract:In this paper, we extend the Bonferroni mean (BM) operator, generalized Bonferroni mean (GBM) operator, dual generalized Bonferroni mean (DGBM) operator and dual generalized geometric Bonferroni mean (DGGBM) operator with 2-tuple linguistic neutrosophic numbers (2TLNNs) to propose 2-tuple linguistic neutrosophic numbers weighted Bonferroni mean (2TLNNWBM) operator, 2-tuple linguistic neutrosophic numbers weighted geometric Bonferroni mean (2TLNNWGBM) operator, generalized 2-tuple linguistic neutrosophic numbers weighted Bonferroni mean (G2TLNNWBM) operator, generalized 2-tuple linguistic neutrosophic numbers weighted geometric Bonferroni mean (G2TL NNWGBM) operator, dual generalized 2-tuple linguistic neutrosophic numbers weighted Bonferroni mean (DG2TLNNWBM) operator, and dual generalized 2-tuple linguistic neutrosophic numbers weighted geometric Bonferroni mean (DG2TLNNWGBM) operator.
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Models for Green Supplier Selection with Some 2-Tuple Linguistic Neutrosophic Number Bonferroni Mean Operators
Symmetry, 2018Co-Authors: Jie Wang, Guiwu Wei, Yu WeiAbstract:In this paper, we extend the Bonferroni mean (BM) operator, generalized Bonferroni mean (GBM) operator, dual generalized Bonferroni mean (DGBM) operator and dual generalized geometric Bonferroni mean (DGGBM) operator with 2-tuple linguistic neutrosophic numbers (2TLNNs) to propose 2-tuple linguistic neutrosophic numbers weighted Bonferroni mean (2TLNNWBM) operator, 2-tuple linguistic neutrosophic numbers weighted geometric Bonferroni mean (2TLNNWGBM) operator, generalized 2-tuple linguistic neutrosophic numbers weighted Bonferroni mean (G2TLNNWBM) operator, generalized 2-tuple linguistic neutrosophic numbers weighted geometric Bonferroni mean (G2TLNNWGBM) operator, dual generalized 2-tuple linguistic neutrosophic numbers weighted Bonferroni mean (DG2TLNNWBM) operator, and dual generalized 2-tuple linguistic neutrosophic numbers weighted geometric Bonferroni mean (DG2TLNNWGBM) operator. Then, the MADM methods are proposed with these operators. In the end, we utilize an applicable example for green supplier selection in green supply chain management to prove the proposed methods.
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picture uncertain linguistic Bonferroni mean operators and their application to multiple attribute decision making
Kybernetes, 2017Co-Authors: Guiwu WeiAbstract:Purpose The purpose of this paper is to develop some picture uncertain linguistic aggregation operators based on Bonferroni mean operators, which is combined with multiple attribute decision-making (MADM) and has applied the proposed MADM model for selecting the service outsourcing provider of communications industry under picture uncertain linguistic environment. Design/methodology/approach The service outsourcing provider selection problem of communications industry can be regarded as a typical MADM problem, in which the decision information should be aggregated. In this paper, the authors investigate the MADM problems with picture uncertain linguistic information based on traditional Bonferroni mean operator. Findings The results show that the proposed model can solve the MADM problems within the context of picture uncertain linguistic information, in which the attributes are existing interaction phenomenon. Some picture uncertain aggregation operators based on Bonferroni mean have been developed. A case study of service outsourcing provider selection problem of communications industry is provided to illustrate the effectiveness and feasibility of the proposed methods. The results show that the proposed methods are useful to aggregate the picture uncertain linguistic decision information in which the attributes are not independent so as to select the most suitable supplier. Research limitations/implications The proposed methods can solve the picture uncertain linguistic MADM problem, in which the interactions exist among the attributes. Therefore, it can be used to solve service outsourcing provider selection problems and other similar management decision problems. Practical implications This paper develops some picture uncertain aggregation operators based on Bonferroni mean and further presents two methods based on the proposed operators for solving MADM problems. It is useful to deal with multiple attribute interaction decision-making problems and suitable to solve a variety of management decision-making applications. Social implications It is useful to deal with multiple attribute interaction decision-making problems and suitable to solve a variety of management decision-making applications. Originality/value The paper investigates the MADM problems with picture uncertain linguistic information based on traditional Bonferroni mean operator and develops the picture uncertain linguistic Bonferroni mean operator and picture uncertain linguistic geometric Bonferroni mean operator, picture uncertain linguistic weighted Bonferroni mean operator and picture uncertain linguistic weighted geometric Bonferroni mean operator for aggregating the picture uncertain linguistic information, respectively. Finally, a numerical example concerning the service outsourcing provider selection problem of communications industry is provided to illustrate the effectiveness and feasibility of the proposed methods.
