Skip to main content

Advertisement

SpringerLink
Log in

A hybrid genetic algorithm and tabu search approach for post enrolment course timetabling

  • Published:
Journal of Scheduling Aims and scope Submit manuscript

Abstract

The post enrolment course timetabling problem (PECTP) is one type of university course timetabling problems, in which a set of events has to be scheduled in time slots and located in suitable rooms according to the student enrolment data. The PECTP is an NP-hard combinatorial optimisation problem and hence is very difficult to solve to optimality. This paper proposes a hybrid approach to solve the PECTP in two phases. In the first phase, a guided search genetic algorithm is applied to solve the PECTP. This guided search genetic algorithm, integrates a guided search strategy and some local search techniques, where the guided search strategy uses a data structure that stores useful information extracted from previous good individuals to guide the generation of offspring into the population and the local search techniques are used to improve the quality of individuals. In the second phase, a tabu search heuristic is further used on the best solution obtained by the first phase to improve the optimality of the solution if possible. The proposed hybrid approach is tested on a set of benchmark PECTPs taken from the international timetabling competition in comparison with a set of state-of-the-art methods from the literature. The experimental results show that the proposed hybrid approach is able to produce promising results for the test PECTPs.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  • Abdullah, S., & Turabieh, H. (2008). Generating university course timetable using genetic algorithm and local search. In Proc. of the 3rd int. conf. on hybrid information technology (pp. 254–260).

    Google Scholar 

  • Abdullah, S., Burke, E. K., & McCollum, B. (2005). An investigation of variable neighbourhood search for university course timetabling. In Proc. of the 2nd multidisciplinary conference on scheduling: theory and applications (pp. 413–427).

    Google Scholar 

  • Abdullah, S., Burke, E. K., & McCollum, B. (2007). Using a randomised iterative improvement algorithm with composite neighbourhood structures. In Proc. of the 6th int. conf. on metaheuristic (pp. 153–169).

    Google Scholar 

  • Abdullah, S., Turabieh, H., McCollum, B., & McMullan, P. (2010a). A multi-objective post enrolment course timetabling problems: a new case study. In IEEE congress on evolutionary computation, Barcelona, Spain.

    Google Scholar 

  • Abdullah, S., Shaker, K., McCollum, B., & McMullan, P. (2010b). Incorporating great deluge with Kempe chain neighbourhood structure for the enrolment-based course timetabling problem. In LNAI : Vol. 6401. The fifth international conference on rough set and knowledge technology (pp. 70–77).

    Chapter  Google Scholar 

  • Abramson, D. (1991). Constructing school timetables using simulated annealing: sequential and parallel algorithms. Management Science, 37(1), 98–113.

    Article  Google Scholar 

  • Acan, A. (2004). An external memory implementation in ant colony optimisation. In Proc. of the 4th int. workshop on ant colony optimisation and swarm intelligence (ANTS 2004) (pp. 73–82).

    Chapter  Google Scholar 

  • Acan, A., & Tekol, Y. (2003). Chromosome reuse in genetic algorithms. In Proc. of the 2003 genetic and evolutionary computation conference (GECCO 2003) (pp. 695–705).

    Google Scholar 

  • Aladğ, Ç. H., & Hocaoğlu, G. (2007). A tabu search algorithm to solve a course timetabling problem. Hacettepe Journal of Mathematics and Statistics, 36(1), 53–64.

    Google Scholar 

  • Al-Betar, M. A., Khader, A. T., & Gani, A. T. (2007). A harmony search algorithm for university course timetabling. In Proc. of the 7th int. conf. on the practice and theory of automated timetabling.

    Google Scholar 

  • Atkin, J. A., Burke, E. K., Greenwood, J., & Reeson, D. (2007). Hybrid meta-heuristics to aid runway scheduling at London Heathrow airport. Transportation Science, 41(1), 90–106.

