Skip to main content
SpringerLink
Log in
Book cover

Evolutionary Scheduling pp 237–272Cite as

Metaheuristics for University Course Timetabling

  • Chapter

Part of the book series: Studies in Computational Intelligence ((SCI,volume 49))

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

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   129.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. http://www.idsia.ch/Files/ttcomp2002/

  2. http://www.emergentcomputing.org/timetabling/harderinstances

  3. D. Abramson, “Constructing School Timetables using Simulated Annealing: Sequential and Parallel Algorithms, ”Management Science, vol. 37, pp. 98-113, 1991.

    Article  Google Scholar 

  4. D. Abramson, H. Krishnamoorthy, and H. Dang,“ Simulated Annealing Cool-ing Schedules for the School Timetabling Problem, ”Asia-Pacific Journal of Operational Research, vol. 16, pp. 1-22, 1996.

    MathSciNet  Google Scholar 

  5. H. Arntzen and A. Løkketangen, “A Tabu Search Heuristic for a University Timetabling Problem, ”in Metaheuristics: Progress as Real Problem Solvers, vol. 32, Computer Science Interfaces Series, T Ikabaki, K. Nonobe, and M. Yagiura, Eds. Berlin: Springer-Verlag, 2005, pp. 65-86.

    Chapter  Google Scholar 

  6. M. Birattari, T. Stützle, L. Paquete, and K. Varrentrapp, “A Racing Algorithm for Configuring Metaheuristics, ”presented at The Genetic and Evolutionary Computation Conference (GECCO) 2002, New York, 2002.

    Google Scholar 

  7. D. Brelaz, “New methods to color the vertices of a graph, ”Commun. ACM, vol. 22, pp. 251-256, 1979.

    Article  MATH  MathSciNet  Google Scholar 

  8. E. Burke, D. Elliman, and R. Weare, “Specialised Recombinative Operators for Timetabling Problems,” in The Artificial Intelligence and Simulated Behaviour Workshop on Evolutionary Computing, vol. 993, Lecture Notes in Computer Science. Berlin: Springer-Verlag, 1995, pp. 75-85.

    Google Scholar 

  9. E. Burke, D. Elliman, and R. Weare, “The Automation of the Timetabling Process in Higher Education, ”Journal of Education Technology Systems, vol. 23, pp. 257-266, 1995.

    Google Scholar 

  10. E. Burke, D. Elliman, and R. Weare, “A Hybrid Genetic Algorithm for Highly Constrained Timetabling Problems., ” presented at Genetic Algo-rithms: Proceedings of the Sixth International Conference (ICGA95), 1995.

    Google Scholar 

  11. E. Burke and M. Petrovic, “Recent Research Directions in Automated Time-tabling, ”European Journal of Operational Research, vol. 140, pp. 266-280, 2002.

    Article  MATH  Google Scholar 

  12. E. K. Burke, D. G. Elliman, P. H. Ford, and R. Weare, “Examination Time- tabling in British Universities: A Survey, ”in Practice and Theory of Auto-mated Timetabling (PATAT) I, vol. 1153, Lecture Notes in Computer Science, E. Burke and P. Ross, Eds. Berlin: Springer-Verlag, 1996, pp. 76-92.

    Google Scholar 

  13. E. K. Burke and J. P. Newall, “A Multi-Stage Evolutionary Algorithm for the Timetable Problem, ”IEEE Transactions on Evolutionary Computation, vol. 3, pp. 63-74, 1999.

    Article  Google Scholar 

  14. M. Carter, “A Survey of Practical Applications of Examination Timetabling Algorithms, ”Operations Research, vol. 34, pp. 193-202, 1986.

    Article  MathSciNet  Google Scholar 

  15. M. Carter and G. Laporte, “Recent Developments in Practical Examination Timetabling, ”in Practice and Theory of Automated Timetabling (PATAT) I, vol. 1153, E. Burke and P. Ross, Eds. Berlin: Springer-Verlag, 1996, pp. 3-21.

    Google Scholar 

  16. M. Carter, G. Laporte, and S. Y. Lee, “Examination Timetabling: Algorithmic Strategies and Applications, ”Journal of the Operational Research Society, vol. 47, pp. 373-383, 1996.