Peide Liu - One of the best experts on this subject based on the ideXlab platform.
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intuitionistic uncertain linguistic partitioned Bonferroni means and their application to multiple attribute decision making
International Journal of Systems Science, 2017Co-Authors: Zhengmin Liu, Peide LiuAbstract:The Bonferroni mean BM was originally introduced by Bonferroni and generalised by many other researchers due to its capacity to capture the interrelationship between input arguments. Nevertheless, in many situations, interrelationships do not always exist between all of the attributes. Attributes can be partitioned into several different categories and members of intra-partition are interrelated while no interrelationship exists between attributes of different partitions. In this paper, as complements to the existing generalisations of BM, we investigate the partitioned Bonferroni mean PBM under intuitionistic uncertain linguistic environments and develop two linguistic aggregation operators: intuitionistic uncertain linguistic partitioned Bonferroni mean IULPBM and its weighted form WIULPBM. Then, motivated by the ideal of geometric mean and PBM, we further present the partitioned geometric Bonferroni mean PGBM and develop two linguistic geometric aggregation operators: intuitionistic uncertain linguistic partitioned geometric Bonferroni mean IULPGBM and its weighted form WIULPGBM. Some properties and special cases of these proposed operators are also investigated and discussed in detail. Based on these operators, an approach for multiple attribute decision-making problems with intuitionistic uncertain linguistic information is developed. Finally, a practical example is presented to illustrate the developed approach and comparison analyses are conducted with other representative methods to verify the effectiveness and feasibility of the developed approach.
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multiattribute group decision making methods based on linguistic intuitionistic fuzzy power Bonferroni mean operators
Complexity, 2017Co-Authors: Peide Liu, Xi LiuAbstract:This paper focuses on the multiattribute group decision making problems with linguistic intuitionistic fuzzy information. Firstly the concept of linguistic intuitionistic fuzzy numbers (LIFNs) is introduced, and then based on the LIFNs, some new aggregation operators based on Bonferroni mean and power operator are proposed, such as linguistic intuitionistic fuzzy power Bonferroni mean (LIFPBM) operator, linguistic intuitionistic fuzzy weighted power Bonferroni mean (LIFWPBM) operator, linguistic intuitionistic fuzzy geometric power Bonferroni mean (LIFGPBM) operator, and linguistic intuitionistic fuzzy weighted geometric power Bonferroni mean (LIFWGPBM) operator. Then, some properties are proved such as idempotency, permutation, and boundedness. Besides, some special situations of the operators are explored. After that, an approach based of the LIFWGPBM and LIFWGPBM operators is proposed. Finally an example is used to illustrate the validity of the developed method.
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multiple attribute decision making method based on some normal neutrosophic Bonferroni mean operators
Neural Computing and Applications, 2017Co-Authors: Peide LiuAbstract:Normal neutrosophic numbers (NNNs) are a significant tool of describing the incompleteness, indeterminacy, and inconsistency of the decision-making information. In this paper, we firstly propose the definition and the properties of the NNNs, and the accuracy function, the score function, and the operational laws of the NNNs are developed. Then, some operators are presented, including the normal neutrosophic Bonferroni mean operator, the normal neutrosophic weighted Bonferroni mean (NNWBM) operator, the normal neutrosophic geometric Bonferroni mean operator, and the normal neutrosophic weighted geometric Bonferroni mean (NNWGBM) operator. We also study their properties and special cases. Further, we put forward a multiple attribute decision-making method which is based on the NNWBM and NNWGBM operators. Finally, an illustrative example is given to verify the practicality and validity of the proposed method.
