Name
Abstract | ||
|---|---|---|
The consistency issue is one of the hot research topics in the analytic hierarchy process (AHP) and analytic network process (ANP). To identify the most inconsistent elements for improving the consistency ratio of a reciprocal pairwise comparison matrix (P CM), a bias matrix can be induced to efficiently identify the most inconsistent elements, which is only based on the original PCM. The goal of this paper is to conduct simulation experiments by randomly generating millions numbers of reciprocal matrices with different orders in order to validate the effectiveness of the induced bias matrix model. The experimental results show that the consistency ratios of most of the random inconsistent matrices can be improved by the induced bias matrix model, few random inconsistent matrices with high orders failed the consistency adjustment. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.15837/ijccc.2014.4.1165 | INTERNATIONAL JOURNAL OF COMPUTERS COMMUNICATIONS & CONTROL |
Keywords | Field | DocType |
Reciprocal random matrix, Consistency ratio, induced bias matrix, simulation experiment, analytic hierarchy process (AHP)/analytic network process (ANP) | Data mining,Reciprocal,Matrix model,Computer science,Matrix (mathematics),Algorithm,Analytic network process,Artificial intelligence,Pairwise comparison matrix,Analytic hierarchy process,Machine learning | Journal |
Volume | Issue | ISSN |
9 | 4 | 1841-9836 |
Citations | PageRank | References |
1 | 0.35 | 9 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Daji Ergu | 1 | 373 | 15.27 |
| Gang Kou | 2 | 2527 | 191.95 |
| Yi Peng | 3 | 1303 | 78.20 |
| Xinfeng Yang | 4 | 2 | 0.71 |