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Evaluating sub-contractors performance using EFNIM
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DOI：10.1016/j.autcon.2006.09.005.png)
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In the construction industry, sub-contractor's performance is a crucial factor in their awards of a new job by a general contractor. The objective of this study is to improve the current practices for evaluating sub-contractors performance. Drawbacks of current evaluation process are discussed firstly. The appropriateness for adopting the Evolutionary Fuzzy Neural Inference Model (EFNIM) for improving the drawbacks is studied. A Sub-contractor Performance Evaluation Model (SPEM) is then developed by employing the EFNIM. The effectiveness of the proposed SPEM is validated by performing case study of a real general contractor. Validation results show that the proposed method accurately measures sub-contractor's performance enhancing the current practice of evaluation. (C) 2006 Elsevier B.V. All rights reserved.
Keyword:
sub-contractor
performance evaluation
artificial intelligence (AI)
genetic algorithms
fuzzy logic
neural networks
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