0TKEvaluating sub-contractors performance using EFNIM.pdfVIP

Automation in Construction 16 (2007) 525 – 530 /locate/autcon Evaluating sub-contractors performance using EFNIM Chien-Ho Ko ⁎, Min-Yuan Cheng, Tsung-Kuei Wu Department of Construction Engineering, National Taiwan University of Science and Technology, Taiwan Accepted 15 September 2006 Abstract In the construction industry, sub-contractors 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-contractors performance enhancing the current practice of evaluation. © 2006 Elsevier B.V. All rights reserved. Keywords: Sub-contractor; Performance Evaluation; Artificial Intelligence (AI); Genetic Algorithms; Fuzzy Logic; Neural Networks 1. Introduction of two stages: primary score and final score. The primary score is examined by field superintendents, whereas final score is Construction is an industry completed by numerous sub-