Knowledge-driven fuzzy consensus model for team formation. (1st December 2021)

• Record Type: Journal Article
• Authors: D'Aniello, Giuseppe; Gaeta, Matteo; Lepore, Mario; Perone, Maria
• Is Part Of: Expert systems with applications. Volume 184(2021)
• Publication Date: 2021-12-01
• Subjects: Fuzzy consensus model -- Knowledge management -- Human resource allocation -- Knowledge skill attitude -- Team formation -- Small and medium enterprises; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2021.115522
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: Team formation usually do not consider social factors and the opinions of workers. KnowMIS-Team: a lightweight team formation approach for small and medium enterprise. Based on competence matching and group decision making using fuzzy consensus model. A conceptual architecture of an intelligent system implementing KnowMIS-Team. Evaluated on teams of real research projects of a R&D center. Abstract: The correct allocation of human resources is of utmost importance for any kind of enterprise and organization. Many approaches have been defined so far to support team formation leveraging on different techniques, from knowledge engineering to operational research and computational intelligence. Unfortunately, these approaches are often specifically thought for large organizations owning the right set of technological assets and human resources able to manage and use these approaches. In this work, we propose an original approach to team formation, namely the KnowMIS-Team approach, specifically designed for knowledge-intensive small and medium enterprises. This is a lightweight hybrid approach that combines three different techniques: a knowledge-driven technique for finding the most competent team for a given project based on a lightweight semantic model of knowledge, skills and attitudes; a top-down, leader-selected approach wherein the competent members selected in the previous phase can propose their candidate teams; a bottom-up fuzzy consensus-based mechanism in which the employees of the organization can express their preferences on the candidate teams. A conceptual architecture of an intelligent system implementing the approach is also presented. The KnowMIS-Team approach is the overall result of many years of experience in team formation and management for a research center and embeds all the best practices therein adopted, and it has been experimented in the same center and in other university spin-offs for many years, contributing to the realization of successful projects.