Solving the Timetabling Problem with Simulated Annealing

  • F. Melício
  • P. Caldeira
  • A. Rosa
Chapter

Abstract

School timetabling is an optimisation problem, which consists in assigning lectures to timeslots, satisfying a set of constraints of various kinds. Due mostly to the constraints this problem falls in the category of NP-complete. Simulated Annealing (SA) have been applied with significant success to different combinatorial optimisation problems. Nevertheless, any implementation of SA algorithm is highly dependent of how structural elements are defined, i.e., solution space, generation of new solutions, cost function. In this paper, we try to solve the timetabling problem using simulated annealing and compare several parameters concerning the algorithm.

Key words

Simulated annealing timetabling scheduling heuristics 

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References

  1. [1]
    F. Catthoor; H. de Man, “SAMURAI: A general and efficient simulated-annealing schedule with fully adaptative annealing parameters”, VLSI Journal, vol. 6, pp. 147–178, 1988.CrossRefGoogle Scholar
  2. [2]
    M. D. Huang; F. Romeo; A. Sangiovanni-Vincetelli, “An Efficient General Cooling Schedule for Simulated-Annealing”, Proc. IEEE-ICCAD, pp. 381–384, 1986.Google Scholar
  3. [3]
    J. Thompson, K. A. Dowsland, “General Cooling Schedules for a Simulated Annealing Based Timetabling System”, Proc. 1st Intl. Conf. on the Pratice and Theory of Automated Timetabling, pp. 345–363, 1995Google Scholar
  4. [4]
    D. de Werra, “An Introduction to Timetabling”, European Journal of Operational Research, vol. 19, pp 151–162, 1985.MathSciNetMATHCrossRefGoogle Scholar
  5. [5]
    D. Abramson, H. Dang, M. Krishnamoorthy, “An Emprirical Study of Simulated Annealing Cooling Schedules”, Griffith Univ. report, Aus. 1994.Google Scholar
  6. [6]
    A. Schaerf; “A survey of automated timetabling”, Report CS-R9567 of Stiching Mathematisch Centrum (SMC), Amsterdam, 1995Google Scholar
  7. [7]
    S. Kirkpatrick; C. D. Gellati; M. Vecchi, “Optimization by Simulated Annealing”, Science, vol. 220, pp. 671–680, 1983.MathSciNetMATHCrossRefGoogle Scholar
  8. [8]
    E. H. L. Aarts; P. J. M. van Laarhoven, “A New Polynomial-Time Cooling Schedule”, Proc. IEEE-ICCAD, pp. 206–208, 1985.Google Scholar
  9. [9]
    E. H. L. Aarts, J. H. M. Korst, P. J. M. van Laarhoven, “Simulated annealing”, Local Search in Combinatorial Optmization, E. H. L. Aarts and J. K. Lenstra (eds.), John Wiley & Sons, 1997.Google Scholar
  10. [10]
    S. Elmohamed, P. Coddington, G. Fox, “A Comparison of Annealing Techniques for Academic Course Scheduling”, Proc. 2nd Intl. Conf. On the Pratice and Theory of Automated Timetabling, pp. 146–166, 1997.Google Scholar
  11. [11]
    S. A. Kravitz, R. A. Rutenbar, “Placement by Simulated Annealing on a Multiprocessor”, IEEE Transactions on CAD, pp. 534–549, vol CAD-6, no. 4, Jul. 1987.Google Scholar
  12. [12]
    V. A. Bardadym, “Computer-Aided School and University Timetabling: The New Wave”, Proc. 1st Intl. Conf. on the Pratice and Theory of Automated Timetabling, pp. 22–45, 1995.Google Scholar
  13. [13]
    K. A. Dowsland, “Off-the-peg or made-to-measure?”, Proc. 2nd Intl. Conf. On the Pratice and Theory of Automated Timetabling, pp. 7–26, 1997.Google Scholar
  14. [14]
    D. Abramson, “Constructing School Timetables using Simulated Annealing: Sequential and Parallel Algorithms”, Management Science, pp. 98–113, vol. 37, no. 1, Jan. 1991.CrossRefGoogle Scholar
  15. [15]
    J. M. Varanelli, J. P. Cohoon, “A Fast Method for Generalized Starting Temperature Determination in Monotonically Cooling Two-Stage Simulated Annealing Systems”, Report CS-9508, University of Virginia, Fev. 1995Google Scholar
  16. [16]
    J. G. Gay, R. Richter, B. J. Berne, “Component placement in VLSI circuits using a constant pressure Monte Carlo method”, VLSI Journal n° 3, North-Holland Integration, pp. 271–282, 1985.Google Scholar
  17. [17]
    L. K. Grover, “Standard Cell Placement using Simulated Sintering”, Proc. 24th ACM/IEEE Design Automation Conference, pp. 56–59, 1987.Google Scholar
  18. [18]
    F. Melicio, “THOR: Uma ferramenta para elaboração de horários duma escola”, Proc. 3° Meeting OE, pp. 77–82, Porto, Jun 1997.Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2000

Authors and Affiliations

  • F. Melício
    • 1
    • 2
  • P. Caldeira
    • 1
    • 3
  • A. Rosa
    • 1
    • 4
  1. 1.Evolutionary Systems and Biomedical Engineering LabLisboaPortugal
  2. 2.Instituto Superior de Engenharia de LisboaLisboaPortugal
  3. 3.Escola Superior de TecnologiaSetúbalPortugal
  4. 4.Instituto Superior TécnicoLisboaPortugal

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