Multi-criterion team formation using fuzzy grouping genetic algorithm approach

Michael Mutingi, Charles Mbohwa

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

1 Citation (Scopus)

Abstract

The performance of the entire organization is largely dependent on how well organized human resources are. Organizing human resources may entail formation of project teams, audit teams, specific task forces, multi-national work-force teams, research and development teams, and other forms of team-based work. The process of organizing human resources into teams is non-trivial. It has been realized that most decision makers in such areas are confronted with complexities related to the imprecise choices by human resources on their preferred team mates, skills ratings or qualities, management aspirations, and other multiple human resource-centered constraints and preferences. Due to the presence of fuzzy multiple optimization criteria, the problem tends to be highly complex. It might be desirable to maximize the total knowledge of individual staff in a team and to maximize the total knowledge of the team on an individual task. Consequently, the development of multi-criterion decision-making techniques is essential. In this chapter, a multi-criterion fuzzy grouping genetic algorithm was proposed for the team formation problem. Imprecise preferences, decision maker’s choices, management aspirations, and other preference constraints can be modeled conveniently using fuzzy set theoretic concepts built in fuzzy grouping genetic algorithm. Computational experiments and results were presented in this chapter, illustrating the usefulness of the proposed algorithm. The proposed algorithm is computationally efficient and effective in that it can produce competitive results in manageable computation times. The problem presented in this chapter has inherent characteristics that are a lot similar to other real-world grouping problems such as reviewer grouping problem in the presence of multiple criteria and construction of audit teams. Therefore, the application of the proposed grouping algorithm can be extended to these and other related grouping problems.

Original languageEnglish
Title of host publicationStudies in Computational Intelligence
PublisherSpringer Verlag
Pages89-105
Number of pages17
DOIs
Publication statusPublished - 2017

Publication series

NameStudies in Computational Intelligence
Volume666
ISSN (Print)1860-949X

ASJC Scopus subject areas

  • Artificial Intelligence

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