Ant colony optimisation for solving university course timetabling problems

  • Abstract
  • Keywords
  • References
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  • Abstract

    Course timetabling is one of the most important activities faced by any educational institution. Furthermore, the course timetabling process is time-consuming and tiresome as it needs to be prepared for each regular semester. This paper aims to apply the Ant Colony Optimisation (ACO) method to solve the course timetabling problem. This approach is to optimise the properties of the course requirement and minimise various conflicts for the time slot assignation. This method is based on the life of the ant colony in generating automatic timetabling according to the properties (pheromones) such as time, student, lecturer and room, besides satisfying the constraints. The implementation of this method is to find an effective and better solution for university course timetabling. The result and performance evaluation is used to determine whether it is reliable in providing the feasible timetable.



  • Keywords

    Ant Colony Optimisation (ACO); Constraints; Course timetabling; Optimisation.

  • References

      [1] Thepphakorn T & Pongcharoen P (2013), Heuristic ordering for ant colony based timetabling tool. Journal of Applied Operational Research 5, 113–123.

      [2] Matijaš VD, Molnar G, Čupić M, Jakobović D & Bašić BD (2010), University course timetabling using ACO: A case study on laboratory exercises. Proceedings of the International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, pp. 100–110.

      [3] Dimopoulou M & Miliotis P (2001), Implementation of a university course and examination timetabling system. European Journal of Operational Research 130, 202–213.

      [4] Hlaing ZC & Khine MA (2011), An ant colony optimization algorithm for solving traveling salesman problem. Proceedings of the International Conference on Information Communication and Management, pp. 54–59.

      [5] Burke EK, McCollum B, Meisels A, Petrovic S & Qu R (2007), A graph-based hyper-heuristic for educational timetabling problems. European Journal of Operational Research 176, 177–192.

      [6] Maniezzo AC (1992), Distributed optimization by ant colonies. Proceedings of the 1st European Conference on Artificial Life, pp. 134–142.

      [7] Colorni A, Dorigo M & Maniezzo V (1992), An investigation of some properties of an" ant algorithm". Proceedings of the Parallel Problem Solving From Nature Conference, pp. 509–520.

      [8] Dorigo M, Maniezzo V & Colorni A (1996), Ant system: Optimization by a colony of cooperating agents. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) 26, 29–41.

      [9] Blum C (2005), Ant colony optimization: Introduction and recent trends. Physics of Life Reviews 2, 353–373.

      [10] Dorigo M & Gambardella LM (1997), Ant colony system: A cooperative learning approach to the traveling salesman problem. IEEE Transactions on Evolutionary Computation 1, 53–66.

      [11] Stützle T & Hoos HH (2000), MAX–MIN ant system. Future Generation Computer Systems 16, 889–914.

      [12] Socha K, Knowles J & Sampels M (2002), A max-min ant system for the university course timetabling problem. Proceedings of the International Workshop on Ant Algorithms, pp. 1–13.




Article ID: 11371
DOI: 10.14419/ijet.v7i2.15.11371

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