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Design of balanced MBA student teams

Author

Listed:
  • J Desrosiers

    (HEC Montréal and GERAD, Chemin de la Côte-Sainte-Catherine)

  • N Mladenović

    (HEC Montréal and GERAD, Chemin de la Côte-Sainte-Catherine
    Mathematical Institute, Serbian Academy of Sciences and Arts, Kneza Mihajla)

  • D Villeneuve

    (HEC Montréal and GERAD, Chemin de la Côte-Sainte-Catherine)

Abstract

In some schools and universities, students must sometimes be divided into several teams in such a way that each team provides a good representation of the classroom population. In this paper, two different ways of measuring the balance among teams are proposed: min-sum and min-max objective functions. For the first function and the L1-norm used in the space of attributes, an exact solution method based on a set partitioning formulation and on the enumeration of all possible team patterns is presented. For the second objective function, a set partitioning formulation is also considered, but as an approximation. In order to solve large problem instances, we have also developed metaheuristics based on variable neighbourhood search. Models and methods are tested on data from an MBA programme.

Suggested Citation

  • J Desrosiers & N Mladenović & D Villeneuve, 2005. "Design of balanced MBA student teams," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 56(1), pages 60-66, January.
    • Handle: RePEc:pal:jorsoc:v:56:y:2005:i:1:d:10.1057_palgrave.jors.2601775
    DOI: 10.1057/palgrave.jors.2601775
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    References listed on IDEAS

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    1. Hansen, Pierre & Mladenovic, Nenad, 2001. "Variable neighborhood search: Principles and applications," European Journal of Operational Research, Elsevier, vol. 130(3), pages 449-467, May.
    2. Bhadury, Joyendu & Mighty, E. Joy & Damar, Hario, 2000. "Maximizing workforce diversity in project teams: a network flow approach," Omega, Elsevier, vol. 28(2), pages 143-153, April.
    3. B M Baker & C Benn, 2001. "Assigning pupils to tutor groups in a comprehensive school," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 52(6), pages 623-629, June.
    4. George B. Dantzig & Philip Wolfe, 1960. "Decomposition Principle for Linear Programs," Operations Research, INFORMS, vol. 8(1), pages 101-111, February.
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    Citations

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    Cited by:

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    2. Yang, Xiao & Cai, Zonghui & Jin, Ting & Tang, Zheng & Gao, Shangce, 2022. "A three-phase search approach with dynamic population size for solving the maximally diverse grouping problem," European Journal of Operational Research, Elsevier, vol. 302(3), pages 925-953.
    3. Theresa M. Roeder & Robert M. Saltzman, 2014. "Schedule-Based Group Assignment Using Constraint Programming," INFORMS Transactions on Education, INFORMS, vol. 14(2), pages 63-72, February.
    4. Krass, Dmitry & Ovchinnikov, Anton, 2010. "Constrained group balancing: Why does it work," European Journal of Operational Research, Elsevier, vol. 206(1), pages 144-154, October.
    5. Pierre Hansen & Nenad Mladenović & José Moreno Pérez, 2010. "Variable neighbourhood search: methods and applications," Annals of Operations Research, Springer, vol. 175(1), pages 367-407, March.
    6. Andrew Bowers & Melissa R. Bowers & Nana Bryan & Paolo Letizia & Spencer Murphy, 2023. "Forming Student Teams to Incorporate Soft Skills and Commonality of Schedule," Interfaces, INFORMS, vol. 53(2), pages 111-127, March.
    7. Sergio García & Valentina Cacchiani & Lieselot Vanhaverbeke & Martin Bischoff, 2014. "The table placement problem: a research challenge at the EWI 2007," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 22(1), pages 208-226, April.

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