The generalized balanced academic curriculum problem with heterogeneous classes

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.

[1]  Luca Di Gaspero,et al.  EASYLOCAL++: an object‐oriented framework for the flexible design of local‐search algorithms , 2003, Softw. Pract. Exp..

[2]  Edmund K. Burke,et al.  A branch-and-cut procedure for the Udine Course Timetabling problem , 2012, Ann. Oper. Res..

[3]  Frédéric Saubion,et al.  Solving the Balanced Academic Curriculum Problem with an Hybridization of Genetic Algorithm and Constraint Propagation , 2006, ICAISC.

[4]  Ryszard Tadeusiewicz,et al.  Artificial Intelligence and Soft Computing - ICAISC 2006, 8th International Conference, Zakopane, Poland, June 25-29, 2006, Proceedings , 2006, International Conference on Artificial Intelligence and Soft Computing.

[5]  Broderick Crawford,et al.  A Quantitative Approach for the Design of Academic Curricula , 2007, HCI.

[6]  Toby Walsh,et al.  CSPLIB: A Benchmark Library for Constraints , 1999, CP.

[7]  R. A. Groeneveld,et al.  Practical Nonparametric Statistics (2nd ed). , 1981 .

[8]  C. Borror Practical Nonparametric Statistics, 3rd Ed. , 2001 .

[9]  S. Dreyfus,et al.  Thermodynamical Approach to the Traveling Salesman Problem : An Efficient Simulation Algorithm , 2004 .

[10]  Luca Di Gaspero,et al.  The balanced academic curriculum problem revisited , 2012, J. Heuristics.

[11]  Emile H. L. Aarts,et al.  Simulated Annealing: Theory and Applications , 1987, Mathematics and Its Applications.

[12]  M. F. Fuller,et al.  Practical Nonparametric Statistics; Nonparametric Statistical Inference , 1973 .

[13]  Luca Di Gaspero,et al.  Hybrid Local Search Techniques for the Generalized Balanced Academic Curriculum Problem , 2008, Hybrid Metaheuristics.

[14]  Thomas W. Lucas,et al.  Efficient Nearly Orthogonal and Space-Filling Latin Hypercubes , 2007, Technometrics.

[15]  Lamberto Cesari,et al.  Optimization-Theory And Applications , 1983 .

[16]  Carlos Castro,et al.  Variable and Value Ordering When Solving Balanced Academic Curriculum Problems , 2001, ArXiv.

[17]  C. D. Gelatt,et al.  Optimization by Simulated Annealing , 1983, Science.

[18]  Yves Deville,et al.  A CP Approach to the Balanced Academic Curriculum Problem , 2007 .

[19]  Ehl Emile Aarts,et al.  Simulated annealing and Boltzmann machines , 2003 .

[20]  Toby Walsh,et al.  Modelling a Balanced Academic Curriculum Problem , 2002 .

[21]  Ben Paechter,et al.  Setting the Research Agenda in Automated Timetabling: The Second International Timetabling Competition , 2010, INFORMS J. Comput..

[22]  Thomas Stützle,et al.  A Racing Algorithm for Configuring Metaheuristics , 2002, GECCO.

[23]  Gavriel Salvendy,et al.  Interacting in information environments , 2007 .

[24]  T. M. Cioppa,et al.  Efficient Nearly Orthogonal and Space-Filling , 2007 .