IDEAS home Printed from https://ideas.repec.org/a/spr/annopr/v194y2012i1p3-3110.1007-s10479-010-0769-z.html
   My bibliography  Save this article

A harmony search algorithm for university course timetabling

Author

Listed:
  • Mohammed Al-Betar
  • Ahamad Khader

Abstract

One of the main challenges for university administration is building a timetable for course sessions. This is not just about building a timetable that works, but building one that is as good as possible. In general, course timetabling is the process of assigning given courses to given rooms and timeslots under specific constraints. Harmony search algorithm is a new metaheuristic population-based algorithm, mimicking the musical improvisation process where a group of musicians play the pitches of their musical instruments together seeking a pleasing harmony. The major thrust of this algorithm lies in its ability to integrate the key components of population-based methods and local search-based methods in a simple optimization model. In this paper, a harmony search and a modified harmony search algorithm are applied to university course timetabling against standard benchmarks. The results show that the proposed methods are capable of providing viable solutions in comparison to previous works. Copyright Springer Science+Business Media, LLC 2012

Suggested Citation

  • Mohammed Al-Betar & Ahamad Khader, 2012. "A harmony search algorithm for university course timetabling," Annals of Operations Research, Springer, vol. 194(1), pages 3-31, April.
    • Handle: RePEc:spr:annopr:v:194:y:2012:i:1:p:3-31:10.1007/s10479-010-0769-z
    DOI: 10.1007/s10479-010-0769-z
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s10479-010-0769-z
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s10479-010-0769-z?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Burke, Edmund Kieran & Petrovic, Sanja, 2002. "Recent research directions in automated timetabling," European Journal of Operational Research, Elsevier, vol. 140(2), pages 266-280, July.
    2. Burke, Edmund K. & McCollum, Barry & Meisels, Amnon & Petrovic, Sanja & Qu, Rong, 2007. "A graph-based hyper-heuristic for educational timetabling problems," European Journal of Operational Research, Elsevier, vol. 176(1), pages 177-192, January.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Assif Assad & Kusum Deep, 2018. "Harmony search based memetic algorithms for solving sudoku," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 9(4), pages 741-754, August.
    2. Wei Sun & Yujun He & Hong Chang, 2015. "Forecasting Fossil Fuel Energy Consumption for Power Generation Using QHSA-Based LSSVM Model," Energies, MDPI, vol. 8(2), pages 1-21, January.
    3. Johnes, Jill, 2015. "Operational Research in education," European Journal of Operational Research, Elsevier, vol. 243(3), pages 683-696.
    4. F. Paola & M. Giugni & F. Pugliese & P. Romano, 2018. "Optimal Design of LIDs in Urban Stormwater Systems Using a Harmony-Search Decision Support System," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(15), pages 4933-4951, December.
    5. MinJun Kim & JuneSeok Hong & Wooju Kim, 2019. "An Efficient Representation Using Harmony Search for Solving the Virtual Machine Consolidation," Sustainability, MDPI, vol. 11(21), pages 1-20, October.
    6. Hasan, Basima Hani F. & Abu Doush, Iyad & Al Maghayreh, Eslam & Alkhateeb, Faisal & Hamdan, Mohammad, 2014. "Hybridizing Harmony Search algorithm with different mutation operators for continuous problems," Applied Mathematics and Computation, Elsevier, vol. 232(C), pages 1166-1182.
    7. Mohammed Al-Betar & Ahamad Khader & Iyad Doush, 2014. "Memetic techniques for examination timetabling," Annals of Operations Research, Springer, vol. 218(1), pages 23-50, July.
    8. Abha Sharma & Pushpendra Kumar & Kanojia Sindhuben Babulal & Ahmed J. Obaid & Harshita Patel, 2022. "Categorical Data Clustering Using Harmony Search Algorithm for Healthcare Datasets," International Journal of E-Health and Medical Communications (IJEHMC), IGI Global, vol. 13(4), pages 1-15, August.
    9. R. A. Oude Vrielink & E. A. Jansen & E. W. Hans & J. Hillegersberg, 2019. "Practices in timetabling in higher education institutions: a systematic review," Annals of Operations Research, Springer, vol. 275(1), pages 145-160, April.
    10. Lahasan, Badr Mohammed & Venkat, Ibrahim & Al-Betar, Mohammed Azmi & Lutfi, Syaheerah Lebai & Wilde, Philippe De, 2016. "Recognizing faces prone to occlusions and common variations using optimal face subgraphs," Applied Mathematics and Computation, Elsevier, vol. 283(C), pages 316-332.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Song, Kwonsik & Kim, Sooyoung & Park, Moonseo & Lee, Hyun-Soo, 2017. "Energy efficiency-based course timetabling for university buildings," Energy, Elsevier, vol. 139(C), pages 394-405.
