The Experts below are selected from a list of 42726 Experts worldwide ranked by ideXlab platform
Shyiming Chen - One of the best experts on this subject based on the ideXlab platform.
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multiattribute Decision making based on converted Decision matrices probability density functions and interval valued intuitionistic fuzzy values
Information Sciences, 2021Co-Authors: Kamal Kumar, Shyiming ChenAbstract:Abstract In this paper, we propose a new multiattribute Decision making method based on converted Decision matrices, probability density functions and interval-valued intuitionist fuzzy values. First, it obtains the converted Decision Matrix from the interval-valued intuitionistic fuzzy Decision Matrix given by the Decision maker. Then, it computes the standard deviations and the mean values of the intervals appear at each row of the converted Decision Matrix, respectively, by using probability density functions. Then, by using the mean values and the standard deviations of the alternatives and the converted Decision Matrix, it gets the z-score Decision Matrix. Afterwards, the optimal weights of the attributes are calculated from the interval-valued intuitionist fuzzy weights of the attributes. Finally, it computes the value of overall performance of each alternative by using the z-score Decision Matrix and the optimal weights of the attributes for ranking the alternatives. The proposed method can conquer the shortcomings of the existing methods for interval-valued intuitionistic fuzzy multiattribute Decision making.
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multiattribute Decision making using probability density functions and transformed Decision matrices in interval valued intuitionistic fuzzy environments
Information Sciences, 2021Co-Authors: Xinyao Zou, Shyiming Chen, Kangyun FanAbstract:Abstract In this paper, we propose a new method for multiattribute Decision making (MADM) using probability density functions and the transformed Decision Matrix (TDMx) of the Decision Matrix (DMx) offered by the Decision maker (DM) in interval-valued intuitionistic fuzzy (IVIF) environments. First, it gets the TDMx of the DMx given by the DM. Then, it computes the average value of the interval-valued intuitionistic fuzzy values (IVIFVs) appearing at each column of the TDMx. Then, it calculates the variance of each IVIFV in the TDMx. Then, it computes the standard deviation (SD) of the IVIFVs appearing at each column of the TDMx. Then, based on the obtained TDMx, the obtained average value of the IVIFVs appearing at each column of the TDMx, and the obtained SD of the IVIFVs appearing at each column of the TDMx, it gets the z-score DMx. Then, each attribute’s IVIF weight is transformed into a crisp weight between zero and one. Finally, each alternative’s weighted score is calculated using the z-score DMx and each attribute’s transformed crisp weight. The larger the weighted score of an alternative, the better the preference order (PO) of the alternative. It can overcome the shortcomings of the existing MADM methods.
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multiattribute Decision making based on u quadratic distribution of intervals and the transformed Matrix in interval valued intuitionistic fuzzy environments
Information Sciences, 2020Co-Authors: Shyiming Chen, Yunchen ChuAbstract:Abstract In this paper, we propose a new multiattribute Decision making (MADM) method based on the U-quadratic distribution of intervals and the transformed Matrix of the Decision Matrix given by the Decision maker in interval-valued intuitionistic fuzzy (IVIF) environments. First, it gets the transformed Matrix of the Decision Matrix. Then, it calculates the variances of the intervals appearing at each element of the obtained transformed Matrix, respectively. Then, it calculates the standard deviations of the intervals appearing in the elements of each column of the obtained transformed Matrix, respectively. Then, it calculates the middle points of the intervals appearing in the elements of each column of the obtained transformed Matrix, respectively. Then, it calculates the average values of the intervals appearing in the elements of each column of the obtained transformed Matrix, respectively. Then, it builds the z-score Matrix. Then, it calculates the transformed weight of the IVIF weight of each attribute. Finally, according to the obtained z-score Matrix and the obtained transformed weight of the IVIF weight of each attribute, it computes the weighted score of each alternative for ranking alternatives. The proposed MADM method can overcome the shortcomings of the existing MADM methods.
