An Introduction to Genetic Algorithms
An Introduction to Genetic Algorithms
Evolutionary Multiobjective Design of Combinational Logic Circuits
EH '00 Proceedings of the 2nd NASA/DoD workshop on Evolvable Hardware
MultiGen: an integrated multiple-objective solution system
Decision Support Systems
Assigning people to roles in software projects
Software—Practice & Experience
Application of Social Network Analysis to Collaborative Team Formation
CTS '06 Proceedings of the International Symposium on Collaborative Technologies and Systems
Approach for requirement oriented team building in industrial processes
Computers in Industry
Metadata and its impact on libraries: Book Reviews
Journal of the American Society for Information Science and Technology
PROJECT TEAM SELECTION USING FUZZY OPTIMIZATION APPROACH
Cybernetics and Systems
Forming effective worker teams with multi-functional skill requirements
Computers and Industrial Engineering - Special issue: Group technology/cellular manufacturing
Forming reasonably optimal groups: (FROG)
Proceedings of the 16th ACM international conference on Supporting group work
Fuzzy data mining: a literature survey and classification framework
International Journal of Networking and Virtual Organisations
International Journal of Information Technology Project Management
Creating effective student groups: an introduction to groupformation.org
Proceeding of the 44th ACM technical symposium on Computer science education
Modelling interpersonal relations in surgical teams with fuzzy logic
MICAI'12 Proceedings of the 11th Mexican international conference on Advances in Artificial Intelligence - Volume Part I
A strategy of multi-criteria decision-making task ranking in social-networks
The Journal of Supercomputing
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The problem of optimal team formation is domestic to many areas of work organization including education, sport, and business. It is beyond manual implementation to build near optimal teams as soon as the pool of available personnel grows into several tens. The selection process itself is usually well defined - for each team we construct the criteria relating to the required properties (i.e., capabilities) of the team members. Because these properties can be arbitrarily combined in the personnel, the objective function becomes self-conflicting. This aggravates the team formation and calls for a specialized software support. In the paper we present a new fuzzy-genetic analytical model for the problem of project team formation. It builds on previous quantitive approaches, but adds several modeling enhancements like derivation of personnel attributes from dynamic quantitive data, complex attribute modeling, and handling of necessary overcompetency. We improve the flexibility of requirements specification using a special format that expresses the required team capabilities using fuzzy descriptors. We then define a single compound objective function, which incorporates multiple opposing criteria that the solution should maximize. To optimize the selection of multiple project teams with possibly conflicting requirements, we propose a special adaptation of island genetic algorithm with mixed crossover where the fitness of common solution is used to drive the selection within the islands. We test the effectiveness of the system using artificial domains of different complexity and describe some practical experiences of using the system in the educational process.