Abstract:
t 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.
Precise computing models, which could take various candidate competencies into consideration and then
pinpoint the most qualified person with a high degree of accuracy, would be beneficial. This paper presents
a fuzzy adaptive decision making model for selection of different types of competent personnel. For this
purpose, human resources are classified into four types of main personnel: Project Manager, Engineer,
Technician, and Laborer. Then the competency criteria model of each main personnel is developed.
Decision making is performed in two stages: a fuzzy Analytic Hierarchy Process (AHP) for evaluating the
competency criteria, and an Adaptive Neuro-Fuzzy Inference System (ANFIS) for establishing competency
IF-THEN rules of the fuzzy inference system. Finally, a hybrid learning algorithm is used to train the
system. The proposed model integrates a fuzzy logic qualitative approach and neural network adaptive
capabilities to evaluate and rank construction personnel based on their competency. Results from this
system in personnel staffing show the high capability of the model in making a high quality personnel
selection.
