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)


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.


Local Search Simulated Annealing Hard Constraint Hill Climbing Space Planning 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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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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