Hybrid Local Search Techniques for the Generalized Balanced Academic Curriculum Problem

The Balanced Academic Curriculum Problem (BACP) consists in assigning courses to teaching periods satisfying prerequisites and balancing students' load. BACP is included in CSPlib along with three benchmark instances. However, the BACP formulation in CSPLib is actually simpler than the real problem that, in general, universities have to solve in practice. In this paper, we propose a generalized formulation of the problem and we study a set of hybrid solution techniques based on high-level control strategies that drive a collection of basic local search components. The result of the study allows us to build a complex combination of simulated annealing, dynamic tabu search and large-neighborhood search. In addition, we present six new instances obtained from our university, which are much larger and more challenging than the CSPlib ones (the latter are always solved to optimality in less than 0.1 seconds by our techniques). For the sake of possible future comparisons, we make available through the web all the input data, our scores and results, and a solution validator.

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

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

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

[4]  Luca Di Gaspero,et al.  Measurability and Reproducibility in University Timetabling Research: Discussion and Proposals , 2006, PATAT.

[5]  Luca Di Gaspero,et al.  Neighborhood Portfolio Approach for Local Search Applied to Timetabling Problems , 2006, J. Math. Model. Algorithms.

[6]  Nenad Mladenović,et al.  An Introduction to Variable Neighborhood Search , 1997 .

[7]  Abraham P. Punnen,et al.  A survey of very large-scale neighborhood search techniques , 2002, Discret. Appl. Math..

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

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

[10]  Thomas Stützle,et al.  Stochastic Local Search: Foundations & Applications , 2004 .

[11]  Helena Ramalhinho Dias Lourenço,et al.  Iterated Local Search , 2001, Handbook of Metaheuristics.

[12]  Silvano Martello,et al.  Meta-Heuristics: Advances and Trends in Local Search Paradigms for Optimization , 2012 .

[13]  F. Glover,et al.  Handbook of Metaheuristics , 2019, International Series in Operations Research & Management Science.

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

[16]  Barry McCollum,et al.  The Second International Timetabling Competition (ITC-2007): Curriculum-based Course Timetabling (Track 3) — preliminary presentation — , 2007 .

[17]  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.

[18]  Edmund K. Burke,et al.  Practice and Theory of Automated Timetabling VI, 6th International Conference, PATAT 2006, Brno, Czech Republic, August 30 - September 1, 2006, Revised Selected Papers , 2007, PATAT.

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