    2. De Causmaecker, Patrick & Demeester, Peter & Vanden Berghe, Greet, 2009. "A decomposed metaheuristic approach for a real-world university timetabling problem," European Journal of Operational Research, Elsevier, vol. 195(1), pages 307-318, May.
    3. Christine Mumford, 2010. "A multiobjective framework for heavily constrained examination timetabling problems," Annals of Operations Research, Springer, vol. 180(1), pages 3-31, November.
    4. R Qu & E K Burke, 2009. "Hybridizations within a graph-based hyper-heuristic framework for university timetabling problems," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(9), pages 1273-1285, September.
    5. Kahar, M.N.M. & Kendall, G., 2010. "The examination timetabling problem at Universiti Malaysia Pahang: Comparison of a constructive heuristic with an existing software solution," European Journal of Operational Research, Elsevier, vol. 207(2), pages 557-565, December.
    6. Thepphakorn, Thatchai & Pongcharoen, Pupong & Hicks, Chris, 2014. "An ant colony based timetabling tool," International Journal of Production Economics, Elsevier, vol. 149(C), pages 131-144.
    7. G N Beligiannis & C Moschopoulos & S D Likothanassis, 2009. "A genetic algorithm approach to school timetabling," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(1), pages 23-42, January.
    8. Sabar, Nasser R. & Ayob, Masri & Kendall, Graham & Qu, Rong, 2012. "A honey-bee mating optimization algorithm for educational timetabling problems," European Journal of Operational Research, Elsevier, vol. 216(3), pages 533-543.
    9. Burke, E.K. & Eckersley, A.J. & McCollum, B. & Petrovic, S. & Qu, R., 2010. "Hybrid variable neighbourhood approaches to university exam timetabling," European Journal of Operational Research, Elsevier, vol. 206(1), pages 46-53, October.
    10. Johnes, Jill, 2015. "Operational Research in education," European Journal of Operational Research, Elsevier, vol. 243(3), pages 683-696.
    11. De Boeck, Liesje & Beliën, Jeroen & Creemers, Stefan, 2016. "A column generation approach for solving the examination-timetabling problemAuthor-Name: Woumans, Gert," European Journal of Operational Research, Elsevier, vol. 253(1), pages 178-194.
    12. Pillay, N. & Banzhaf, W., 2009. "A study of heuristic combinations for hyper-heuristic systems for the uncapacitated examination timetabling problem," European Journal of Operational Research, Elsevier, vol. 197(2), pages 482-491, September.
    13. Raphael Medeiros Alves & Francisco Cunha & Anand Subramanian & Alisson V. Brito, 2022. "Minimizing energy consumption in a real-life classroom assignment problem," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 44(4), pages 1149-1175, December.
    14. Edmund Burke & Graham Kendall & Mustafa Mısır & Ender Özcan, 2012. "Monte Carlo hyper-heuristics for examination timetabling," Annals of Operations Research, Springer, vol. 196(1), pages 73-90, July.
    15. Martin Geiger, 2012. "Applying the threshold accepting metaheuristic to curriculum based course timetabling," Annals of Operations Research, Springer, vol. 194(1), pages 189-202, April.
    16. Zhang, Defu & Liu, Yongkai & M'Hallah, Rym & Leung, Stephen C.H., 2010. "A simulated annealing with a new neighborhood structure based algorithm for high school timetabling problems," European Journal of Operational Research, Elsevier, vol. 203(3), pages 550-558, June.
    17. Soria-Alcaraz, Jorge A. & Ochoa, Gabriela & Swan, Jerry & Carpio, Martin & Puga, Hector & Burke, Edmund K., 2014. "Effective learning hyper-heuristics for the course timetabling problem," European Journal of Operational Research, Elsevier, vol. 238(1), pages 77-86.
    18. Qu, Rong & Burke, Edmund K. & McCollum, Barry, 2009. "Adaptive automated construction of hybrid heuristics for exam timetabling and graph colouring problems," European Journal of Operational Research, Elsevier, vol. 198(2), pages 392-404, October.
    19. Yuri Bykov & Sanja Petrovic, 2016. "A Step Counting Hill Climbing Algorithm applied to University Examination Timetabling," Journal of Scheduling, Springer, vol. 19(4), pages 479-492, August.
    20. Edmund K. Burke & Yuri Bykov, 2016. "An Adaptive Flex-Deluge Approach to University Exam Timetabling," INFORMS Journal on Computing, INFORMS, vol. 28(4), pages 781-794, November.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:annopr:v:194:y:2012:i:1:p:3-31:10.1007/s10479-010-0769-z. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.