Name
Abstract | ||
|---|---|---|
The evaluation of potential subcontractors is a complex task for construction companies. The success of such evaluations currently relies heavily on personal factors that include management experience and intuition. The object of this study was to propose a support model that would improve current subcontractor performance evaluation practices. The appropriateness of employing the Evolutionary Support Vector Machine Inference Model (ESIM) in evaluation procedures was studied and analyzed, and a Subcontractor Rating Evaluation Model (SREM) was developed by adapting the ESIM to fit subcontractor performance cases in the historical record. The effectiveness of the proposed SRPM was subsequently validated in a case study on an actual general contractor. The proposed method assigned ratings to subcontractors that were substantively the same as ratings assigned by traditional means. Results demonstrate the value of employing the proposed SREM in subcontractor evaluations. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1080/08839514.2012.648403 | APPLIED ARTIFICIAL INTELLIGENCE |
Keywords | Field | DocType |
support vector machine | Computer science,Inference,Support vector machine,Intuition,Artificial intelligence,Machine learning | Journal |
Volume | Issue | ISSN |
26.0 | 3 | 0883-9514 |
Citations | PageRank | References |
0 | 0.34 | 3 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Min-Yuan Cheng | 1 | 174 | 19.84 |
| Yu-Wei Wu | 2 | 43 | 5.89 |