    Article  Google Scholar 

  • Atsuta, M., Nonobe, K., & Ibaraki, T. (2008). ITC2007 Track 2, an approach using general csp solver. www.cs.qub.ac.uk/itc2007.

  • Bonissone, P. P., Subbu, R., Eklund, N., & Kiehl, T. R. (2006). Evolutionary algorithms + domain knowledge = real-world evolutionary computation. IEEE Transactions on Evolutionary Computation, 10(3), 256–280.

    Article  Google Scholar 

  • Burke, E. K., & Petrovic, S. (2002). Recent research directions in automated timetabling. European Journal of Operation Research, 140(2), 266–280.

    Article  Google Scholar 

  • Burke, E. K., Elliman, D. G., & Weare, R. F. (1995). A hybrid genetic algorithm for highly constrained timetabling problems. In Proc. of 6th int. conf. on genetic algorithms (pp. 605–610).

    Google Scholar 

  • Burke, E. K., Kendall, G., & Soubeiga, E. (2003). A tabu-search hyper-heuristic for timetabling and rostering. Journal of Heuristics, 9(6), 451–470.

    Article  Google Scholar 

  • Burke, E. K., Causmaecker, P. D., Berghe, G. V., & Landeghem, H. V. (2004). The state of the art of nurse rostering. Journal of Scheduling, 7(6), 441–499.

    Article  Google Scholar 

  • Cambazard, H., Hebrard, E., OŚullivan, B., & Papadopoulos, A. (2008). Local search and constraint programming for the post enrolment-based course timetabling problem. In Proc. of the 7th int. conf. on the practice and theory of automated timetabling (PATAT 2008).

    Google Scholar 

  • Carter, M. W., & Laporte, G. (1998). Recent developments in practical course timetabling. In LNCS : Vol. 1408. Proc. of the 2nd int. conf. on practice and theory of automated timetabling (pp. 3–19).

    Chapter  Google Scholar 

  • Chiarandini, M., Birattari, M., Socha, K., & Rossi-Doria, O. (2006). An effective hybrid algorithm for university course timetabling. Journal of Scheduling, 9(5), 403–432.

    Article  Google Scholar 

  • Chiarandini, M., Fawcett, C., & Hoos, H. H. (2008). A modular multiphase heuristic solver for post enrollment course timetabling. In Proc. of the 7th int. conf. on the practice and theory of automated timetabling (PATAT 2008).

    Google Scholar 

  • Colorni, A., Dorigo, M., & Maniezzo, V. (1990). Genetic algorithms—a new approach to the timetable problem. In LNCS : Vol. F(82). NATO ASI series, combinatorial optimisation (pp. 235–239).

    Google Scholar 

  • Chu, S. C., & Frang, H. L. (1999). Genetic algorithm vs. tabu search in timetabling scheduling. In Proc. of the 3rd int. conf. on knowledge-based intelligent information engineering system.

    Google Scholar 

  • Datta, D., Deb, K., & Fonseca, C. M. (2007). Multi-objective evolutionary algorithm for university class timetabling problem. In K. P. Dahal, K. C. Tan, & P. I. Cowling (Eds.), Evolutionary scheduling (pp. 197–236). Berlin: Springer.

    Chapter  Google Scholar 

  • Erben, W., & Keppler, J. (1995). A genetic algorithm solving a weekly course timetabling problem. In LNCS : Vol. 1153. Proc. of the 1st int. conf. on practice and theory of automated timetabling (pp. 198–211).

    Google Scholar 

  • Even, S., Itai, A., & Shamir, A. (1997). On the complexity of timetable and multicommodity flow problems. SIAM Journal on Computing, 5(4), 691–703.

    Article  Google Scholar 

  • Freisleben, B., & Merz, P. (1996). A genetic local search algorithm for solving symmetric and asymmetric traveling salesman problems. In Proc. of IEEE int. conf. on evolutionary computation (pp. 616–621).