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multi valued neutrosophic number Bonferroni mean operators with their applications in multiple attribute group decision making
International Journal of Information Technology and Decision Making, 2016Co-Authors: Peide Liu, Lili Zhang, Xi Liu, Peng WangAbstract:Bonferroni mean (BM) is a very useful aggregation operator, which can consider the correlations between the aggregated arguments and the multi-valued neutrosophic set can be much more convenient to denote the incomplete, indeterminate and inconsistent information, in this paper, we applied the Bonferroni mean to the multi-valued neutrosophic set, and proposed some Bonferroni mean operators of multi-valued neutrosophic numbers (MVNNs). First, we gave some operational laws and a comparison method of MVNNs, then we presented the weighted Bonferroni mean (WBM) operator and weighted geometric Bonferroni mean (WGBM) operator. Further, we proposed the multi-valued neutrosophic weighted Bonferroni mean (MVNWBM) operator and the multi-valued neutrosophic weighted geometric Bonferroni mean (MVNWGBM) operator and some properties of them are also investigated. Finally, the decision making methods are developed based on MVNWGBM operator and MVNWBM operator, and an example about investment selection is given to illustrate the applications of the developed methods and the influence of different parameter values on the decision-making results.
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multiple attribute decision making method based on single valued neutrosophic normalized weighted Bonferroni mean
Neural Computing and Applications, 2014Co-Authors: Peide Liu, Yumei WangAbstract:In this paper, we proposed a single-valued neutrosophic normalized weighted Bonferroni mean (SVNNWBM) operator on the basis of Bonferroni mean, the weighted Bonferroni mean (WBM), and the normalized WBM. Firstly, the definition, operational laws, characteristics, and comparing method of single-valued neutrosophic numbers (SVNNs) are introduced. Then, the SVNNWBM operator is developed, and some properties and special cases of this operator are analyzed. Furthermore, an approach is developed to solve the multiple attribute decision-making problems with SVNNs based on the SVNNWBM operator. Finally, an illustrative example is given to verify the developed approach and to demonstrate its practicality and effectiveness.
José M. Merigó - One of the best experts on this subject based on the ideXlab platform.
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Covariances with OWA operators and Bonferroni means
Soft Computing, 2020Co-Authors: Fabio Blanco-mesa, Ernesto León-castro, José M. MerigóAbstract:The covariance is a statistical technique that is widely used to measure the dispersion between two sets of elements. This work develops new covariance measures by using the ordered weighted average (OWA) operator and Bonferroni means. Thus, this work presents the Bonferroni covariance OWA operator. The main advantage of this approach is that the decision maker can underestimate or overestimate the covariance according to his or her attitudes. The article further generalizes this formulation by using generalized and quasi-arithmetic means to obtain a wide range of particular types of covariances, including the quadratic Bonferroni covariance and the cubic Bonferroni covariance. The paper also considers some other extensions by using induced aggregation operators in order to use complex reordering processes in the analysis. The work ends by studying the applicability of these new techniques to real-world problems and presents an illustrative example of a research and development (R&D) investment problem.
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Bonferroni Distances and Their Application in Group Decision Making
Cybernetics and Systems, 2019Co-Authors: Fabio Blanco-mesa, José M. MerigóAbstract:The aim of the paper is to develop new aggregation operators using Bonferroni means, ordered weighted averaging (OWA) operators and some distance measures. We introduce the Bonferroni-Hamming weigh...
