Name
Abstract | ||
|---|---|---|
An approach using defuzzifying methods is proposed for the fuzzy multiple attribute decision-making (MADM) problems. The computing effectiveness of the proposed defuzzifying methods combined with the simple additive weighting (SAW) method and the technique for order preference by similarity to ideal solution (TOPSIS) method are evaluated based on a comparison to the improved fuzzy weighted average (IFWA) followed by a ranking method. Both SAW and TOPSIS methods are two of classic MADM methods. The purpose of this application is to make the method easier to program and data easier to manipulate. This results in a more practical method for fuzzy decisions. A numerical example and experiment are discussed to demonstrate the implementation of the methods in different input conditions. |
Year | DOI | Venue |
|---|---|---|
1997 | 10.1142/S0218488597000348 | International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems |
Keywords | Field | DocType |
fuzzy madm problem,fuzzy set theory,taguchi methods,fuzzy number | Data mining,Weighting,Fuzzy set operations,Fuzzy set,Artificial intelligence,Fuzzy number,Mathematical optimization,Ranking,Fuzzy logic,Ideal solution,TOPSIS,Mathematics,Machine learning | Journal |
Volume | Issue | ISSN |
5 | 4 | 0218-4885 |
Citations | PageRank | References |
3 | 0.44 | 0 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chie-Bein Chen | 1 | 220 | 21.53 |
| Chiu-Chi Wei | 2 | 32 | 6.90 |