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A Tabu Search-based University Lectures Timetable Scheduling Model

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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2018
Authors:
Adewale O. Sunday, Ibam E. Onwuka, Izundu Kingsley E.
10.5120/ijca2018917599

Adewale O Sunday, Ibam E Onwuka and Izundu Kingsley E.. A Tabu Search-based University Lectures Timetable Scheduling Model. International Journal of Computer Applications 181(9):16-23, August 2018. BibTeX

@article{10.5120/ijca2018917599,
	author = {Adewale O. Sunday and Ibam E. Onwuka and Izundu Kingsley E.},
	title = {A Tabu Search-based University Lectures Timetable Scheduling Model},
	journal = {International Journal of Computer Applications},
	issue_date = {August 2018},
	volume = {181},
	number = {9},
	month = {Aug},
	year = {2018},
	issn = {0975-8887},
	pages = {16-23},
	numpages = {8},
	url = {http://www.ijcaonline.org/archives/volume181/number9/29799-2018917599},
	doi = {10.5120/ijca2018917599},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}
}

Abstract

Scheduling University Courses is regarded as a Non-deterministic Polynomial-time hardness (NP-hard) problem. This is because no universal constraint works for all universities. While some will have constraints similar, they might differ in their resource values - length of days, time slots, and rooms. Several literatures have been able to address several constraints, using various optimization methods - genetic algorithms, tabu search and so on. The result often time works but lacks adoptability due to their non-inclusiveness of some resource parameters - day and dynamic time-slot. In this research, we address the various constraints related to the Federal University of Technology Akure (FUTA) using mathematical model that includes the necessary resource parameters. We adopt Tabu search diversification approach to implement a scheduling system that satisfies the constraints defined.

References

  1. Alizera R. K., Mehrdad N. K. 2015. A mathematical model for University Course Scheduling, A case Study. International Journal of Technical Research and Applications, 20-25.
  2. Anibal T., Per M. G. and Alexandar K. 2013, A Mathematical Model for Determining Timetables that minimizes the Number of Students with Conflicting Schedules. In Proceedings of the European Modeling and Simulation Symposium, 619-624.
  3. Carter, M. W., and Laporte, G. 1998. Recent developments in practical course timetabling. In Burke, E., and Carter, M., eds., Practice and Theory of Automated Timetabling II, 3–19. Springer-Verlag LNCS 1408
  4. Thomas M. and Roman B. 2001. Interactive Timetabling. Accessed at https://www.researchgate.net/publication/220482439_Interactive_Timetabling, on May 27, 2017.
  5. Haroldo G., Luiz S., and Marcone J. 2004. An Efficient Tabu Search Heuristic for the School Timetabling Problem. In Proceedings of the Third International Workshop on Experimental and Efficient Algorithms, WEA 2004, Angra dos Reis, Brazil, May 25-28.
  6. ] Keith M., Tornaj M. P. and Hana R. 2010. System Demonstration of Interactive Course Timetabling. In Proceedings of the 8th International Conference on the Practice and Theory of Automated Timetabling, 573-577. Queen's University Belfast.
  7. Lintang Y., B., and Vega V. 2011. University Timetabling Algorithm Considering Lecturers Workload. In Proceedings of the sixth International Multi-Conference on Computing in the Global Information Technology, Luxembourg, 31-37.
  8. Elizabeth M., Mar´ıa-Cristina R. and Leopoldo A. 2011. A PSO algorithm to solve a Real Course+Exam Timetabling Problem. In Proceedings of the International conference on swarm intelligence. Cergy, France, June 14-15.
  9. Premlata A. S. and Leena R. 2014. Hybrid Genetic Algorithm and Tabu Search Algorithm to Solve Class Timetabling Scheduling Problem. International Journal of Research Studies in Computer Science and Engineering (IJRSCSE) Vol. 1, Issue 4, 19-26.
  10. Hamed B. and Aminu H. 2014. A Review of Distributed Multi-Agent Systems Approach to Solve University Course Timetabling Problem. ACSIJ Advances in Computer Science: an International Journal, Vol. 3, Issue 5, No.11.
  11. Alvarez, R., Crespo, E., & Tamarit, J. M. 2002. Design and Implementation of a Course Scheduling System Using Tabu Search. European Journal of Operational Research, Vol. 137, 512-523.
  12. Sarah A., Fai A., Hawazen A., Sarah A., Maha A. and Manar H. 2018. An Intelligent Bio-Inspired Algorithm for the Faculty Scheduling Problem. International Journal of Advanced Computer Science and Applications, Vol. 9, No. 5.
  13. Glover F. and Laguna M. 1997. Tabu Search. Kluwer Academic Publishers, Boston.
  14. Hertz A. (1991). Tabu search for large scale timetabling problems. European Journal of Operational Research, Vol. 54, Issue 1, 39-47

Keywords

Timetable, Tabu Search, Constraint, Diversification, Economy Code Protocol (ECP), Mathematical Model, Comfort Adjustment Factor (CAF).