scholarly journals Synergies of Text Mining and Multiple Attribute Decision Making: A Criteria Selection and Weighting System in a Prospective MADM Outline

Symmetry ◽  
2020 ◽  
Vol 12 (5) ◽  
pp. 868
Author(s):  
Sarfaraz Hashemkhani Zolfani ◽  
Arman Derakhti
Keyword(s):  
Decision Making ◽  
Text Mining ◽  
Latent Semantic Analysis ◽  
Semantic Analysis ◽  
Multiple Attribute Decision Making ◽  
General Concept ◽  
Futures Studies ◽  
Weighting System ◽  
Multiple Attribute ◽  
Multi Attribute Decision Making

In this study, a new way of criteria selection and a weighting system will be presented in a multi-disciplinary framework. Weighting criteria in Multi-Attribute Decision Making (MADM) has been developing as the most attractive section in the field. Although many ideas have been developed during the last decades, there is no such great diversity that can be mentioned in the literature. This study is looking from outside the box and is presenting something totally new by using big data and text mining in a Prospective MADM outline. PMADM is a hybrid interconnected concept between the Futures Studies and MADM fields. Text mining, which is known as a useful tool in Futures Studies, is applied to create a widespread pilot system for weighting and criteria selection in the PMADM outline. Latent Semantic Analysis (LSA), as an influential method inside the general concept of text mining, is applied to show how a data warehouse’s output, which in this case is Scopus, can reach the final criteria selection and weighting of the criteria.

Set Pair Analysis Method of Containing Target Constraint Mixed Interval Multi-Attribute Decision-Making

2012 ◽  
Vol 226-228 ◽  
pp. 2222-2226 ◽  
Author(s):  
Wen Sheng Lü ◽  
Bin Zhang
Keyword(s):  
Decision Making ◽  
Multiple Attribute Decision Making ◽  
Set Pair Analysis ◽  
Analysis Theory ◽  
Multiple Attribute ◽  
Complex Decision Making ◽  
Multi Attribute Decision Making ◽  
Interval Type ◽  
Pair Analysis ◽  
Attribute Value

In view of target attribute value for different sector number, moreover, also attaches a target constraint condition kind of mix sector multi-attribute decision making question, this paper presents set pair analysis decision-making method. Firstly this paper puts forward three typical interval type attribute value representation; Then using set pair analysis theory, the interval type attribute value unified convert the correlate form, Finally has given complex decision-making criterion function, which collected Conformity degree criteria and Criteria for membership degree. Through the construction plan changes decision-making example analysis shows that this method is a simple and effective method for solving multiple attribute decision making.

scholarly journals A Novel q-Rung Dual Hesitant Fuzzy Multi-Attribute Decision-Making Method Based on Entropy Weights

Entropy ◽  
2021 ◽  
Vol 23 (10) ◽  
pp. 1322
Author(s):  
Yaqing Kou ◽  
Xue Feng ◽  
Jun Wang
Keyword(s):  
Decision Making ◽  
Multiple Attribute Decision Making ◽  
Aggregation Operators ◽  
Entropy Measure ◽  
Fuzzy Environment ◽  
Multiple Attribute ◽  
Special Cases ◽  
Multi Attribute Decision Making ◽  
Novel Method ◽  
And Performance

In this paper, a new multiple attribute decision-making (MADM) method under q-rung dual hesitant fuzzy environment from the perspective of aggregation operators is proposed. First, some aggregation operators are proposed for fusing q-rung dual hesitant fuzzy sets (q-RDHFSs). Afterwards, we present properties and some desirable special cases of the new operators. Second, a new entropy measure for q-RDHFSs is developed, which defines a method to calculate the weight information of aggregated q-rung dual hesitant fuzzy elements. Third, a novel MADM method is introduced to deal with decision-making problems under q-RDHFSs environment, wherein weight information is completely unknown. Finally, we present numerical example to show the effectiveness and performance of the new method. Additionally, comparative analysis is conducted to prove the superiorities of our new MADM method. This study mainly contributes to a novel method, which can help decision makes select optimal alternatives when dealing with practical MADM problems.

scholarly journals Possibility Neutrosophic Cubic Sets and Their Application to Multiple Attribute Decision Making

Symmetry ◽  
2020 ◽  
Vol 12 (2) ◽  
pp. 269 ◽  
Author(s):  
Huiling Xue ◽  
Xiaotong Yang ◽  
Chunfang Chen
Keyword(s):  
Decision Making ◽  
Binary Operation ◽  
Score Function ◽  
Multiple Attribute Decision Making ◽  
Membership Degree ◽  
Neutrosophic Sets ◽  
Important Indicator ◽  
Multiple Attribute ◽  
Decision Making Problem ◽  
Multi Attribute Decision Making

