Abstract
Metaheuristics define and explore a set of different neighborhoods, in general, adapted to specific characteristics of a problem. The quality of the solution found relies on the efficiency of the neighborhood used on the local search phase, therefore it is very important to research about the movements or combination of them which compose the neighborhood structure. This paper is based on a recent work reported on literature that deals with four standard movements for the university timetabling problem. This work complements the analysis already done so far, adding five new movements widely known in the literature. Two of then are specific for the restrictions adopted by the curriculum-based formulation proposed on the Second International Timetabling Competition (ITC-2007). The Steepest Descent (SD) algorithm was implemented to study each movement separately and combined. This analysis shows that the quality of the solutions is highly affected by the movements chosen, since the ratio between the worst and best solution (in terms of objective function value), can be up to 13.5.
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References
Bolaji, A.L., Khader, A.T., Al-Betar, M.A., Thomas, J.J.: The effect of neighborhood structures on examination timetabling with artificial bee colony. In: Proceedings of the 9th International Conference on the Practice and Theory of Automated Timetabling, pp. 29–31 (2012)
Ceschia, S., Di Gaspero, L., Schaerf, A.: Design, engineering, and experimental analysis of a simulated annealing approach to the post-enrolment course timetabling problem. Comput. Oper. Res. 39(7), 1615–1624 (2012)
Della Croce, F., Salassa, F.: A variable neighborhood search based matheuristic for nurse rostering problems. Ann. Oper. Res. 218(1), 185–199 (2014)
Di Gaspero, L., Schaerf, A., McCollum, B.: The second international timetabling competition (ITC-2007): curriculum-based course timetabling (track 3). Technical report (2007)
Dueck, G.: New optimization heuristics: the great deluge algorithm and the record-to-record travel. J. Comput. Phys. 104(1), 86–92 (1993)
Erben, W., Keppler, J.: A genetic algorithm solving a weekly course-timetabling problem. In: Burke, E., Ross, P. (eds.) PATAT 1995. LNCS, vol. 1153, pp. 198–211. Springer, Heidelberg (1996). doi:10.1007/3-540-61794-9_60
Kampke, E.H., de Souza Rocha, W., Boeres, M.C.S., Rangel, M.C.: A grasp algorithm with path relinking for the university courses timetabling problem. In: Proceeding Series of the Brazilian Society of Computational and Applied Mathematics, vol. 3(2), pp. 1081–1087 (2015)
Kirkpatrick, S.: Optimization by simulated annealing: quantitative studies. J. Stat. Phys. 34(5), 975–986 (1984)
Lewis, R.: A survey of metaheuristic-based techniques for university timetabling problems. OR Spectr. 30(1), 167–190 (2008)
Lü, Z., Hao, J.-K.: Solving the course timetabling problem with a hybrid heuristic algorithm. In: Dochev, D., Pistore, M., Traverso, P. (eds.) AIMSA 2008. LNCS, vol. 5253, pp. 262–273. Springer, Heidelberg (2008). doi:10.1007/978-3-540-85776-1_22
Lü, Z., Hao, J.K.: Adaptive tabu search for course timetabling. Eur. J. Oper. Res. 200(1), 235–244 (2010)
Lü, Z., Hao, J.K., Glover, F.: Neighborhood analysis: a case study on curriculum-based course timetabling. J. Heuristics 17(2), 97–118 (2011)
Müller, T.: ITC 2007 solver description: a hybrid approach. Ann. Oper. Res. 172(1), 429–446 (2009)
Papadimitriou, C.H., Steiglitz, K.: Combinatorial Optimization: Algorithms and Complexity. Courier Corporation, Mineola (1982)
Ribeiro, C.C., Urrutia, S.: Scheduling the Brazilian soccer tournament: solution approach and practice. Interfaces 42(3), 260–272 (2012)
Russell, S., Norvig, P.: Artificial intelligence: a modern approach, pp. 111–113. Prentice-Hall, Englewood Cliffs, New Jersey (1995)
Santos, H.G., Uchoa, E., Ochi, L.S., Maculan, N.: Strong bounds with cut and column generation for class-teacher timetabling. Ann. Oper. Res. 194(1), 399–412 (2012)
Schaerf, A.: A survey of automated timetabling. Artif. Intell. Rev. 13(2), 87–127 (1999)
Teoh, C.K., Abdullah, M.Y.C., Haron, H.: Effect of pre-processors on solution quality of university course timetabling problem. In: Proceedings of the 2015 IEEE Student Conference on Research and Development, pp. 472–477 (2015)
Tuga, M., Berretta, R., Mendes, A.: A hybrid simulated annealing with kempe chain neighborhood for the university timetabling problem. In: Proceedings of the 6th IEEE/ACIS International Conference on Computer and Information Science (ICIS 2007), pp. 400–405 (2007)
Acknowledgments
We want to express our thanks to Conselho Nacional de Desenvolvimento Científico e Tecnológico - CNPq (processes 454569/2014-9 and 301725/2016-0) and Fundação de Amparo à Pesquisa e Inovação do Espírito Santo - FAPES (processes 67656021/2014, 67627153/2014, 70232628/2015 and 73290475/2015) for financial support.
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Kampke, E.H., Segatto, E.A., Boeres, M.C.S., Rangel, M.C., Mauri, G.R. (2017). Neighborhood Analysis on the University Timetabling Problem. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2017. ICCSA 2017. Lecture Notes in Computer Science(), vol 10406. Springer, Cham. https://doi.org/10.1007/978-3-319-62398-6_11
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