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

As part of human resource management policies and practices, construction firms need to definecompetency requirements for project staff, and recruit the necessary team for completion of projectassignments. traditionally, potential candidates are interviewed and the most qualified are selected.precise computing models, which could take various candidate competencies into consideration and thenpinpoint the most qualified person with a high degree of accuracy, would be beneficial. this paper presentsa fuzzy adaptive decision making model for selection of different types of competent personnel. for thispurpose, 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 thecompetency criteria, and an adaptive neuro-fuzzy inference system (anfis) for establishing competencyif-then rules of the fuzzy inference system. finally, a hybrid learning algorithm is used to train thesystem. the proposed model integrates a fuzzy logic qualitative approach and neural network adaptivecapabilities to evaluate and rank construction personnel based on their competency. results from thissystem in personnel staffing show the high capability of the model in making a high quality personnelselection.