The neutrosophic cubic sets are an extension of the cubic sets to the neutrosophic sets. It contains three variables, which respectively represent the membership degree, non-membership degree and uncertainty of the element to the set. The score function is an important indicator in the multi-attribute decision-making problem. In this paper, we consider the possibility that an element belongs to a set and put forward the concept of possibility neutrosophic cubic sets. On this basis, we introduce some related concepts and give the binary operation of possibility neutrosophic cubic sets and use specific examples to supplement the corresponding definition. Meanwhile, a decision-making method based on the score function of possibility neutrosophic cubic sets is proposed and a numerical example is given to illustrate the effectiveness of the proposed method.

scholarly journals Multiple Attribute Decision Making Based on Linguistic Generalized Weighted Heronian Mean

Symmetry ◽  
2021 ◽  
Vol 13 (7) ◽  
pp. 1191
Author(s):  
Ximei Hu ◽  
Shuxia Yang ◽  
Ya-Ru Zhu
Keyword(s):  
Decision Making ◽  
Multiple Attribute Decision Making ◽  
Multiple Attribute ◽  
Theoretical Significance ◽  
Multi Attribute Decision Making ◽  
Parameter Symmetry ◽  
Heronian Mean

In actual multiple attribute decision making, people often use language to evaluate attributes of the object, and sometimes there are associations between the attributes. Therefore, the study of multiple attribute decision making with language as attributes and associations between attributes is of great theoretical significance and practical value. The Heronian mean is not only an operator which reflects the associations between attributes, but also has excellent properties, including idempotency, monotonicity, boundedness, parameter symmetry, and alternate symmetry. In this paper, firstly a new linguistic generalized weighted Heronian mean (LGWHM) was provided, and its properties including idempotency, monotonicity, boundedness, and limit were studied. Then, a new three-parameter linguistic generalized weighted Heronian mean (TPLGWHM) and its idempotency, monotonicity, and boundedness properties were proposed. Finally, multi-attribute decision making methods based on the new linguistic generalized weighted Heronian mean were given, and an example was analyzed and compared with other methods.

scholarly journals Improved correlation coe_cients of quadripartitioned neutrosophic pythagorean sets for madm

2021 ◽  
Vol 5 (1) ◽  
Author(s):  
Radha R ◽  
Stanis Arul Mary A
Keyword(s):  
Decision Making ◽  
Correlation Coefficient ◽  
Multiple Attribute Decision Making ◽  
Neutrosophic Sets ◽  
Multiple Attribute ◽  
Multi Attribute Decision Making

Quadripartitioned single valued Neutrosophic Pythagorean Sets is a improvisation of Wang's single valued neutrosophic sets. In this paper we have studied. The improved correlation coefficient of Quadripartitioned Neutrosophic Pythagorean Sets and investigate it's properties. Further,we have applied the concept of multi attribute decision making methods with quadripartitioned neutrosophic pythagorean environment. Finally we illustrated an example with above proposed method to the multiple attribute decision making problems

scholarly journals Bipolar Complex Fuzzy Hamacher Aggregation Operators and Their Applications in Multi-Attribute Decision Making

Mathematics ◽  
2021 ◽  
Vol 10 (1) ◽  
pp. 23
Author(s):  
Tahir Mahmood ◽  
Ubaid ur Rehman ◽  
Jabbar Ahmmad ◽  
Gustavo Santos-García
Keyword(s):  
Decision Making ◽  
Weighted Average ◽  
Multiple Attribute Decision Making ◽  
Aggregation Operators ◽  
Numerical Example ◽  
Multiple Attribute ◽  
Multi Attribute Decision Making ◽  
Ordered Weighted Average

On the basis of Hamacher operations, in this manuscript, we interpret bipolar complex fuzzy Hamacher weighted average (BCFHWA) operator, bipolar complex fuzzy Hamacher ordered weighted average (BCFHOWA) operator, bipolar complex fuzzy Hamacher hybrid average (BCFHHA) operator, bipolar complex fuzzy Hamacher weighted geometric (BCFHWG) operator, bipolar complex fuzzy Hamacher ordered weighted geometric (BCFHOWG) operator, and bipolar complex fuzzy Hamacher hybrid geometric (BCFHHG) operator. We present the features and particular cases of the above-mentioned operators. Subsequently, we use these operators for methods that can resolve bipolar complex fuzzy multiple attribute decision making (MADM) issues. We provide a numerical example to authenticate the interpreted methods. In the end, we compare our approach with existing methods in order to show its effectiveness and practicality.