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multiattribute Decision making based on probability density functions and the variances and standard deviations of largest ranges of evaluating interval valued intuitionistic fuzzy values
Information Sciences, 2019Co-Authors: Shyiming Chen, Kangyun FanAbstract:Abstract In this paper, we propose a new multiattribute Decision making (MADM) method based on probability density functions (PDFs) and the variances and standard deviations of the largest ranges of evaluating interval-valued intuitionistic fuzzy values (IVIFVs). First, the proposed MADM method gets the largest range of each evaluating IVIFV in the Decision Matrix and calculates the average value of the largest ranges of each attribute. Then, it gets the PDF of the largest range of each evaluating IVIFV in the Decision Matrix, calculates the variance of each largest range and calculates the standard deviation of the largest ranges of each attribute. Then, it constructs the z-score Decision Matrix and gets the transformed weight of the interval-valued intuitionistic fuzzy (IVIF) weight of each attribute. Finally, it calculates the weighted score of each alternative based on the obtained z-score Decision Matrix and the transformed weight of the IVIF weight of each attribute. The larger the value of the weighted score, the better the preference order (PO) of the alternative. The proposed MADM method can overcome the drawbacks of the existing MADM methods.
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multiattribute Decision making based on non linear programming methodology with hyperbolic function and interval valued intuitionistic fuzzy values
Information Sciences, 2018Co-Authors: Shyiming Chen, Liwei KuoAbstract:Abstract In this paper, we present a novel multiattribute Decision making (MADM) method based on the non-linear programming (NLP) methodology with the hyperbolic tangent function and interval-valued intuitionistic fuzzy values (IVIFVs). Both the attributes’ weights and the Decision Matrix (DM) are expressed by IVIFVs. First, the proposed MADM method constructs the transformed Decision Matrix (TDM) of the DM. Then, it constructs a NLP model with the hyperbolic tangent function to get the optimal weights of the attributes. Then, it uses the interval-valued intuitionistic fuzzy weighted averaging (IVIFWA) operator to calculate the weighted evaluating IVIFVs of the alternatives. Finally, it uses Wang et al.’s method (2009) for comparing IVIFVs to obtain the preference order (PO) of the alternatives. It can conquer the shortcomings of Chen and Huang's MADM method (2017).
Shahryar Sorooshian - One of the best experts on this subject based on the ideXlab platform.
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upgrading current multi attribute Decision making with a 3 dimensional Decision Matrix for future based Decisions
MethodsX, 2021Co-Authors: Shahryar SorooshianAbstract:Abstract Two shortcomings of existing Multi-Attribute Decision-Making (MADM) approaches are presented in this paper. The problems were Decision makers' (experts') dynamic level of experience in certain areas, as well as the difficulties of forecasting the future with the existing MADM techniques. A solution is also proposed to overcome the issues. • Two critical shortcomings of existing multi-attribute Decision-making (MADM) approaches are presented with this work. • As a solution for the shortcomings, a 3-dimensional Decision Matrix is introduced.
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upgrading current multi attribute Decision making with a 3 dimensional Decision Matrix for future based Decisions
Social Science Research Network, 2021Co-Authors: Shahryar SorooshianAbstract:Two shortcomings of existing Multi-Attribute Decision-Making (MADM) approaches are presented in this paper. The problems were Decision makers' (experts') dynamic level of experience in certain areas, as well as the difficulties of forecasting the future with the existing MADM techniques. A solution is also proposed to overcome the issues.
Young Chel Kwun - One of the best experts on this subject based on the ideXlab platform.
