grouping multiple attribute decision-making techniques (MADM)
What is it about?
Multiple attribute decision-making (MADM) techniques lack a comprehensive classification. We measure the performance of 17 techniques and classify them into some groups based on their performance using fuzzy c-means clustering method.
Why is it important?
-- We measured the quantitative performance of MADM techniques in 7 variables (simplicity in learning and deploying, speed, complexity of calculations, the number of inputs, the quality of the underlying logic, the quality of rankings, and the rate of growth in large problems). -- We presented for the first time a classification approach that considers the performance of MADM technique, that's why we call it output-oriented. Previous works had only provided input-oriented or process-oriented classifications.
The following have contributed to this page: Dr Mohammad Reza Taghizadeh Yazdi and Mr. Abdolkarim Mohammadi-Balani
In partnership with: