A Modified Artificial Bee Colony Algorithm for Post-enrolment Course Timetabling

  • Asaju La’aro Bolaji
  • Ahamad Tajudin Khader
  • Mohammed Azmi Al-Betar
  • Mohammed A. Awadallah
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7928)


The post-enrolment course timetabling is concern with assigning a set of courses to a set of rooms and timeslots according to the set of constraints. The problem has been tackled using metaheuristic techniques. Artificial Bee Colony (ABC) algorithm has been successfully used for tackling uncapaciated examination and curriculum based course timetabling problems. In this paper ABC is modified for post-enrolment course timetabling problem. The modification is embedded in the onlooker bee where the multiswap algorithm is used to replace its process. The dataset established by Socha including 5 small, 5 medium and one large dataset are used in the evaluation of the proposed technique. Interestingly, the results obtained is highly competitive when compared with those previously published techniques.


Artificial Bee Colony University Course Timetabling Nature-inspired Computing 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    McCollum, B.: A perspective on bridging the gap between theory and practice in university timetabling. In: Burke, E.K., Rudová, H. (eds.) PATAT 2007. LNCS, vol. 3867, pp. 3–23. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  2. 2.
    Burke, E., Kendall, G., Mısır, M., Özcan, E., Burke, E., Kendall, G., Özcan, E., Mısır, M.: Applications to timetabling. In: Handbook of Graph Theory, ch.5.6. Citeseer (2004)Google Scholar
  3. 3.
    Daskalaki, S., Birbas, T., Housos, E.: An integer programming formulation for a case study in university timetabling. European Journal of Operational Research 153(1), 117–135 (2004)MathSciNetzbMATHCrossRefGoogle Scholar
  4. 4.
    Burke, E., Petrovic, S., Qu, R.: Case-based heuristic selection for timetabling problems. Journal of Scheduling 9(2), 115–132 (2006)zbMATHCrossRefGoogle Scholar
  5. 5.
    Aladag, C., Hocaoglu, G., Basaran, M.: The effect of neighborhood structures on tabu search algorithm in solving course timetabling problem. Expert Systems with Applications 36(10), 12349–12356 (2009)CrossRefGoogle Scholar
  6. 6.
    Kostuch, P.: The university course timetabling problem with a three-phase approach. In: Burke, E.K., Trick, M.A. (eds.) PATAT 2004. LNCS, vol. 3616, pp. 109–125. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  7. 7.
    Abdullah, S., Shaker, K., McCollum, B., McMullan, P.: Incorporating great deluge with kempe chain neighbourhood structure for the enrolment-based course timetabling problem. In: Yu, J., Greco, S., Lingras, P., Wang, G., Skowron, A. (eds.) RSKT 2010. LNCS, vol. 6401, pp. 70–77. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  8. 8.
    Abdullah, S., Burke, E., Mccollum, B.: An investigation of variable neighbourhood search for university course timetabling. In: The 2nd Multidisciplinary International Conference on Scheduling: Theory and Applications (MISTA 2005), pp. 413–427 (2005)Google Scholar
  9. 9.
    Socha, K., Knowles, J.D., Sampels, M.: A \(\cal{MAX-MIN}\) ant system for the university course timetabling problem. In: Dorigo, M., Di Caro, G.A., Sampels, M. (eds.) ANTS 2002. LNCS, vol. 2463, pp. 1–13. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  10. 10.
    Yang, S., Jat, S.: Genetic algorithms with guided and local search strategies for university course timetabling. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews 41(1), 93–106 (2011)CrossRefGoogle Scholar
  11. 11.
    Al-Betar, M.A., Khader, A.T., Zaman, M.: University Course Timetabling Using a Hybrid Harmony Search Metaheuristic Algorithm. IEEE Transactions on Systems, Man, and Cybernetics — Part C: Applications and Reviews (2011), doi:10.1109/TSMCC.2011.2174356Google Scholar
  12. 12.
    Irene, S., Deris, S., Zaiton, M.: A study on PSO-based university course timetabling problem. In: International Conference on Advanced Computer Control, pp. 648–651. IEEE (2009)Google Scholar
  13. 13.
    Abdullah, S., Turabieh, H., McCollum, B., McMullan, P.: A hybrid metaheuristic approach to the university course timetabling problem. Journal of Heuristics, 1–23 (2012)Google Scholar
  14. 14.
    Lewis, R.: A survey of metaheuristic-based techniques for university timetabling problems. OR Spectrum 30(1), 167–190 (2008)zbMATHCrossRefGoogle Scholar
  15. 15.
    McCollum, B., Schaerf, A., Paechter, B., McMullan, P., Lewis, R., Parkes, A., Gaspero, L., Qu, R., Burke, E.: Setting the research agenda in automated timetabling: The second international timetabling competition. INFORMS Journal on Computing 22(1), 120–130 (2010)zbMATHCrossRefGoogle Scholar
  16. 16.
    Bolaji, A., Khader, A., Al-Betar, M., Awadallah, M.: Artificial bee colony, its variants and applications: a survey. Journal of Theoretical and Applied Information Technology (JATIT) 47(2), 1–27 (2013)Google Scholar
  17. 17.
    Bolaji, A., Khader, A., Al-Betar, M., Awadallah, M.: An improved artificial bee colony for course timetabling. In: 2011 Sixth International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA), pp. 9–14. IEEE (2011)Google Scholar
  18. 18.
    Bolaji, A., Khader, A., Al-Betar, M., Awadallah, M.: Artificial bee colony algorithm for solving educational timetabling problems. International Journal of Natural Computing Research 3(2), 1–21 (2012)CrossRefGoogle Scholar
  19. 19.
    Bolaji, A., Khader, A., Al-betar, M., Awadallah, M.: The effect of neighborhood structures on examination timetabling with artificial bee colony. In: Practice and Theory of Automated Timetabling IX, pp. 131–144 (2012)Google Scholar
  20. 20.
    Karaboga, D.: An idea based on honey bee swarm for numerical optimization. Techn. Rep. TR06, Erciyes Univ. Press, Erciyes (2005)Google Scholar
  21. 21.
    Teodorović, D., DellOrco, M.: Bee colony optimization–a cooperative learning approach to complex transportation problems. In: Advanced OR and AI Methods in Transportation. Proceedings of the 10th Meeting of the EURO Working Group on Transportation, pp. 51–60. Citeseer, Poznan (2005)Google Scholar
  22. 22.
    Akay, B., Karaboga, D.: Solving integer programming problems by using artificial bee colony algorithm. In: Serra, R., Cucchiara, R. (eds.) AI*IA 2009. LNCS, vol. 5883, pp. 355–364. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  23. 23.
    Arani, T., Lotfi, V.: A three phased approach to final exam scheduling. IIE Transactions 21(1), 86–96 (1989)CrossRefGoogle Scholar
  24. 24.
    Carter, M., Laporte, G., Lee, S.: Examination timetabling: Algorithmic strategies and applications. Journal of the Operational Research Society 47(3), 373–383 (1996)Google Scholar
  25. 25.
    Al-Betar, M., Khader, A.: A harmony search algorithm for university course timetabling. Annals of Operations Research, 1–29 (2012), doi:10.1007/s10479-010-0769-zGoogle Scholar
  26. 26.
    Al-Betar, M., Khader, A., Muslih, O.: A multiswap algorithm for the university course timetabling problem. In: 2012 International Conference on Computer & Information Science (ICCIS), vol. 1, pp. 301–306. IEEE (2012)Google Scholar
  27. 27.
    Abdullah, S., Burke, E., McCollum, B.: A hybrid evolutionary approach to the university course timetabling problem. In: IEEE Congress on Evolutionary Computation, CEC 2007, pp. 1764–1768. IEEE (2007)Google Scholar
  28. 28.
    Landa-Silva, D., Obit, J.H.: Evolutionary non-linear great deluge for university course timetabling. In: Corchado, E., Wu, X., Oja, E., Herrero, Á., Baruque, B. (eds.) HAIS 2009. LNCS, vol. 5572, pp. 269–276. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  29. 29.
    Jat, S., Yang, S.: A guided search genetic algorithm for the university course timetabling problem. In: The 4th Multidisciplinary International Scheduling Conference: Theory and Applications (MISTA 2009), Dublin, Ireland, August 10-12, pp. 180–191 (2009)Google Scholar
  30. 30.
    Ayob, M., Jaradat, G.: Hybrid ant colony systems for course timetabling problems. In: 2nd Conference on Data Mining and Optimization, DMO 2009, pp. 120–126. IEEE (2009)Google Scholar
  31. 31.
    Turabieh, H., Abdullah, S., McCollum, B.: Electromagnetism-like mechanism with force decay rate great deluge for the course timetabling problem. In: Wen, P., Li, Y., Polkowski, L., Yao, Y., Tsumoto, S., Wang, G. (eds.) RSKT 2009. LNCS, vol. 5589, pp. 497–504. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  32. 32.
    Abdullah, S., Turabieh, H.: Generating university course timetable using genetic algorithms and local search. In: Third International Conference onConvergence and Hybrid Information Technology, ICCIT 2008, vol. 1, pp. 254–260. IEEE (2008)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Asaju La’aro Bolaji
    • 1
    • 2
  • Ahamad Tajudin Khader
    • 1
  • Mohammed Azmi Al-Betar
    • 1
    • 3
  • Mohammed A. Awadallah
    • 1
  1. 1.School of Computer SciencesUniversiti Sains MalaysiaPenangMalaysia
  2. 2.Department of Computer ScienceUniversity of IlorinIlorinNigeria
  3. 3.Department of Computer ScienceJadara UniversityIrbidJordan

Personalised recommendations