Abstract
Students from Master in Business Administration (MBA) programs are usually split into teams. Many schools rotate the teams at the beginning of every term, so that each student works with a different set of peers during every term. Diversity within every team is desirable regarding gender, major, age and other criteria. Achieving diverse teams while avoiding -or minimizing- the repetition of student pairs is a time-consuming complex task for MBA Directors.
The Max-Diversity Orthogonal Regrouping (MDOR) problem is here introduced, where the goal is to maximize a global notion of diversity, considering multiple stages (i.e., terms) and intra-diversity within the teams. A hybrid GRASP/VND heuristic combined with Tabu Search is developed for its resolution. Its effectiveness has been tested in real-life groups from the MBA program offered at IEEM Business School, Universidad de Montevideo, Uruguay, with a notorious gain regarding team diversity and repetition level.
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References
Bahargam, S., Golshan, B., Lappas, T., Terzi, E.: A team-formation algorithm for faultline minimization. Expert Syst. Appl. 119, 441–455 (2019)
Baker, B.M., Benn, C.: Assigning pupils to tutor groups in a comprehensive school. J. Oper. Res. Soc. 52, 623–629 (2001)
Bhadurya, J., Mightyb, E.J., Damar, H.: Maximizing workforce diversity in project teams: a network flow approach. Omega 28, 143–153 (2000)
Brimberg, Jack, Mladenovic, Nenad, Uroševic, Dragan: Solving the maximally diverse grouping problem by skewed general variable neighborhood search. Inf. Sci. 295, 650–675 (2015)
De Bruecker, P., Van den Bergh, J., Beliën, J., Demeulemeester, E.: Workforce planning incorporating skills: state of the art. Eur. J. Oper. Res. 243(1), 1–16 (2015)
Colbourn, C.J.: The complexity of completing partial Latin squares. Discret. Appl. Math. 8(1), 25–30 (1984)
Desrosiers, J., Mladenović, N., Villeneuve, D.: Design of balanced MBA student teams. J. Oper. Res. Soc. 56, 60–66 (2005)
Dragan, Uroševic: Variable neighborhood search for maximum diverse grouping problem. Yugoslav J. Oper. Res. 24, 21–33 (2014)
Fan, Z.P., Chen, Y., Ma, J., Zeng, S.: A hybrid genetic algorithmic approach to the maximally diverse grouping problem. J. Oper. Res. Soc. 62, 1423–1430 (2011)
Feo, T.A., Khellaf, M.: A class of bounded approximation algorithms for graph partitioning. Networks 20, 181–195 (1990)
Mart, R., Pardalos, P.M., Mauricio, G., Resende, C.: Handbook of Heuristics, 1st edn. Springer, Heidelberg (2018). https://doi.org/10.1007/978-3-319-07124-4
Rodriguez, F.J., Lozano, M., García-Martínez, C., González, J.D.: An artificial bee colony algorithm for the maximally diverse grouping problem. Inf. Sci. 230, 183–196 (2013)
Triska, M.: Solution methods for the social golfer problem. Inf. Sci. 295 (2008)
Wi, H., Seungjin, O., Mun, J., Jung, M.: A team formation model based on knowledge and collaboration. Expert Syst. Appl. 36(5), 9121–9134 (2009)
Acknowledgements
This work is partially supported by Project ANII FCE_1_2019_1_156693 Teoría y Construcción de Redes de Máxima Confiabilidad, MATHAMSUD 19-MATH-03 Rare events analysis in multi-component systems with dependent components and STIC-AMSUD ACCON Algorithms for the capacity crunch problem in optical networks.
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Banchero, M., Robledo, F., Romero, P., Sartor, P., Servetti, C. (2021). Max-Diversity Orthogonal Regrouping of MBA Students Using a GRASP/VND Heuristic. In: Mladenovic, N., Sleptchenko, A., Sifaleras, A., Omar, M. (eds) Variable Neighborhood Search. ICVNS 2021. Lecture Notes in Computer Science(), vol 12559. Springer, Cham. https://doi.org/10.1007/978-3-030-69625-2_5
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