Multigranulation rough set model in hesitant fuzzy information systems and its application in person-job fit

Abstract

Person-job fit is a significant issue in the variety of critical business intelligence applications that aims to match suitable professional abilities with job demands for each job seeker, and many studies based on fuzzy sets have been developed on this topic. Among different types of fuzzy sets, hesitant fuzzy sets are usually utilized to handle situations in which experts hesitate among several values to evaluate an alternative. Recently, various hesitant fuzzy decision making methods have been established, but none of them can be used to solve group decision making problems by means of the multigranulation rough set model and the TODIM (an acronym in Portuguese of interactive and multi-criteria decision making) approach. Thus, to solve problems of hesitant fuzzy information analysis and group decision making for person-job fit, we construct a new multigranulation rough set model, named hesitant fuzzy multigranulation rough sets, through combining hesitant fuzzy sets with multigranulation rough sets. Then in order to express the decision making knowledge base more reasonably, we extend the proposed model from single universe to two universes. At last, by utilizing the TODIM approach, we propose a general decision making method that is applied to person-job fit, and the effectiveness of the proposed decision making method is demonstrated by a case study.