scholarly journals Evaluation and Selection of Manufacturing Suppliers in B2B E-Commerce Environment

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-8 ◽  
Author(s):  
Quan Zhang ◽  
Zhen Guo ◽  
Feiyu Man ◽  
Jiyun Ma
Keyword(s):  
Correlation Coefficients ◽  
Multiple Attribute Decision Making ◽  
Entropy Method ◽  
Linguistic Terms ◽  
Decision Matrix ◽  
Interval Numbers ◽  
Attribute Weights ◽  
Multiple Attribute ◽  
Available Information ◽  
Selection Of

The evaluation and selection of manufacturing suppliers in B2B e-commerce environment is summed up as a multiple-attribute decision-making problem. In B2B E-commerce environment, some performance indicators of manufacturing suppliers present uncertainty and could not be expressed with precise numeric values. Linguistic terms, preference orderings, or interval numbers are commonly used to express the performances of the suppliers accurately instead of crisp values when the available information is uncertain or incomplete. This paper proposes an approach to the selection of manufacturing suppliers in B2B E-commerce environment, where the attribute values in decision matrix are expressed with linguistic terms, preference orderings, and interval numbers. Firstly, the hybrid decision matrix is normalized by calculating the grey correlation coefficients of attribute values with the ideal values of attributes. Secondly, a deviation maximization model is proposed to determine the attribute weights, which is combined with those derived from the entropy method. Thirdly, the overall values of suppliers are calculated and their rankings are obtained. Finally, an example is used to illustrate the proposed approach.

scholarly journals An Approach to Determining Attribute Weights Based on Integrating Preference Information on Attributes with Decision Matrix

2018 ◽  
Vol 2018 ◽  
pp. 1-8
Author(s):  
Quan Zhang ◽  
HongWei Xiu
Keyword(s):  
Inequality Constraints ◽  
Multiple Attribute Decision Making ◽  
Entropy Method ◽  
Linguistic Terms ◽  
Decision Matrix ◽  
Interval Numbers ◽  
Preference Information ◽  
Attribute Weights ◽  
Multiple Attribute ◽  
Set Up

The interval multiple attribute decision-making problems are studied in this paper, where the preference information on attributes is expressed with preference orderings, linguistic terms, interval numbers, and inequality constraints among partial attribute weights. An approach is proposed to determine the attribute weights based on the preference information on attributes and the interval decision matrix. Firstly, preference orderings, linguistic terms, and interval numbers are normalized and aggregated into the group opinions, based on which an optimization model is set up to calculate the subjective attribute weights by including inequality constraints among partial attribute weights in the model. Then, based on the interval decision matrix, the entropy method is adopted to calculate the objective attribute weights, which is integrated with the subjective weights so that both the subjective preference information and the objective information in the decision matrix are reflected. Finally, an example is used to illustrate the proposed approach.

Entropy method of determining the attribute weights of interval numbers based on relative superiority

2021 ◽  
pp. 1-8
Author(s):  
Lin Li ◽  
Tiejun Ci ◽  
Xiaoyu Yang ◽  
Heng Du ◽  
Haocan Ma ◽  
...  
Keyword(s):  
Decision Making ◽  
Basic Principle ◽  
Multiple Attribute Decision Making ◽  
Entropy Method ◽  
Interval Numbers ◽  
Attribute Weights ◽  
Multiple Attribute ◽  
Multi Attribute Decision Making ◽  
Judgment Matrix ◽  
Train Of Thought

In view of the multi-attribute decision making problems which the attribute values are in the forms of interval numbers, the paper presents an entropy method to obtain the attribute weights using the relative superiority concept. Firstly, the concept of this kind of problem is explained; Then in the light of the basic principle of the traditional entropy value method and train of thought, it given the calculation steps of weights using the relative superiority about the attribute value is interval number multiple attribute decision making problems. Its core is that relative superiority judgment matrix is obtained by comparing with two sets of interval numbers under the same indicator, which the group of interval numbers is equivalently mapped to the exact value form with the merits of relationship, then the weights of each indicator are calculated. Finally, the method is illustrated by giving an example.

scholarly journals A Heterogeneous Multiattribute Group Decision-Making Method Based on Intuitionistic Triangular Fuzzy Information

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-18 ◽  
Author(s):  
Jun Xu ◽  
Jiu-Ying Dong ◽  
Shu-Ping Wan ◽  
De-Yan Yang ◽  
Yi-Feng Zeng
Keyword(s):  
Decision Making ◽  
Group Decision Making ◽  
Fuzzy Numbers ◽  
Group Decision ◽  
Entropy Method ◽  
Triangular Fuzzy Numbers ◽  
Decision Matrix ◽  
Interval Numbers ◽  
Attribute Weights ◽  
Relative Closeness

How to aggregate decision information in heterogeneous multiattribute group decision making (HMAGDM) is vital. The aim of this paper is to develop an approach to aggregating decision data into intuitionistic triangular fuzzy numbers (ITFNs) for heterogeneous MAGDM problems with real numbers (RNs), interval numbers (INs), triangular fuzzy numbers (TFNs), trapezoidal fuzzy numbers (TrFNs), and triangular intuitionistic fuzzy number (TIFNs). Using the relative closeness of technique for order preference by similarity to ideal solution (TOPSIS) and geometry entropy method, we first present a general approach to aggregating heterogeneous information into ITFNs, which takes the group consistency of experts into account. Based on the collective intuitionistic triangular fuzzy decision matrix and extended TOPSIS, a multiple objective mathematical program is constructed to determine the optimal attribute weights. Subsequently, a new method to solve HMAGDM problems is presented based on the aforementioned discussion. A trustworthy service selection example is provided to verify the practicality and effectiveness of the proposed method.