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Extension of the VIKOR method for group Decision making with interval-valued intuitionistic fuzzy information
Fuzzy Optimization and Decision Making, 2011Co-Authors: Jin Han Park, Hyun Ju Cho, Young Chel KwunAbstract:The aim of this paper is to extend the VIKOR method for multiple attribute group Decision making in interval-valued intuitionistic fuzzy environment, in which all the preference information provided by the Decision-makers is presented as interval-valued intuitionistic fuzzy Decision matrices where each of the elements is characterized by interval-valued intuitionistic fuzzy number, and the information about attribute weights is partially known, which is an important research field in Decision science and operation research. First, we use the interval-valued intuitionistic fuzzy hybrid geometric operator to aggregate all individual interval-valued intuitionistic fuzzy Decision matrices provided by the Decision-makers into the collective interval-valued intuitionistic fuzzy Decision Matrix, and then we use the score function to calculate the score of each attribute value and construct the score Matrix of the collective interval-valued intuitionistic fuzzy Decision Matrix. From the score Matrix and the given attribute weight information, we establish an optimization model to determine the weights of attributes, and then determine the interval-valued intuitionistic positive-ideal solution and interval-valued intuitionistic negative-ideal solution. We use the different distances to calculate the particular measure of closeness of each alternative to the interval-valued intuitionistic positive-ideal solution. According to values of the particular measure, we rank the alternatives and then select the most desirable one(s). Finally, a numerical example is used to illustrate the applicability of the proposed approach.
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extension of the topsis method for Decision making problems under interval valued intuitionistic fuzzy environment
Applied Mathematical Modelling, 2011Co-Authors: Jin Han Park, Il Young Park, Young Chel KwunAbstract:TOPSIS is one of the well-known methods for multiple attribute Decision making (MADM). In this paper, we extend the TOPSIS method to solve multiple attribute group Decision making (MAGDM) problems in interval-valued intuitionistic fuzzy environment in which all the preference information provided by the Decision-makers is presented as interval-valued intuitionistic fuzzy Decision matrices where each of the elements is characterized by interval-valued intuitionistic fuzzy number (IVIFNs), and the information about attribute weights is partially known. First, we use the interval-valued intuitionistic fuzzy hybrid geometric (IIFHG) operator to aggregate all individual interval-valued intuitionistic fuzzy Decision matrices provided by the Decision-makers into the collective interval-valued intuitionistic fuzzy Decision Matrix, and then we use the score function to calculate the score of each attribute value and construct the score Matrix of the collective interval-valued intuitionistic fuzzy Decision Matrix. From the score Matrix and the given attribute weight information, we establish an optimization model to determine the weights of attributes, and construct the weighted collective interval-valued intuitionistic fuzzy Decision Matrix, and then determine the interval-valued intuitionistic positive-ideal solution and interval-valued intuitionistic negative-ideal solution. Based on different distance definitions, we calculate the relative closeness of each alternative to the interval-valued intuitionistic positive-ideal solution and rank the alternatives according to the relative closeness to the interval-valued intuitionistic positive-ideal solution and select the most desirable one(s). Finally, an example is used to illustrate the applicability of the proposed approach.
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correlation coefficient of interval valued intuitionistic fuzzy sets and its application to multiple attribute group Decision making problems
Mathematical and Computer Modelling, 2009Co-Authors: Dong Gun Park, Young Chel Kwun, Jin Han Park, Il Young ParkAbstract:In this paper, we investigate the group Decision making problems in which all the information provided by the Decision-makers is presented as interval-valued intuitionistic fuzzy Decision matrices where each of the elements is characterized by interval-valued intuitionistic fuzzy number (IVIFN), and the information about attribute weights is partially known. First, we use the interval-valued intuitionistic fuzzy hybrid geometric (IIFHG) operator to aggregate all individual interval-valued intuitionistic fuzzy Decision matrices provided by the Decision-makers into the collective interval-valued intuitionistic fuzzy Decision Matrix, and then we use the score function to calculate the score of each attribute value and construct the score Matrix of the collective interval-valued intuitionistic fuzzy Decision Matrix. From the score Matrix and the given attribute weight information, we establish an optimization model to determine the weights of attributes, and then we use the obtained attribute weights and the interval-valued intuitionistic fuzzy weighted geometric (IIFWG) operator to fuse the interval-valued intuitionistic fuzzy information in the collective interval-valued intuitionistic fuzzy Decision Matrix to get the overall interval-valued intuitionistic fuzzy values of alternatives, and then rank the alternatives according to the correlation coefficients between IVIFNs and select the most desirable one(s). Finally, a numerical example is used to illustrate the applicability of the proposed approach.