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Bonferroni induced heavy operators in erm decision making a case on large companies in colombia
Applied Soft Computing, 2018Co-Authors: Fabio Blancomesa, José M. Merigó, Ernesto LeoncastroAbstract:Abstract Averaging aggregation operators analyse a set of data providing a summary of the results. This study focuses on the Bonferroni mean and the induced and heavy aggregation operators. The aim of the work is to present new aggregation operators that combine these concepts forming the Bonferroni induced heavy ordered weighted average and several particular formulations. This approach represents Bonferroni means with order inducing variables and with weighting vectors that can be higher than one. The paper also develops some extensions by using distance measures forming the Bonferroni induced heavy ordered weighted average distance and several particular cases. The study ends with an application in a large companies risk management problem in Colombia. The main advantage of this approach is that it provides a more general framework for analysing the data in scenarios where the numerical values may have some complexities that should be assessed with complex attitudinal characters.
Hongjun Wang - One of the best experts on this subject based on the ideXlab platform.
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uncertain linguistic Bonferroni mean operators and their application to multiple attribute decision making
Applied Mathematical Modelling, 2013Co-Authors: Guiwu Wei, Xiaofei Zhao, Rui Lin, Hongjun WangAbstract:Abstract In this paper, we investigate the multiple attribute decision making (MADM) problems with uncertain linguistic information. Motivated by the ideal of Bonferroni mean and geometric Bonferroni mean, we develop two aggregation techniques called the uncertain linguistic Bonferroni mean (ULBM) operator and the uncertain linguistic geometric Bonferroni mean (ULGBM) operator for aggregating the uncertain linguistic information. We study its properties and discuss its special cases. For the situations where the input arguments have different importance, we then define the uncertain linguistic weighted Bonferroni mean (ULWBM) operator and the uncertain linguistic weighted geometric Bonferroni mean (ULWGBM) operator, based on which we develop two procedures for multiple attribute decision making under the uncertain linguistic environments. Finally, a practical example is given to verify the developed approach and to demonstrate its practicality and effectiveness.
Jie Wang - One of the best experts on this subject based on the ideXlab platform.
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models for madm with 2 tuple linguistic neutrosophic dombi Bonferroni mean operators
IEEE Access, 2019Co-Authors: Guiwu Wei, Cun Wei, Jie WangAbstract:The Dombi t-norm and Dombi t-conorm operations, which have the advantages of describing decision making information flexibly by using the general parameter, can be valid to deal with actual multiple attribute decision making (MADM) problems. Motivated by the basic Bonferroni mean (BM) operator, some novel Dombi Bonferroni mean (DBM) operators and Dombi geometric Bonferroni mean (DGBM) operators are developed to aggregate 2-tuple linguistic neutrosophic information in our manuscript. Obviously, some precious properties and numerical examples are given to testify the effective and scientific of these operators. Finally, the green supplier selection in green supply chain management is taken as an example to verify the proposed methods.
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Models for Green Supplier Selection with Some 2-Tuple Linguistic Neutrosophic Number Bonferroni Mean Operators
Symmetry, 2018Co-Authors: Jie Wang, Guiwu Wei, Yu WeiAbstract:In this paper, we extend the Bonferroni mean (BM) operator, generalized Bonferroni mean (GBM) operator, dual generalized Bonferroni mean (DGBM) operator and dual generalized geometric Bonferroni mean (DGGBM) operator with 2-tuple linguistic neutrosophic numbers (2TLNNs) to propose 2-tuple linguistic neutrosophic numbers weighted Bonferroni mean (2TLNNWBM) operator, 2-tuple linguistic neutrosophic numbers weighted geometric Bonferroni mean (2TLNNWGBM) operator, generalized 2-tuple linguistic neutrosophic numbers weighted Bonferroni mean (G2TLNNWBM) operator, generalized 2-tuple linguistic neutrosophic numbers weighted geometric Bonferroni mean (G2TLNNWGBM) operator, dual generalized 2-tuple linguistic neutrosophic numbers weighted Bonferroni mean (DG2TLNNWBM) operator, and dual generalized 2-tuple linguistic neutrosophic numbers weighted geometric Bonferroni mean (DG2TLNNWGBM) operator. Then, the MADM methods are proposed with these operators. In the end, we utilize an applicable example for green supplier selection in green supply chain management to prove the proposed methods.