    Article  Google Scholar 

  17. M. Carter and G. Laporte, “Recent Developments in Practical Course Time- tabling, ”in Practice and Theory of Automated Timetabling (PATAT) II, vol. 1408, Lecture Notes in Computer Science, E. Burke and M. Carter, Eds. Ber-lin: Springer-Verlag, 1998, pp. 3-19.

    Google Scholar 

  18. S. Casey and J. Thompson, “GRASPing the Examination Scheduling Prob-lem, ”in Practice and Theory of Automated Timetabling (PATAT) IV, vol. 2740, Lecture Notes in Computer Science, E. Burke and P. De Causmaecker, Eds. Berlin: Springer-Verlag, 2002, pp. 233-244.

    Google Scholar 

  19. M. Chiarandini, K. Socha, M. Birattari, and O. Rossi-Doria, “An Effective Hybrid Approach for the University Course Timetabling Problem, ”Technical Report AIDA-2003-05, FG Intellektik, FB Informatik, TU Darmstadt, Ger-many, 2003.

    Google Scholar 

  20. A. Colorni, M. Dorigo, and V. Maniezzo, “Metaheuristics for high-school timetabling, ”Computational Optimization and Applications, vol. 9, pp. 277-298, 1997.

    Google Scholar 

  21. T. Cooper and J. Kingston, “The Complexity of Timetable Construction Problems, ”in Practice and Theory of Automated Timetabling (PATAT ) I, vol. 1153, Lecture Notes in Computer Science, E. Burke and P. Ross, Eds. Berlin: Springer-Verlag, 1996, pp. 283-295.

    Google Scholar 

  22. D. Corne, P. Ross, and H. Fang, “Evolving Timetables, ”in The Practical Handbook of Genetic Algorithms, vol. 1, L. C. Chambers, Ed.: CRC Press, 1995, pp. 219-276.

    Google Scholar 

  23. P. Cote, T. Wong, and R. Sabourin, “Application of a Hybrid Multi-Objective Evolutionary Algorithm to the Uncapacitated Exam Proximity Problem, ”in Practice and Theory of Automated Timetabling (PATAT) V, vol. 3616, Lec-ture Notes in Computer Science, E. Burke and M. Trick, Eds. Berlin: Springer-Verlag, 2005, pp. 294-312.

    Chapter  Google Scholar 

  24. P. Cowling, S. Ahmadi, P. Cheng, and R. Barone, “Combining Human and Machine Intelligence to Produce Effective Examination Timetables, ” pre-sented at The Forth Asia-Pacific Conference on Simulated Evolution and Learning (SEAL2002), Singapore, 2002.

    Google Scholar 

  25. A. E. Eiben, J. K. van der Hauw, and J. I. van Hemert, “Graph Coloring with Adaptive Evolutionary Algorithms, ”Journal of Heuristics, vol. 4, pp. 25-46, 1998.

    Article  MATH  Google Scholar 

  26. S. Elmohamed, G. Fox, and P. Coddington, “A Comparison of Annealing Techniques for Academic Course Scheduling, ”in Practice and Theory of Automated Timetabling (PATAT) II, vol. 1408, Lecture Notes in Computer Science, E. Burke and M. Carter, Eds. Berlin: Springer-Verlag, 1998, pp. 146-166.

    Google Scholar 

  27. E. Erben, “A Grouping Genetic Algorithm for Graph Colouring and Exam Timetabling, ”in Practice and Theory of Automated Timetabling (PATAT) III, vol. 2079, Lecture Notes in Computer Science, E. Burke and W. Erben, Eds. Berlin: Springer-Verlag, 2001, pp. 132-158.

    Chapter  Google Scholar 

  28. S. Even, A. Itai, and A. Shamir, “On the complexity of Timetable and Multi- commodity Flow Problems, ”SIAM Journal of Computing, vol. 5, pp. 691- 703, 1976.

