The Teaching Space Allocation Problem with Splitting

  • Camille Beyrouthy
  • Edmund K. Burke
  • Dario Landa-Silva
  • Barry McCollum
  • Paul McMullan
  • Andrew J. Parkes
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3867)

Abstract

A standard problem within universities is that of teaching space allocation which can be thought of as the assignment of rooms and times to various teaching activities. The focus is usually on courses that are expected to fit into one room. However, it can also happen that the course will need to be broken up, or ‘split’, into multiple sections. A lecture might be too large to fit into any one room. Another common example is that of seminars or tutorials. Although hundreds of students may be enrolled on a course, it is often subdivided into particular types and sizes of events dependent on the pedagogic requirements of that particular course.

Typically, decisions as to how to split courses need to be made within the context of limited space requirements. Institutions do not have an unlimited number of teaching rooms, and need to effectively use those that they do have. The efficiency of space usage is usually measured by the overall ‘utilisation’ which is basically the fraction of the available seat-hours that are actually used. A multi-objective optimisation problem naturally arises; with a trade-off between satisfying preferences on splitting, a desire to increase utilisation, and also to satisfy other constraints such as those based on event location and timetabling conflicts. In this paper, we explore such trade-offs. The explorations themselves are based on a local search method that attempts to optimise the space utilisation by means of a ‘dynamic splitting’ strategy. The local moves are designed to improve utilisation and satisfy the other constraints, but are also allowed to split, and un-split, courses so as to simultaneously meet the splitting objectives.

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References

  1. 1.
    Aarts, E., Korst, J.: Simulated Annealing and Boltzman Machines. Wiley, New York (1990)Google Scholar
  2. 2.
    Alvarez-Valdes, R., Crespo, E., Tamarit, J.: Assigning students to course sections using tabu search. Annals of Operations Research 96, 1–16 (2000)MATHCrossRefGoogle Scholar
  3. 3.
    AminToosi, M., Yazdi, H.S., Haddadnia, J.: Feature selection in a fuzzy student sectioning algorithm. In: Burke, E.K., Trick, M.A. (eds.) PATAT 2004. LNCS, vol. 3616, pp. 147–160. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  4. 4.
    Bardadym, V.: Computer-aided school and university timetabling: The new wave. In: Burke, E.K., Ross, P. (eds.) Practice and Theory of Automated Timetabling. LNCS, vol. 1153, pp. 22–45. Springer, Heidelberg (1996)Google Scholar
  5. 5.
    Beyrouthy, C., Burke, E.K., McCollum, B., McMullan, P., Landa-Silva, D., Parkes, A.: Towards improving the utilisation of university teaching space. Technical Report NOTTCS-TR-2006-5, School of Computer Science & IT, University of Nottingham (2006)Google Scholar
  6. 6.
    Beyrouthy, C., Burke, E.K., McCollum, B., McMullan, P., Landa-Silva, D., Parkes, A.: Understanding the role of UFOs within space exploitation. In: Proceedings of the 6th International Conference on the Practice and Theory of Automated Timetabling, Brno, pp. 359–362 (August 2006)Google Scholar
  7. 7.
    Burke, E.K., Jackson, K., Kingston, J., Weare, R.: Automated timetabling: The state of the art. The Computer Journal 40, 565–571 (1997)CrossRefGoogle Scholar
  8. 8.
    Burke, E.K., Petrovic, S.: Recent research directions in automated timetabling. European Journal of Operational Research 140, 266–280 (2002)MATHCrossRefGoogle Scholar
  9. 9.
    Carter, M.: Timetabling. In: Gass, S., Harris, C. (eds.) Encyclopedia of Operations Research and Management Science, pp. 833–836. Kluwer, Dordrecht (2001)CrossRefGoogle Scholar
  10. 10.
    Carter, M., Laporte, G.: Recent developments in practical course timetabling. In: Burke, E.K., Carter, M. (eds.) PATAT 1997. LNCS, vol. 1408, pp. 3–19. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  11. 11.
    Kirkpatrick, S., Gelatt, C.D., Vecchi, M.P.: Optimization by simulated annealing. Science 220, 671–680 (1983)CrossRefMathSciNetGoogle Scholar
  12. 12.
    Laporte, G., Desroches, S.: The problem of assigning students to course sections in a large engineering school. Computers and Operations Research 13, 387–394 (1986)CrossRefGoogle Scholar
  13. 13.
    McCollum, B., McMullan, P.: The cornerstone of effective management and planning of space. Technical Report, Realtime Solutions Ltd (2004)Google Scholar
  14. 14.
    McCollum, B., Roche, T.: Scenarios for allocation of space. Technical Report, Realtime Solutions Ltd (2004)Google Scholar
  15. 15.
    Petrovic, S., Burke, E.K.: University timetabling. In: Leung, J. (ed.) Handbook of Scheduling: Algorithms, Models, and Performance Analysis, pp. 1–23. Chapman and Hall/CRC Press, London (2004)Google Scholar
  16. 16.
    Schaerf, A.: A survey of automated timetabling. Artificial Intelligence Review 13, 18–27 (1999)CrossRefGoogle Scholar
  17. 17.
    Selim, S.: Split vertices in vertex colouring and their application in developing a solution to the faculty timetable problem. The Computer Journal 31, 76–82 (1988)MATHCrossRefMathSciNetGoogle Scholar
  18. 18.
    de Werra, D.: An introduction to timetabling. European Journal of Operational Research 19, 151–162 (1985)MATHCrossRefMathSciNetGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Camille Beyrouthy
    • 1
  • Edmund K. Burke
    • 1
  • Dario Landa-Silva
    • 1
  • Barry McCollum
    • 2
    • 3
  • Paul McMullan
    • 2
  • Andrew J. Parkes
    • 1
  1. 1.School of Computer Science and IT, University of Nottingham, Nottingham NG8 1BBUK
  2. 2.Queen’s University of Belfast, Belfast, BT7 1NNUK
  3. 3.Realtime Solutions Ltd, 21 Stranmillis Road, Belfast, BT9 5AF 

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