MULTI-ATTRIBUTE DECISION MAKING BASED ON LABEL SEMANTICS

2008 ◽  
Vol 16 (supp02) ◽  
pp. 69-86 ◽  
Author(s):  
JONATHAN LAWRY ◽  
HONGMEI HE
Keyword(s):  
Decision Making ◽  
Decision Trees ◽  
Information Aggregation ◽  
Multiple Attribute Decision Making ◽  
Probabilistic Uncertainty ◽  
Functional Relationships ◽  
Multiple Attribute ◽  
Label Semantics ◽  
Multi Attribute Decision Making ◽  
High Level

We propose label semantics as an integrated representation framework for probabilistic uncertainty and fuzziness in multiple-attribute decision making problems. Linguistic attribute hierarchies are then introduced as a means of modelling the complex and often imprecise functional relationships between low-level attributes or measurements and high-level decision or classification variables. Within this framework we introduce linguistic decision trees as a tool for information aggregation in multi-attribute decision problems and describe the process of information propagation through a hierarchy of linked decision trees. In addition, we consider the ranking of different alternatives or examples based on their linguistic descriptions of a high-level utility variable. Finally, we discuss how linguistic decision trees embedded in attribute hierarchies can be learnt from data.

Materials Ranking by Means of Multi Attribute Decision Making

2015 ◽  
Vol 760 ◽  
pp. 135-140
Author(s):  
Adrian Stere Paris ◽  
Constantin Târcolea
Keyword(s):  
Decision Making ◽  
Multiple Attribute Decision Making ◽  
Decision Makers ◽  
Mathematical Methods ◽  
Multiple Attribute ◽  
Similar Materials ◽  
Multi Attribute Decision Making

The first part of the work shortly describes possible mathematical methods for decision making. The paper gives an overview with general comments on multi-criteria problems, related to the preferences and the priorities of the decision-makers. The paper proposed and solves an example for material rating by a multiple attribute decision making. The MADM and PCA methodologies have been applied to rank out ten alternatives. Similar materials were selected using both techniques.

PROJECTION METHOD FOR UNCERTAIN MULTI-ATTRIBUTE DECISION MAKING WITH PREFERENCE INFORMATION ON ALTERNATIVES

2004 ◽  
Vol 03 (03) ◽  
pp. 429-434 ◽  
Author(s):  
ZESHUI XU ◽  
QINGLI DA
Keyword(s):  
Decision Making ◽  
Projection Method ◽  
Multiple Attribute Decision Making ◽  
Projection Model ◽  
Preference Information ◽  
Objective Information ◽  
Attribute Weights ◽  
Multiple Attribute ◽  
Multi Attribute Decision Making

In this paper, we study the uncertain multiple attribute decision making problems with preference information on alternatives (UMADM-PIA, for short), in which the information on attribute weights is not precisely known, but value ranges can be obtained. A projection method is proposed for the UMADM-PIA. To reflect the decision maker's preference information, a projection model is established to determine the weights of attributes, and then to select the most desirable alternative(s). The method can reflect both the objective information and the decision maker's subjective preferences, and can also be performed on computer easily. Finally, an illustrative example is given to verify the proposed method and to demonstrate its feasibility and practicality.

scholarly journals Multiple Attribute Decision-Making Method Using Linguistic Cubic Hesitant Variables

Algorithms ◽  
2018 ◽  
Vol 11 (9) ◽  
pp. 135 ◽  
Author(s):  
Jun Ye ◽  
Wenhua Cui
Keyword(s):  
Decision Making ◽  
Score Function ◽  
Multiple Attribute Decision Making ◽  
Aggregation Operators ◽  
Hesitant Fuzzy Set ◽  
Least Common Multiple ◽  
Weighted Arithmetic ◽  
Multiple Attribute ◽  
Multi Attribute Decision Making ◽  
Human Thinking

Linguistic decision making (DM) is an important research topic in DM theory and methods since using linguistic terms for the assessment of the objective world is very fitting for human thinking and expressing habits. However, there is both uncertainty and hesitancy in linguistic arguments in human thinking and judgments of an evaluated object. Nonetheless, the hybrid information regarding both uncertain linguistic arguments and hesitant linguistic arguments cannot be expressed through the various existing linguistic concepts. To reasonably express it, this study presents a linguistic cubic hesitant variable (LCHV) based on the concepts of a linguistic cubic variable and a hesitant fuzzy set, its operational relations, and its linguistic score function for ranking LCHVs. Then, the objective extension method based on the least common multiple number/cardinality for LCHVs and the weighted aggregation operators of LCHVs are proposed to reasonably aggregate LCHV information because existing aggregation operators cannot aggregate LCHVs in which the number of their hesitant components may imply difference. Next, a multi-attribute decision-making (MADM) approach is proposed based on the weighted arithmetic averaging (WAA) and weighted geometric averaging (WGA) operators of LCHVs. Lastly, an illustrative example is provided to indicate the applicability of the proposed approaches.

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