    Article  MATH  MathSciNet  Google Scholar 

  29. E. Falkenauer, Genetic Algorithms and Grouping Problems: John Wiley and Sons, 1998.

    Google Scholar 

  30. S. Kirkpatrick, C. Gelatt, and M. Vecchi, “Optimization by Simulated An-nealing, ”Science, pp. 671-680, 1983.

    Google Scholar 

  31. P. Kostuch, “The University Course Timetabling Problem with a 3-Phase Approach, ”in Practice and Theory of Automated Timetabling (PATAT) V, vol. 3616, Lecture Notes in Computer Science, E. Burke and M. Trick, Eds. Berlin: Springer-Verlag, 2005, pp. 109-125.

    Chapter  Google Scholar 

  32. R. Lewis and B. Paechter, “New Crossover Operators for Timetabling with Evolutionary Algorithms, ”presented at The Fifth International Conference on Recent Advances in Soft Computing RASC2004, Nottingham, England, 2004.

    Google Scholar 

  33. R. Lewis and B. Paechter, “Application of the Grouping Genetic Algorithm to University Course Timetabling, ”in Evolutionary Computation in Combinato rial Optimization (EvoCop), vol. 3448, Lecture Notes in Computer Science, G. Raidl and J. Gottlieb, Eds. Berlin: Springer-Verlag, 2005, pp. 144-153.

    Google Scholar 

  34. R. Lewis and B. Paechter, “An Empirical Analysis of the Grouping Genetic Algorithm: The Timetabling Case, ”presented at the IEEE Congress on Evo-lutionary Computation (IEEE CEC) 2005, Edinburgh, Scotland, 2005.

    Google Scholar 

  35. R. Lewis and B. Paechter, “Finding Feasible Timetables using Group Based Operators,” (Forthcoming) Accepted for publication in the IEEE Trans. Evo-lutionary Computation, 2006.

    Google Scholar 

  36. Z. Michalewicz, “The Significance of the Evaluation Function in Evolution- ary Algorithms,” presented at The Workshop on Evolutionary Algorithms, Institute for Mathematics and Its Applications, University of Minnesota, Minneapolis, Minnesota, 1998.

    Google Scholar 

  37. B. Paechter, R. Rankin, A. Cumming, and T. Fogarty, “Timetabling the Classes of an Entire University with an Evolutionary Algorithm, ”in Parallel Problem Solving from Nature (PPSN) V, vol. 1498, Lecture Notes in Com-puter Science, T. Baeck, A. Eiben, M. Schoenauer, and H. Schwefel, Eds. Berlin: Springer-Verlag, 1998, pp. 865-874.

    Google Scholar 

  38. J. T. Richardson, M. R. Palmer, G. Liepins, and M. Hilliard, “Some Guide-lines for Genetic Algorithms with Penalty Functions., ”in the Third International Conference on Genetic Algorithms, J. D. Schaffer, Ed. San Francisco, CA, USA: Morgan Kaufmann Publishers Inc, 1989, pp. 191-197.

    Google Scholar 

  39. P. Ross, D. Corne, and H.-L. Fang, “Improving Evolutionary Timetabling with Delta Evaluation and Directed Mutation, ”in Parallel Problem Solving from Nature (PPSN) III, vol. 866, Lecture Notes in Computer Science, Y. Davidor, H. Schwefel, and M. Reinhard, Eds. Berlin: Springer-Verlag, 1994, pp. 556-565.

    Google Scholar 

  40. P. Ross, D. Corne, and H. Terashima-Marin, “The Phase-Transition Niche for Evolutionary Algorithms in Timetabling, ”in Practice and Theory of Auto mated Timetabling (PATAT) I, vol. 1153, Lecture Notes in Computer Science, E. Burke and P. Ross, Eds. Berlin: Springer-Verlag, 1996, pp. 309-325.

    Google Scholar 

  41. P. Ross, E. Hart, and D. Corne, “Genetic Algorithms and Timetabling, ”in Advances in Evolutionary Computing: Theory and Applications, A. Ghosh and K. Tsutsui, Eds.: Springer-Verlag, New York., 2003, pp. 755-771.

    Google Scholar 

  42. O. Rossi-Doria, J. Knowles, M. Sampels, K. Socha, and B. Paechter, “A Lo-cal Search for the Timetabling Problem, ”presented at Practice And Theory of Automated Timetabling (PATAT) IV, Gent, Belgium, 2002.