    Chapter  Google Scholar 

  • Gaspero, L. D., & Schaerf, A. (2001). Tabu search techniques for examination timetabling. In LNCS : Vol. 2079. Practice and theory of automated timetabling III (pp. 104–117). Berlin: Springer.

    Chapter  Google Scholar 

  • Gen, M., & Cheng, R. (1997). Genetic algorithms and engineering design. New York: Wiley-IEEE.

    Google Scholar 

  • Glover, F., & Laguna, M. (1997). Tabu search. Dordrecht: Kluwer Academic.

    Book  Google Scholar 

  • Goldberg, D. (1989). Genetic algorithms in search, optimisation and machine learning. Reading: Addison-Wesley.

    Google Scholar 

  • Gotlieb, C. C. (1963). The construction of class-teacher timetables. IFIP Congress, 62, 73–77.

    Google Scholar 

  • Gunadhi, H., Anand, V. J., & Yong, Y. W. (1996). Automated timetabling using an object-oriented scheduler. Expert Systems with Applications, 10(2), 243–256.

    Article  Google Scholar 

  • Hageman, J. A., Wehrens, R., Sprang, H. A., & Buydens, L. M. C. (2003). Hybrid genetic algorithmtabu search approach for optimizing multilayer optical coatings. Analytica Chimica Acta, 490, 211–222.

    Article  Google Scholar 

  • Jat, S. N., & Yang, S. (2008). A memetic algorithm for the university course timetabling problem. In Proc. of the 20th IEEE int. conf. tools with artif. intell. (pp. 427–433).

    Chapter  Google Scholar 

  • Jat, S. N., & Yang, S. (2009). A guided search genetic algorithm for the university course timetabling problem. In Proc. of the 4th multidisciplinary int. scheduling conf: theory and applications (pp. 180–191).

    Google Scholar 

  • Kendall, G., Knust, S., Ribeiro, C. C., & Urrutia, S. (2010). Scheduling in sports: an annotated bibliography. Computers and Operations Research, 37(1), 1–19.

    Article  Google Scholar 

  • Knauer, B. A. (1974). Solutions of a timetable problem. Computers and Operations Research, 1(3), 363–375 –4.

    Article  Google Scholar 

  • Lewis, R. (2008). A survey of metaheuristic based techniques for university timetabling problems. OR Spectrum, 30(1), 167–190.

    Article  Google Scholar 

  • Lewis, R., & Paechter, B. (2005). Application of the grouping genetic algorithm to university course timetabling. In LNCS : Vol. 3448. Proc. of the 5th European conf. on evol. comput. in combinatorial optimisation (EvoCOP 2005) (pp. 144–153).

    Chapter  Google Scholar 

  • Lewis, R., Paechter, B., & McCollum, B. (2007). Post enrolment based course timetabling: a description of the problem model used for track two of the second international timetabling competition. Technical Report, Cardiff University.

  • Liu, Y. H. (2010). Different initial solution generators in genetic algorithms for solving the probabilistic traveling salesman problem. Applied Mathematics and Computation, 216(1), 125–137.

    Article  Google Scholar 

  • Louis, S., & Li, G. (1997). Augmenting genetic algorithms with memory to solve traveling salesman problem. In Proc. of the 1997 joint conference on information sciences (pp. 108–111).

    Google Scholar 

  • Lü, Z., & Hao, J. K. (2010). Adaptive tabu search for course timetabling. European Journal of Operational Research, 200(1), 235–244.

    Article  Google Scholar 

  • Malim, M. R., Khader, A. T., & Mustafa, A. (2006). Artificial immune algorithms for university timetabling. In E. K. Burke & H. Rudova (Eds.), Proc of the 6th int. conf. on practice and theory of automated timetabling (pp. 234–245).

    Google Scholar 

  • Müller, T. (2008). ITC2007 solver description: a hybrid approach. In Proc. of the 7th int. conf. on the practise and theory of automated timetabling (PATAT 2008).

