Journal of Heuristics

, Volume 17, Issue 2, pp 97–118 | Cite as

Neighborhood analysis: a case study on curriculum-based course timetabling

Article

Abstract

In this paper, we present an in-depth analysis of neighborhood relations for local search algorithms. Using a curriculum-based course timetabling problem as a case study, we investigate the search capability of four neighborhoods based on three evaluation criteria: percentage of improving neighbors, improvement strength and search steps. This analysis shows clear correlations of the search performance of a neighborhood with these criteria and provides useful insights on the very nature of the neighborhood. This study helps understand why a neighborhood performs better than another one and why and how some neighborhoods can be favorably combined to increase their search power. This study reduces the existing gap between reporting experimental assessments of local search-based algorithms and understanding their behaviors.

Keywords

Neighborhood structure Neighborhood combination Local search Timetabling Metaheuristics 

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References

  1. Burke, E.K., Newall, J.P.: Solving examination timetabling problems through adaptation of heuristic orderings. Ann. Oper. Res. 129, 107–134 (2004) MathSciNetMATHCrossRefGoogle Scholar
  2. Burke, E.K., MacCarthy, B.L., Petrovic, S., Qu, R.: Multiple-retrieval case-based reasoning for course timetabling problems. J. Oper. Res. Soc. 57(2), 148–162 (2006) MATHGoogle Scholar
  3. Casey, S., Thompson, J.: Grasping the examination scheduling problem. In: Burke, E.K., Causmaecker, P.D. (eds.) Proceedings of the 4th PATAT Conference. LNCS, vol. 2740, pp. 232–246. Springer, Berlin (2003) Google Scholar
  4. Chiarandini, M., Birattari, M., Socha, K., Rossi-Doria, O.: An effective hybrid algorithm for university course timetabling. J. Sched. 9, 403–432 (2006) MathSciNetMATHCrossRefGoogle Scholar
  5. Côté, P., Wong, T., Sabourin, R.: Application of a hybrid multi-objective evolutionary algorithm to the uncapacitated exam proximity problem. In: Burke, E.K., Trick, M. (eds.) Proceedings of the 5th PATAT Conference. LNCS, vol. 3616, pp. 151–168. Springer, Berlin (2005) Google Scholar
  6. Di Gaspero, L., Schaerf, A.: Neighborhood portfolio approach for local search applied to timetabling problems. J. Math. Model. Algorithms 5(1), 65–89 (2006) MathSciNetMATHCrossRefGoogle Scholar
  7. Glover, F.: Tabu thresholding: Improved search by nonmonotonic trajectories. ORSA J. Comput. 7(4), 426–442 (1995) MathSciNetMATHGoogle Scholar
  8. Glover, F.: Tabu Search and adaptive memory programming—advances, applications and challenges. In: Interfaces in Computer Science and Operations Research, pp. 1–75. Kluwer Academic, Dordrecht (1996) Google Scholar
  9. Glover, F., Laguna, M.: Tabu Search. Kluwer Academic, Boston (1997) MATHGoogle Scholar
  10. Glover, F., McMillan, C., Glover, R.: A heuristic programming approach to the employee scheduling problem and some thoughts on managerial robots. J. Oper. Manag. 4(2), 113–128 (1984) CrossRefGoogle Scholar
  11. Goëfon, A., Richer, J.M., Hao, J.K.: Progressive tree neighborhood applied to the maximum parsimony problem. IEEE/ACM Trans. Comput. Biol. Bioinform. 5(1), 136–145 (2008) CrossRefGoogle Scholar
  12. Hansen, P., Mladenovi, N.: Variable neighborhood search: principles and applications. Eur. J. Oper. Res. 130(3), 449–467 (2001) MATHCrossRefGoogle Scholar
  13. Hoos, H.H., Stützle, T.: Stochastic Local Search: Foundations and Applications. Morgan Kaufmann, Elsevier, San Francisco (2004) Google Scholar
  14. Johnson, D.S.: A theoretician’s guide to the experimental analysis of algorithms. In: Goldwasser, M.H., Johnson, D.S., McGeoch, C.C. (eds.) Data Structures, Near Neighbor Searches, and Methodology: Fifth and Sixth DIMACS Implementation Challenges, pp. 215–250. American Mathematical Society, Providence (2002) Google Scholar
  15. Lewis, R.: A survey of metaheuristic-based techniques for university timetabling problems. OR Spectrum 30(1), 167–190 (2008) MATHCrossRefGoogle Scholar
  16. Lourenco, H.R., Martin, O., Stützle, T.: Iterated local search. In: Handbook of Meta-heuristics, pp. 321–353. Springer, Berlin (2003) Google Scholar
  17. Lü, Z., Hao, J.K.: A critical element-guided perturbation strategy for iterated local search. In: Cotta, C., Cowling, P. (eds.) EvoCop 2009. LNCS, vol. 5482, pp. 1–12. Springer, Berlin (2009) Google Scholar
  18. Lü, Z., Hao, J.K.: Adaptive tabu search for course timetabling. Eur. J. Oper. Res. 200(1), 235–244 (2010) MATHCrossRefGoogle Scholar
  19. McCollum, B.: A perspective on bridging the gap between research and practice in university timetabling. In: Burke, E.K., Rudova, H. (eds.) Proceedings of the 6th PATAT Conference. LNCS, vol. 3867, pp. 3–23. Springer, Berlin (2007) Google Scholar
  20. McCollum, B., McMullan, P., Paechter, B., Lewis, R., Schaerf, A., Di Gaspero, L., Parkes, A.J., Qu, R., Burke, E.K.: Setting the research agenda in automated timetabling: the second international timetabling competition. Technical Report http://www.cs.qub.ac.uk/itc2007/ITC2007_Background_Techreportv1.pdf (2008)
  21. Merlot, L.T.G., Boland, N., Hughes, B.D., Stuckey, P.J.: A hybrid algorithm for the examination timetabling problem. In: Burke, E.K., Causmaecker, P.D. (eds.) Proceedings of the 4th PATAT Conference. LNCS, vol. 2740, pp. 207–231. Springer, Berlin (2003) Google Scholar
  22. Mlandenovic, N., Hansen, P.: Variable neighbourhood search. Comput. Oper. Res. 24(11), 1097–1100 (1997) MathSciNetCrossRefGoogle Scholar
  23. Müller, T.: Solver description: a hybrid approach. In: Burke, E.K., Gendreau, M. (eds.) Proceedings of the 7th PATAT Conference. http://www.unitime.org/papers/itc2007.pdf (2008)
  24. Papadimitriou, C.H., Steiglitz, K.: Combinatorial Optimization: Algorithms and Complexity. Dover, Mineola (1998) MATHGoogle Scholar
  25. Rossi-Doria, O., Paechter, B., Blum, C., Socha, K., Samples, M.: A local search for the timetabling problem. In: Burke, E.K., Causmaecker, P.D. (eds.) Proceedings of the 4th PATAT Conference. Gent, Belgium (2002) Google Scholar
  26. Schaerf, A.: A survey of automated timetabling. Artif. Intell. Review 13(2), 87–127 (1999) CrossRefGoogle Scholar
  27. Schuurmans, D., Southey, F.: Local search characteristics of incomplete sat procedures. Artif. Intell. 132(2), 121–150 (2001) MathSciNetMATHCrossRefGoogle Scholar
  28. Sedgewick, R.: Algorithms, 2nd edn. Addison-Wesley, Reading (1988) Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  1. 1.LERIAUniversité d’AngersAngers Cedex 01France
  2. 2.OptTek Systems, Inc.BoulderUSA

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