    Google Scholar 

  43. O. Rossi-Doria, M. Samples, M. Birattari, M. Chiarandini, J. Knowles, M. Manfrin, M. Mastrolilli, L. Paquete, B. Paechter, and T. Stützle, “A Compari-son of the Performance of Different Metaheuristics on the Timetabling Prob-lem, ”in Practice and Theory of Automated Timetabling (PATAT) IV, vol. 2740, Lecture Notes in Computer Science, E. Burke and P. De Causmaecker, Eds. Berlin: Springer-Verlag, 2002, pp. 329-351.

    Google Scholar 

  44. W. Salwach, “Genetic Algorithms in Solving Constraint Satisfaction Prob- lems: The Timetabling Case, ”Badania Operacyjne i Decyzje, 1997.

    Google Scholar 

  45. A. Schaerf, “Tabu Search Techniques for Large High-School Timetabling Problems, ”in Proceedings of the Thirteenth National Conference on Artificial Intelligence. Portland (OR): AAAI Press/ MIT Press, 1996, pp. 363-368.

    Google Scholar 

  46. A. Schaerf, “A Survey of Automated Timetabling,” Artificial Intelligence Review, vol. 13, pp. 87-127, 1999.

    Article  Google Scholar 

  47. K. Socha and M. Samples, “Ant Algorithms for the University Course Time- tabling Problem with Regard to the State-of-the-Art, ”in Evolutionary Com-putation in Combinatorial Optimization (EvoCOP 2003), vol. 2611, Lecture Notes in Computer Science. Berlin: Springer-Verlag, 2003, pp. 334-345.

    Google Scholar 

  48. H. Terashima-Marin, P. Ross, and M. Valenzuela-Rendon, “Evolution of Constraint Satisfaction Strategies in Examination Timetabling, ”presented at The Genetic and Evolutionary Computation Conference (GECCO), 2000.

    Google Scholar 

  49. J. M. Thompson and K. A. Dowsland, “A Robust Simulated Annealing based Examination Timetabling System, ”Computers and Operations Research, vol. 25, pp. 637-648, 1998.

    Article  MATH  Google Scholar 

  50. P. van Laarhoven and E. Aarts, Simulated Annealing: Theory and Applica-tions. Reidel, The Netherlands: Kluwer Academic Publishers, 1987.

    Google Scholar 

  51. G. White and W. Chan, “Towards the Construction of Optimal Examination Schedules, ”INFOR, vol. 17, pp. 219-229, 1979.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Centre for Emergent Computing, Napier University, EH10 5DT, Edinburgh, Scotland

    Rhydian Lewis

  2. Centre for Emergent Computing, Napier University, EH10 5DT, Edinburgh, Scotland

    Ben Paechter

  3. Dipartimento di Matematica Pura ed Applicata, Univesita' degli Studi di Padova, via G. Belzoni 7, 35131, Padua, Italy

    Olivia Rossi-Doria

Authors
  1. Rhydian Lewis
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ben Paechter
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Olivia Rossi-Doria
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Modeling Optimisation Scheduling and Intelligent Control (MOSAIC), Research Centre, Department of Computing, University of Bradford, West Yorkshire, BD7 1DP, Bradford, UK

    Keshav P. Dahal

  2. Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, Singapore, Singapore

    Kay Chen Tan

  3. Modeling Optimisation Scheduling and Intelligent Control (MOSAIC), Research Centre, Department of Computing, University of Bradford, West Yorkshire, BD7 1DP, Bradford, UK

    Peter I. Cowling

Rights and permissions

Reprints and permissions

Copyright information

© 2007 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Lewis, R., Paechter, B., Rossi-Doria, O. (2007). Metaheuristics for University Course Timetabling. In: Dahal, K.P., Tan, K.C., Cowling, P.I. (eds) Evolutionary Scheduling. Studies in Computational Intelligence, vol 49. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-48584-1_9

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-48584-1_9

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-48582-7

  • Online ISBN: 978-3-540-48584-1

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation