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The generalized balanced academic curriculum problem with heterogeneous classes

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  • Sara Ceschia
  • Luca Di Gaspero
  • Andrea Schaerf

Abstract

We propose an extension of the Generalized Balanced Academic Curriculum Problem (GBACP), a relevant planning problem arising in many universities. The problem consists of assigning courses to teaching terms and years, satisfying a set of precedence constraints and balancing students’ load among terms. Differently from the original GBACP formulation, in our case, the same course can be assigned to different years for different curricula (i.e., the predetermined sets of courses from which a student can choose), leading to a more complex solution space. The problem is tackled by both Integer Programming (IP) methods and combinations of metaheuristics based on local search. The experimental analysis shows that the best results are obtained by means of a two-stage metaheuristic that first computes a solution for the underlying GBACP and then refines it by searching in the extended solution space. Copyright Springer Science+Business Media New York 2014

Suggested Citation

  • Sara Ceschia & Luca Di Gaspero & Andrea Schaerf, 2014. "The generalized balanced academic curriculum problem with heterogeneous classes," Annals of Operations Research, Springer, vol. 218(1), pages 147-163, July.
    • Handle: RePEc:spr:annopr:v:218:y:2014:i:1:p:147-163:10.1007/s10479-013-1358-8
    DOI: 10.1007/s10479-013-1358-8
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    References listed on IDEAS

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    1. Barry McCollum & Andrea Schaerf & Ben Paechter & Paul McMullan & Rhyd Lewis & Andrew J. Parkes & Luca Di Gaspero & Rong Qu & Edmund K. Burke, 2010. "Setting the Research Agenda in Automated Timetabling: The Second International Timetabling Competition," INFORMS Journal on Computing, INFORMS, vol. 22(1), pages 120-130, February.
    2. Edmund Burke & Jakub Mareček & Andrew Parkes & Hana Rudová, 2012. "A branch-and-cut procedure for the Udine Course Timetabling problem," Annals of Operations Research, Springer, vol. 194(1), pages 71-87, April.
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    Cited by:

    1. Philippe Olivier & Andrea Lodi & Gilles Pesant, 2022. "Measures of balance in combinatorial optimization," 4OR, Springer, vol. 20(3), pages 391-415, September.
    2. P. Solano Cutillas & D. Pérez-Perales & M. M. E. Alemany Díaz, 2022. "A mathematical programming tool for an efficient decision-making on teaching assignment under non-regular time schedules," Operational Research, Springer, vol. 22(3), pages 2899-2942, July.

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