Improved Construction Subcontractor Evaluation Performance Using ESIM

Abstract

This article may be used for research, teaching, and private study purposes. Any substantial or systematic reproduction, redistribution, reselling, loan, sub-licensing, systematic supply, or distribution in any form to anyone is expressly forbidden. The publisher does not give any warranty express or implied or make any representation that the contents will be complete or accurate or up to date. The accuracy of any instructions, formulae, and drug doses should be independently verified with primary sources. The publisher shall not be liable for any loss, actions, claims, proceedings, demand, or costs or damages whatsoever or howsoever caused arising directly or indirectly in connection with or arising out of the use of this material. & 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.

DOI: 10.1080/08839514.2012.648403

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Cite this paper

@article{Cheng2012ImprovedCS, title={Improved Construction Subcontractor Evaluation Performance Using ESIM}, author={Min-Yuan Cheng and Yu-Wei Wu}, journal={Applied Artificial Intelligence}, year={2012}, volume={26}, pages={261-273} }