Evaluating applicability of VIKOR method of multi-criteria decision making for parameters selection problem in rotor spinning

2012 ◽  
Vol 13 (6) ◽  
pp. 802-808 ◽  
Author(s):  
A. R. Fallahpour ◽  
A. R. Moghassem
Keyword(s):  
Decision Making ◽  
Selection Problem ◽  
Multi Criteria Decision Making ◽  
Vikor Method ◽  
Rotor Spinning ◽  
Parameters Selection

Rank Reversal and the VIKOR Method: An Empirical Evaluation

2018 ◽  
Vol 17 (02) ◽  
pp. 513-525 ◽  
Author(s):  
Blanca Ceballos ◽  
David A. Pelta ◽  
María T. Lamata
Keyword(s):  
Decision Making ◽  
Empirical Evaluation ◽  
Decision Problems ◽  
Multi Criteria Decision Making ◽  
Common Phenomenon ◽  
Vikor Method ◽  
Rank Reversal

Rank reversal is a common phenomenon in multi-criteria decision-making methods. It appears when the addition/deletion of new options to the alternatives’ set produces a change in the original ranking. In this contribution, we want to assess this phenomenon in the context of the VIKOR method. Using randomly generated multi-criteria decision problems, we confirmed that rank reversal existed and strongly depended on VIKOR’s parameter. Also, we observed that the influence of the number of alternatives was stronger than that of the number of criteria. Finally, although rank reversal may exist, we saw that it may not affect the top alternative of the ranking, thus potentially having a low impact.

Modeling Knowledge Employee’s Turnover Based on P-SVM

2010 ◽  
Vol 121-122 ◽  
pp. 825-831
Author(s):  
Yong Zhao ◽  
Ye Zheng Liu
Keyword(s):  
Genetic Algorithm ◽  
Decision Making ◽  
Model Development ◽  
Forecast Model ◽  
Support Vector ◽  
Multi Criteria Decision Making ◽  
Parameters Selection ◽  
Vector Machines ◽  
Decision Making Problem ◽  
Simulation Results

Knowledge employee’s turnover forecast is a multi-criteria decision-making problem involving various factors. In order to forecast accurately turnover of knowledge employees, the potential support vector machines(P-SVM) is introduced to develop a turnover forecast model. In the model development, a chaos algorithm and a genetic algorithm (GA) are employed to optimize P-SVM parameters selection. The simulation results show that the model based on potential support vector machine with chaos not only has much stronger generalization ability but also has the ability of feature selection.

An Improved TOPSIS Method for Material Selection Model

2014 ◽  
Vol 951 ◽  
pp. 120-123
Author(s):  
Wei Fan Huang
Keyword(s):  
Decision Making ◽  
Influencing Factors ◽  
Material Science ◽  
Material Selection ◽  
Selection Process ◽  
Selection Problem ◽  
Entropy Measure ◽  
Multi Criteria Decision Making ◽  
Topsis Method ◽  
Science Field

Material selection problem becomes an important issue in the material science field. It is important for design for mechanical, electrical, thermal, chemical et al. There are several influencing factors in the material selection process, and thus material selection problem is a multi-criteria decision making (MCDM) problem. Relative entropy measure can depict the closeness of the two systems, and then this paper will use it to develop an improved TOPSIS method for the material selection problem. Finally, a practical example is given to demonstrate that the proposed method is effective and feasible.

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