The team selection problem is usually solved by ranking candidates based on the preferences of decision-makers and allowing the decision-makers to take turns selecting candidates. While this solution method is simple and might seem fair it usually results in an unfair allocation of candidates to the different teams, i.e. the quality of the teams might be quite different according to the rankings articulated by the decision-makers. In this paper, we propose a new method based on Ant Colony Optimization (ACO), where the selection process is performed in a new context, with more than two decision-makers selecting from a common set of candidates. Furthermore, a plugin implementing this method for the KNIME platform was developed.
Lugo, L, Bello, M, Nowe, A & Bello, R 2018, A solution for the team selection problem using aco. in C Blum, A Reina, M Dorigo, M Birattari, AL Christensen & V Trianni (eds), Swarm Intelligence - 11th International Conference, ANTS 2018, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11172 LNCS, Springer Verlag, pp. 325-332, 11th International Conference on Swarm Intelligence, 29/10/18. https://doi.org/10.1007/978-3-030-00533-7_26
Lugo, L., Bello, M., Nowe, A., & Bello, R. (2018). A solution for the team selection problem using aco. In C. Blum, A. Reina, M. Dorigo, M. Birattari, A. L. Christensen, & V. Trianni (Eds.), Swarm Intelligence - 11th International Conference, ANTS 2018, Proceedings (pp. 325-332). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11172 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-030-00533-7_26
@inproceedings{1d6b9305d2da43f9810b467924b910db,
title = "A solution for the team selection problem using aco",
abstract = "The team selection problem is usually solved by ranking candidates based on the preferences of decision-makers and allowing the decision-makers to take turns selecting candidates. While this solution method is simple and might seem fair it usually results in an unfair allocation of candidates to the different teams, i.e. the quality of the teams might be quite different according to the rankings articulated by the decision-makers. In this paper, we propose a new method based on Ant Colony Optimization (ACO), where the selection process is performed in a new context, with more than two decision-makers selecting from a common set of candidates. Furthermore, a plugin implementing this method for the KNIME platform was developed.",
author = "L{\'a}zaro Lugo and Marilyn Bello and Ann Nowe and Rafael Bello",
year = "2018",
month = jan,
day = "1",
doi = "10.1007/978-3-030-00533-7_26",
language = "English",
isbn = "9783030005320",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "325--332",
editor = "Christian Blum and Andreagiovanni Reina and Marco Dorigo and Mauro Birattari and Christensen, {Anders L.} and Vito Trianni",
booktitle = "Swarm Intelligence - 11th International Conference, ANTS 2018, Proceedings",
address = "Germany",
note = "11th International Conference on Swarm Intelligence, ANTS 2018 ; Conference date: 29-10-2018 Through 31-10-2018",
}