A COMBINATION WEIGHTING ALGORITHM USING RELATIVE ENTROPY FOR DOCUMENT CLUSTERING

2014 ◽  
Vol 28 (03) ◽  
pp. 1453002 ◽  
Author(s):  
BO JI ◽  
YANGDONG YE ◽  
YU XIAO
Keyword(s):  
Relative Entropy ◽  
Document Clustering ◽  
Multiple Attribute Decision Making ◽  
Entropy Method ◽  
Data Object ◽  
Information Bottleneck ◽  
Subjective Weight ◽  
Multiple Attribute ◽  
Sequential Information ◽  
Relative Entropy Method

This paper proposes a combination weighting algorithm using relative entropy for document clustering. Combination weighting is widely used in multiple attribute decision making (MADM) problem. However, there exist two difficulties to hinder the applications of combination weighting on document clustering. First, combination weighting is based on the integration of subjective weighting and objective weighting. However, there are so many attributes in documents that the subjective weights which rely on manual annotation by experts are impracticable. Secondly, a document data object might contain hundreds or even thousands of features. It is an extremely time-consuming task to calculate the combination weights. To address the issues, we suggest to simplify the combination weighting by not distinguishing subjective weight and objective weight. Meanwhile, we choose relative entropy method to reduce running time. In our algorithm, we obtain a combination weight set with 14 combination forms. The experiments on real document data show that both on the AC/PR/RE measures and the mutual information (MI) measure, the proposed CWRE-sIB algorithm is superior to the original sequential information bottleneck (sIB) algorithm and a series of weighting-sIB algorithms, which are built by applying a single weighting scheme to the original sIB algorithm.

scholarly journals Models for Multiple Attribute Decision Making with Some 2-Tuple Linguistic Pythagorean Fuzzy Hamy Mean Operators

Mathematics ◽  
2018 ◽  
Vol 6 (11) ◽  
pp. 236 ◽  
Author(s):  
Xiumei Deng ◽  
Jie Wang ◽  
Guiwu Wei ◽  
Mao Lu
Keyword(s):  
Decision Making ◽  
Supplier Selection ◽  
Fuzzy Numbers ◽  
Special Kind ◽  
Multiple Attribute Decision Making ◽  
Linguistic Terms ◽  
Multiple Integration ◽  
Multiple Attribute ◽  
Pythagorean Fuzzy Numbers ◽  
Actual Decision

The Hamy mean (HM) operator, as a useful aggregation tool, can capture the correlation between multiple integration parameters, and the 2-tuple linguistic Pythagorean fuzzy numbers (2TLPFNs) are a special kind of Pythagorean fuzzy numbers (PFNs), which can easily describe the fuzziness in actual decision making by 2-tuple linguistic terms (2TLTs). In this paper, to consider both Hamy mean (HM) operator and 2TLPFNs, we combine the HM operator, weighted HM (WHM) operator, dual HM (DHM) operator, and dual WHM (DWHM) operator with 2TLPFNs to propose the 2-tuple linguistic Pythagorean fuzzy HM (2TLPFHM) operator, 2-tuple linguistic Pythagorean fuzzy WHM (2TLPFWHM) operator, 2-tuple linguistic Pythagorean fuzzy DHM (2TLPFDHM) operator and 2-tuple linguistic Pythagorean fuzzy DWHM (2TLPFDWHM) operator. Then some multiple attribute decision making (MADM) procedures are developed based on these operators. At last, an applicable example for green supplier selection is given.

Material Selection Using a Novel Multiple Attribute Decision Making Method

2012 ◽  
pp. 252-266
Author(s):  
R. V. Rao ◽  
B. K. Patel
Keyword(s):  
Decision Making ◽  
Fuzzy Logic ◽  
Decision Maker ◽  
Design Process ◽  
Material Selection ◽  
Multiple Attribute Decision Making ◽  
Decision Makers ◽  
Selection Problem ◽  
Multiple Attribute ◽  
Selection Of

Selection of a most appropriate material is a very important task in design process of every product. There is a need for simple, systematic, and logical methods or mathematical tools to guide decision makers in considering a number of selection attributes and their interrelations and in making right decisions. This paper proposes a novel multiple attribute decision making (MADM) method for solving the material selection problem. The method considers the objective weights of importance of the attributes as well as the subjective preferences of the decision maker to decide the integrated weights of importance of the attributes. Furthermore, the method uses fuzzy logic to convert the qualitative attributes into the quantitative attributes. Two examples are presented to illustrate the potential of the proposed method.

Improved Correlation Coefficients of Quadripartitioned Single-Valued Neutrosophic Sets and Interval-Quadripartitioned Neutrosophic Sets

2020 ◽  
pp. 331-363
Author(s):  
Mohanasundari M. ◽  
Mohana K.
Keyword(s):  
Decision Making ◽  
Correlation Coefficient ◽  
Correlation Coefficients ◽  
Multiple Attribute Decision Making ◽  
Neutrosophic Sets ◽  
Statistical Measures ◽  
Multiple Attribute

A correlation coefficient is one of the statistical measures that helps to find the degree of changes to the value of one variable predict change to the value of another. Quadripartitioned single valued neutrosophic sets is an improvization of Wang's single valued neutrosophic sets. This chapter deals the improved correlation coefficients of quadripartitioned single valued neutrosophic sets, interval quadripartitioned neutrosophic sets, and investigates its properties. And this concept is also applied in multiple-attribute decision-making methods with quadripartitioned single valued neutrosophic environment and interval quadripartitioned neutrosophic environment. Finally an illustrated example is given in the proposed method to the multiple-attribute decision-making problems.

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