Kangyun Fan - One of the best experts on this subject based on the ideXlab platform.
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multiattribute Decision making using probability density functions and transformed Decision matrices in interval valued intuitionistic fuzzy environments
Information Sciences, 2021Co-Authors: Xinyao Zou, Shyiming Chen, Kangyun FanAbstract:Abstract In this paper, we propose a new method for multiattribute Decision making (MADM) using probability density functions and the transformed Decision Matrix (TDMx) of the Decision Matrix (DMx) offered by the Decision maker (DM) in interval-valued intuitionistic fuzzy (IVIF) environments. First, it gets the TDMx of the DMx given by the DM. Then, it computes the average value of the interval-valued intuitionistic fuzzy values (IVIFVs) appearing at each column of the TDMx. Then, it calculates the variance of each IVIFV in the TDMx. Then, it computes the standard deviation (SD) of the IVIFVs appearing at each column of the TDMx. Then, based on the obtained TDMx, the obtained average value of the IVIFVs appearing at each column of the TDMx, and the obtained SD of the IVIFVs appearing at each column of the TDMx, it gets the z-score DMx. Then, each attribute’s IVIF weight is transformed into a crisp weight between zero and one. Finally, each alternative’s weighted score is calculated using the z-score DMx and each attribute’s transformed crisp weight. The larger the weighted score of an alternative, the better the preference order (PO) of the alternative. It can overcome the shortcomings of the existing MADM methods.
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multiattribute Decision making based on probability density functions and the variances and standard deviations of largest ranges of evaluating interval valued intuitionistic fuzzy values
Information Sciences, 2019Co-Authors: Shyiming Chen, Kangyun FanAbstract:Abstract In this paper, we propose a new multiattribute Decision making (MADM) method based on probability density functions (PDFs) and the variances and standard deviations of the largest ranges of evaluating interval-valued intuitionistic fuzzy values (IVIFVs). First, the proposed MADM method gets the largest range of each evaluating IVIFV in the Decision Matrix and calculates the average value of the largest ranges of each attribute. Then, it gets the PDF of the largest range of each evaluating IVIFV in the Decision Matrix, calculates the variance of each largest range and calculates the standard deviation of the largest ranges of each attribute. Then, it constructs the z-score Decision Matrix and gets the transformed weight of the interval-valued intuitionistic fuzzy (IVIF) weight of each attribute. Finally, it calculates the weighted score of each alternative based on the obtained z-score Decision Matrix and the transformed weight of the IVIF weight of each attribute. The larger the value of the weighted score, the better the preference order (PO) of the alternative. The proposed MADM method can overcome the drawbacks of the existing MADM methods.
Jin Han Park - One of the best experts on this subject based on the ideXlab platform.
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Extension of the VIKOR method for group Decision making with interval-valued intuitionistic fuzzy information
Fuzzy Optimization and Decision Making, 2011Co-Authors: Jin Han Park, Hyun Ju Cho, Young Chel KwunAbstract:The aim of this paper is to extend the VIKOR method for multiple attribute group Decision making in interval-valued intuitionistic fuzzy environment, in which all the preference information provided by the Decision-makers is presented as interval-valued intuitionistic fuzzy Decision matrices where each of the elements is characterized by interval-valued intuitionistic fuzzy number, and the information about attribute weights is partially known, which is an important research field in Decision science and operation research. First, we use the interval-valued intuitionistic fuzzy hybrid geometric operator to aggregate all individual interval-valued intuitionistic fuzzy Decision matrices provided by the Decision-makers into the collective interval-valued intuitionistic fuzzy Decision Matrix, and then we use the score function to calculate the score of each attribute value and construct the score Matrix of the collective interval-valued intuitionistic fuzzy Decision Matrix. From the score Matrix and the given attribute weight information, we establish an optimization model to determine the weights of attributes, and then determine the interval-valued intuitionistic positive-ideal solution and interval-valued intuitionistic negative-ideal solution. We use the different distances to calculate the particular measure of closeness of each alternative to the interval-valued intuitionistic positive-ideal solution. According to values of the particular measure, we rank the alternatives and then select the most desirable one(s). Finally, a numerical example is used to illustrate the applicability of the proposed approach.
