Name
Abstract | ||
|---|---|---|
TOPSIS is a multiple criteria method to identify solutions from a finite set of alternatives based upon simultaneous minimization of distance from an ideal positive point and maximization of distance from a negative point. Owing to vague concepts frequently represented in decision data, the crisp value is inadequate to model real-life situations. In this paper, the scoring of each alternative and the weight of each criterion are described by linguistic terms which can be expressed in triangular fuzzy numbers. Then, the ratings and weights assigned by decision makers are averaged and normalized into a comparable scale. A coefficient of variation is defined to determine the ranking order of alternatives by calculating the mean value and standard deviation. A numerical example demonstrates the feasibility of the proposed method. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1007/11785231_28 | ICAISC |
Keywords | Field | DocType |
multiple criteria method,ideal positive point,finite set,decision maker,fuzzy ranking,crisp value,mean value,comparable scale,negative point,decision data,multi attribute decision,fuzzy topsis,standard deviation,coefficient of variation | Finite set,Ranking,Fuzzy logic,Artificial intelligence,TOPSIS,Soft computing,Fuzzy number,Standard deviation,Machine learning,Maximization,Mathematics | Conference |
Volume | ISSN | ISBN |
4029 | 0302-9743 | 3-540-35748-3 |
Citations | PageRank | References |
1 | 0.38 | 6 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mohammad Reza Mehregan | 1 | 4 | 1.13 |
| Hossein Safari | 2 | 28 | 2.82 |