Name
Abstract | ||
|---|---|---|
A distance approach based on extreme points, or predefined ideal and anti-ideal points, is proposed to improve on the TOPSIS (Technique for Order Performance [or Ordered Preference] by Similarity to Ideal Solution) method of multiple criteria ranking. Two case studies demonstrate how the analysis procedure works, and provide a basis for comparison of the proposed method to the original TOPSIS and similar methods. In applications, the new method produces results that are generally consistent with the original technique, but offers new features such as a clear interpretation of extreme points, more flexibility in setting extreme points, no normalization distortion, and the ability to handle non-monotonic criteria. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1016/j.mcm.2010.10.001 | Mathematical and Computer Modelling |
Keywords | Field | DocType |
ideal solution,multiple criteria ranking,order performance,comparative study,extreme point,distance-based ranking,original topsis,extreme-distance approach,topsis,original technique,ordered preference,multiple criteria decision analysis,new method,new feature,multiple criterion,similar method | Extreme point,Data mining,Mathematical optimization,Normalization (statistics),Multiple criteria,Multiple-criteria decision analysis,Ranking,Ideal solution,TOPSIS,Distortion,Mathematics | Journal |
Volume | Issue | ISSN |
53 | 5-6 | Mathematical and Computer Modelling |
Citations | PageRank | References |
4 | 0.41 | 13 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ye Chen | 1 | 66 | 7.20 |
| D. Marc Kilgour | 2 | 571 | 70.61 |
| K. W. Hipel | 3 | 812 | 116.70 |