Advertisement

Dynamic Timetable Generation Using Constraint Satisfaction Algorithm

  • Urmila Kalshetti
  • Deepika Nahar
  • Ketan Deshpande
  • Sanket Gawas
  • Sujay Sudeep
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 379)

Abstract

Manual method of generating timetable has always been a time-consuming, laborious, and tedious task. It is neither efficient nor effective in terms of utilization of resources. The complicated relationships between time periods, classes (lectures), classrooms, and instructors (staff) make it difficult to attain a feasible solution. In this paper, timetabling problem is modeled as a constraint satisfaction problem. The algorithm dynamically builds the timetable adjusting resources in order of complexity. The main focus is to satisfy all the hard constraints and maximum soft constraints without any conflicts among resources. In order to reach a subsolution state, we use various heuristics that guide the search. Along with this, chronological backtracking and look-ahead techniques are also discussed. This software is ergonomic in nature as it also provides a way to alter the given inputs.

Keywords

Timetabling problem Constraint satisfaction Hard constraints Soft constraints Chronological backtracking Look-ahead 

References

  1. 1.
    Lien-Fu, L., Nien-Lin, H., Liang-Tsung, H., Tien-Chun, C.: An artificial intelligence approach to course timetabling. In: Proceedings of the 18th IEEE International Conference on Tools with Artificial Intelligence (ICTAI ’06)Google Scholar
  2. 2.
    Oyebanjo, A.: Development of a University Timetable Automation System, OTA, May 2012Google Scholar
  3. 3.
    Gotlieb, C.C.: The construction of class-teacher timetables. In: Proceedings of IFIP Congress, pp. 73–77. North-Holland Pub. Co., Amsterdam (1962)Google Scholar
  4. 4.
    Lawrie, N.L.: An integer programming model of a school timetabling problem. Comput. J. 12, 307–316 (1969)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Tripathy, A.: School timetabling—a case in large binary integer linear programming. Manag. Sci. 30(12), 1473–1489 (1984)MATHCrossRefGoogle Scholar
  6. 6.
    Abramson, D.: Constructing school timetables using simulated annealing: Sequential and parallel algorithms management-science (1991)Google Scholar
  7. 7.
    Dikman, R., Luling, R., Simon, J.: Problem independent distributed simulated annealing and its applications. Technical Report—Paderborn Center for Parallel Computing (1993)Google Scholar
  8. 8.
    Elaine, R.: Artificial Intelligence. ISBN-13: 978-0-07-008770-5Google Scholar
  9. 9.
    Zhang, L., Lau, S.: Constructing university timetable using constraint satisfaction programming approach. In: International Conference on Computational Intelligence for Modelling, Control and Automation (2005)Google Scholar
  10. 10.
    Hana, R., Keith, M.: University Course Timetabling with Soft Constraints, USAGoogle Scholar
  11. 11.
    Tomáš, M.: Constraint Based Timetabling. Prague (2005)Google Scholar
  12. 12.
    Sandhu, K.S.: Automating class schedule generation in the context of a University timetabling information system. Griffith University (2001)Google Scholar
  13. 13.
    Rina, D.: Constraint Processing. ISBN 1-55860-890-7Google Scholar

Copyright information

© Springer India 2016

Authors and Affiliations

  • Urmila Kalshetti
    • 1
  • Deepika Nahar
    • 1
  • Ketan Deshpande
    • 1
  • Sanket Gawas
    • 1
  • Sujay Sudeep
    • 1
  1. 1.Department of Computer and Information TechnologyPune Vidyarthi Griha’s COETPuneIndia

Personalised recommendations