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extension of the topsis method for Decision making problems under interval valued intuitionistic fuzzy environment
Applied Mathematical Modelling, 2011Co-Authors: Jin Han Park, Il Young Park, Young Chel KwunAbstract:TOPSIS is one of the well-known methods for multiple attribute Decision making (MADM). In this paper, we extend the TOPSIS method to solve multiple attribute group Decision making (MAGDM) problems in interval-valued intuitionistic fuzzy environment in which all the preference information provided by the Decision-makers is presented as interval-valued intuitionistic fuzzy Decision matrices where each of the elements is characterized by interval-valued intuitionistic fuzzy number (IVIFNs), and the information about attribute weights is partially known. First, we use the interval-valued intuitionistic fuzzy hybrid geometric (IIFHG) operator to aggregate all individual interval-valued intuitionistic fuzzy Decision matrices provided by the Decision-makers into the collective interval-valued intuitionistic fuzzy Decision Matrix, and then we use the score function to calculate the score of each attribute value and construct the score Matrix of the collective interval-valued intuitionistic fuzzy Decision Matrix. From the score Matrix and the given attribute weight information, we establish an optimization model to determine the weights of attributes, and construct the weighted collective interval-valued intuitionistic fuzzy Decision Matrix, and then determine the interval-valued intuitionistic positive-ideal solution and interval-valued intuitionistic negative-ideal solution. Based on different distance definitions, we calculate the relative closeness of each alternative to the interval-valued intuitionistic positive-ideal solution and rank the alternatives according to the relative closeness to the interval-valued intuitionistic positive-ideal solution and select the most desirable one(s). Finally, an example is used to illustrate the applicability of the proposed approach.
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correlation coefficient of interval valued intuitionistic fuzzy sets and its application to multiple attribute group Decision making problems
Mathematical and Computer Modelling, 2009Co-Authors: Dong Gun Park, Young Chel Kwun, Jin Han Park, Il Young ParkAbstract:In this paper, we investigate the group Decision making problems in which all the information provided by the Decision-makers is presented as interval-valued intuitionistic fuzzy Decision matrices where each of the elements is characterized by interval-valued intuitionistic fuzzy number (IVIFN), and the information about attribute weights is partially known. First, we use the interval-valued intuitionistic fuzzy hybrid geometric (IIFHG) operator to aggregate all individual interval-valued intuitionistic fuzzy Decision matrices provided by the Decision-makers into the collective interval-valued intuitionistic fuzzy Decision Matrix, and then we use the score function to calculate the score of each attribute value and construct the score Matrix of the collective interval-valued intuitionistic fuzzy Decision Matrix. From the score Matrix and the given attribute weight information, we establish an optimization model to determine the weights of attributes, and then we use the obtained attribute weights and the interval-valued intuitionistic fuzzy weighted geometric (IIFWG) operator to fuse the interval-valued intuitionistic fuzzy information in the collective interval-valued intuitionistic fuzzy Decision Matrix to get the overall interval-valued intuitionistic fuzzy values of alternatives, and then rank the alternatives according to the correlation coefficients between IVIFNs and select the most desirable one(s). Finally, a numerical example is used to illustrate the applicability of the proposed approach.
