| Article ID | Journal | Published Year | Pages | File Type |
|---|---|---|---|---|
| 495909 | Applied Soft Computing | 2012 | 12 Pages |
As part of human resource management policies and practices, construction firms need to define competency requirements for project staff, and recruit the necessary team for completion of project assignments. Traditionally, potential candidates are interviewed and the most qualified are selected. Applicable methodologies that could take various candidate competencies and inherent uncertainties of human evaluation into consideration and then pinpoint the most qualified person with a high degree of reliability would be beneficial. In the last decade, computing with words (CWW) has been the center of attention of many researchers for its intrinsic capability of dealing with linguistic, vague, interdependent, and imprecise information under uncertain environments. This paper presents a CWW approach, based on the specific architecture of Perceptual Computer (Per-C) and the Linguistic Weighted Average (LWA), for competency based selection of human resources in construction firms. First, human resources are classified into two types of main personnel: project manager and engineer. Then, a hierarchical criteria structure for competency based evaluation of each main personnel category is established upon the available literature and survey. Finally, the perceptual computer approach is utilized to develop a practical model for competency based selection of personnel in construction companies. We believe that the proposed approach provides a useful tool to handle personnel selection problem in a more reliable and intelligent manner.
► We present a computing with words approach for competency based selection of human resources. ► Competency framework is developed for the evaluation of project managers and engineers. ► The selection problem is established upon perceptual computer and Linguistic Weighted Average methodologies. ► Interval type-2 fuzzy sets (IT2 FSs) are exploited for modeling the uncertainties associated with words. ► IT2 FS results are mapped into linguistic terms in order to evaluate the resource pool.
