A type-2 fuzzy based system for handling the uncertainties in group decisions for ranking job applicants within Human Resources systems

Ranking applicants for a given job is one of the most important processes for Human Resources (HR) systems. The ranking of job applicants involves two main processes which are the specification of the requirements criteria for a given job (experience, skills, etc) and the matching between the applicantspsila profiles and the job requirements. There is currently a strong move towards automating these two processes to generate an applicantspsila ranking system that gives consistent and fair results. However there is a high level of uncertainty involved in these two processes as they involve the input of several experts. These experts will have different opinions, expectations the interpretations for the requirements specification as well as for the applicants matching and ranking. This paper presents a novel approach for ranking job applicants by employing type-2 fuzzy sets for handling the uncertainties in group decisions in a panel of experts. Hence the presented system will enable automating the processes of requirements specification and applicants matching/ranking. We have performed real world experiments in the care domain where our system handled the uncertainties and produced ranking decisions that were consistent with those of the human experts. To the authorspsila knowledge, this will be the first type-2 based commercial software system.