Rashidi, A and Jazebi, F and Brilakis, I (2011) Neuro-Fuzzy genetic system for selection of construction project managers. Journal of Construction Engineering and Management, 137. pp. 17-29. ISSN 0733-9364Full text not available from this repository.
Choosing a project manager for a construction project—particularly, large projects—is a critical project decision. The selection process involves different criteria and should be in accordance with company policies and project specifications. Traditionally, potential candidates are interviewed and the most qualified are selected in compliance with company priorities and project conditions. Precise computing models that could take various candidates’ information into consideration and then pinpoint the most qualified person with a high degree of accuracy would be beneficial. On the basis of the opinions of experienced construction company managers, this paper, through presenting a fuzzy system, identifies the important criteria in selecting a project manager. The proposed fuzzy system is based on IF-THEN rules; a genetic algorithm improves the overall accuracy as well as the functions used by the fuzzy system to make initial estimates of the cluster centers for fuzzy c-means clustering. Moreover, a back-propagation neutral network method was used to train the system. The optimal measures of the inference parameters were identified by calculating the system’s output error and propagating this error within the system. After specifying the system parameters, the membership function parameters—which by means of clustering and projection were approximated—were tuned with the genetic algorithm. Results from this system in selecting project managers show its high capability in making high-quality personnel predictions
|Uncontrolled Keywords:||Construction project manager Selection criteria Fuzzy system Parameter identification|
|Divisions:||Div D > Construction Engineering|
|Depositing User:||Cron Job|
|Date Deposited:||07 Mar 2014 11:21|
|Last Modified:||19 May 2014 01:12|
Actions (login required)