This paper presents a new output‐oriented classification of multiple attribute decision‐making (MADM) techniques, not based on common subjective comparisons, but mostly on quantitative and computer‐aided comparisons and results. Several classifi...
http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
https://www.riss.kr/link?id=O119330574
2019년
-
0969-6016
SCOPUS;SCIE;SSCI
학술저널
2476-2493 [※수록면이 p5 이하이면, Review, Columns, Editor's Note, Abstract 등일 경우가 있습니다.]
0
상세조회0
다운로드다국어 초록 (Multilingual Abstract)
This paper presents a new output‐oriented classification of multiple attribute decision‐making (MADM) techniques, not based on common subjective comparisons, but mostly on quantitative and computer‐aided comparisons and results. Several classifi...
This paper presents a new output‐oriented classification of multiple attribute decision‐making (MADM) techniques, not based on common subjective comparisons, but mostly on quantitative and computer‐aided comparisons and results. Several classifications of MADM techniques exist, all of which are either input‐oriented (based on the type of input data) or process‐oriented (depending on the process of calculating the final results using the input data). The classification provided in this paper is based on measuring the performance of 17 MADM techniques (SAW, ELECTRE I, TOPSIS, ORESTE, PROMETHEE I, EVAMIX, MAUT, REGIME, MAPPAC, TACTIC, VIKOR, ARGUS, COPRAS, SMART, PACMAN, MOORA, and ARAS) in seven performance 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) and clustering them using fuzzy c‐means clustering method. Results indicate that the considered techniques can be best classified into two clusters.
Designing service system networks with interruption risks
Portfolio management with higher moments: the cardinality impact