Competency-based selection and assignment of human resources to construction projects

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.