    Google Scholar 

  • Nothegger, C., Mayer, A., Chwatal, A., & Raidl, G. (2008). Solving the post enrolment course timetabling problem by ant colony optimisation. In Proc. of the 7th int. conf. on the practice and theory of automated timetabling (PATAT 2008).

    Google Scholar 

  • Pongcharoen, P., Promtet, W., Yenradee, P., & Hicks, C. (2008). Stochastic optimisation timetabling tool for university course scheduling. International Journal of Production Economics, 112, 903–918.

    Article  Google Scholar 

  • Prestwich, S., Tarim, A., Rossi, R., & Hnich, B. (2008). A steady-state genetic algorithm with resampling for noisy inventory control. In LNCS : Vol. 5199. Proc. of the 10th int conf on parallel problem solving from nature (pp. 559–568).

    Chapter  Google Scholar 

  • Qu, R., Burke, E. K., McCollum, B., & Merlot, L. T. G. (2009). A survey of search methodologies and automated system development for examination timetabling. Journal of Scheduling, 12(1), 55–89.

    Article  Google Scholar 

  • Rossi-Doria, O., & Paechter, B. (2004). A memetic algorithm for university course timetabling. In Proc. of combinatorial optimisation (CO 2004) (p. 56).

    Google Scholar 

  • Rossi-Doria, O., Sampels, M., Birattari, M., Chiarandini, M., Dorigo, M., Gambardella, L., Knowles, J., Manfrin, M., Mastrolilli, M., Paechter, B., Paquete, L., & Stützle, T. (2002). A comparison of the performance of different metaheuristics on the timetabling problem. In Lecture notes in computer science (Vol. 2740, pp. 329–351).

    Google Scholar 

  • Sastry, K., Goldberg, D., & Kendall, G. (2005). Genetic algorithms. In E. K. Burke & G. Kendall (Eds.), Search methodologies: introductory tutorials in optimisation and decision support techniques (pp. 97–125). New York: Springer. Chap. 4.

    Google Scholar 

  • Schearf, A. (1999). A survey of automated timetabling. Artificial Intelligence Review, 13(2), 87–127.

    Article  Google Scholar 

  • Sigl, B., Golub, M., & Mornar, V. (2003). Solving timetable scheduling problem using genetic algorithms. In Proc. of the 25th int. conf. on information technology interfaces (pp. 519–524).

    Google Scholar 

  • Socha, K., Knowles, J., & Samples, M. (2002). A max-min ant system for the university course timetabling problem. In LNCS : Vol. 2463. Proc. of the 3rd int. workshop on ant algorithms (ANTS) (pp. 1–13).

    Google Scholar 

  • Thanh, N. D. (2006). Solving timetabling problem using genetic and heuristics algorithms. Journal of Scheduling, 9(5), 403–432.

    Article  Google Scholar 

  • Tuga, M., Berretta, R., & Mendes, A. (2007). A hybrid simulated annealing with Kempe chain neighborhood for the university timetabling problem. In Proc. of the 6th IEEE/ACIS int. conf. on computer and information science (pp. 400–405).

    Chapter  Google Scholar 

  • Turabieh, H., & Abdullah, S. (2009). Incorporating tabu search into memetic approach for enrolment-based course timetabling problems. In 2nd data mining and optimisation conference (pp. 122–126).

    Google Scholar 

  • Werra, D. (1986). An introduction to timetabling. European Journal of Operational Research, 19(2), 151–162.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science, University of Leicester, University Road, Leicester, LE1 7RH, UK

    Sadaf Naseem Jat

  2. Department of Information Systems and Computing, Brunel University, Uxbridge, Middlesex, UB8 3PH, UK

    Shengxiang Yang

Authors
  1. Sadaf Naseem Jat
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shengxiang Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Shengxiang Yang.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Jat, S.N., Yang, S. A hybrid genetic algorithm and tabu search approach for post enrolment course timetabling. J Sched 14, 617–637 (2011). https://doi.org/10.1007/s10951-010-0202-0

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10951-010-0202-0

Keywords

Search

Navigation