An automated procedure for selecting project managers in construction firms

Abstract

Selecting a suitable project manager for construction projects is one of the most important decisions made by construction firms. In the traditional approach, interviews are conducted by senior managers of the company, who consider the project requirements and the candidates’ capabilities. However, interviewing candidates is usually time consuming and there is the risk of impaired judgment, leading to human error. Decision-making support systems are therefore very useful. In previous work, the authors proposed a fuzzy system to address the issues described above (Rashidi et al. 2011). In this paper, a simpler robust model is presented. The advantages of this new model lie in its simplicity and the fact that it is not necessary to consider many criteria in the selection procedure when using this model. This model only requires 15 fields of candidate information. In the development of this model, the first step is construction of an initial fuzzy model based on all the criteria that may be considered when selecting a project manager. The significance of the coefficients of the criteria are then determined. In the next step, the model is optimized by changing the number of fuzzy rules and reducing the number of criteria. Finally, the most appropriate model is chosen on the basis of the least number of criteria required to obtain accurate results. To show the model's capability, it is used in real interviews. The obtained results indicate the high accuracy of the model in predicting the output, that is, the best candidate in the interviews.

Keywords:

• construction project manager
• fuzzy rules
• criteria
• input and output data
• fuzzy curves