A Survey of Approaches for Designing Course Timetable Scheduling Systems in Tertiary Institutions

  • Usman Bala Musa Ibrahim Badamasi Babangida University, Lapai, Nigeria
  • Akinyemi Moruff Oyelakin College of Information and Communication Technology, Crescent University, Abeokuta, Nigeria
Keywords: Scheduling Problem, Educational Time Table Scheduling, Hard Constraints, Soft Constraints

Abstract

Scheduling the course schedule in tertiary institutions is a complex and crucial task. Past studies have pointed out that when scheduling is performed effectively, it influences students' learning experiences, faculty workloads, and overall institutional efficiency. It has also been argued that in the allocation of courses, classrooms, and faculty members, various constraints, preferences, assumptions, dependencies, and objectives must be taken into consideration. This article reviewed different approaches that have been employed in designing course schedule scheduling systems with particular reference to tertiary institutions. Relevant articles were sourced from notable research repositories using identified keywords. The articles obtained were categorized according to the different methods that were used to solve the scheduling problems of course schedules in higher institutions. The review evaluated how each approach addresses the challenges in course time table scheduling. Thereafter, the paper discussed the advantages, limitations, and suitability of these scheduling techniques time-tabling. Additionally, real-world implementations in various tertiary institutions are mentioned. By discussing the strengths and weaknesses of different methodologies in this work, this survey is believed to be a valuable resource for future studies in the area of course scheduling in tertiary institutions.

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References

Al-Yakoob, Sherali, A. Et, Al, M. [ 10], and L. Zhang, “A Survey on the Requirements of University Course Timetabling,” International Science Index, Mathematical and Computational Sciences, vol. 10, no. 5, 2016.

K. Zhu, L. D. Li, and M. Li, “A Survey of Computational Intelligence in Educational Timetabling,” Int J Mach Learn Comput, vol. 11, no. 1, pp. 40–47, Jan. 2021, doi: 10.18178/ijmlc.2021.11.1.1012.

PremasirilDM, “University Timetable Scheduling Using Genetic Algorithm Approach Case Study: Rajarata University OF Sri Lanka,” Journal of Engineering Research and Application www.ijera.com, vol. 8, no. 12, 2018.

E. Andrew Okonji, “Intelligence Classification of the Timetable Problem: A Memetic Approach,” International Journal on Data Science and Technology, vol. 3, no. 2, 2017, doi: 10.11648/j.ijdst.20170302.12.

R. R. Iwańkowicz and M. Taraska, “Self-classification of assembly database using evolutionary method,” Assembly Automation, vol. 38, no. 3, 2018, doi: 10.1108/AA-06-2017-071.

W. Tian, W. Guo, and M. He, “On the Classification of NP Complete Problems and Their Duality Feature,” International Journal of Computer Science and Information Technology, vol. 10, no. 1, 2018, doi: 10.5121/ijcsit.2018.10106.

A. E. Phillips, C. G. Walker, M. Ehrgott, and D. M. Ryan, “Integer programming for minimal perturbation problems in university course timetabling,” Ann Oper Res, vol. 252, no. 2, 2017, doi: 10.1007/s10479-015-2094-z.

R. Ganguli and S. Roy, “A Study on Course Timetable Scheduling using Graph Coloring Approach,” 2017.

A. Soni, K. Padayachee, and R. Ajoodha, “A Graph Colouring Solution to the Timetable Problem with Hard Constraints in Higher Education Institutions,” SSRN Electronic Journal, 2023, doi: 10.2139/ssrn.4332718.

H. Babaei and A. Hadidi, “A Review of Distributed Multi-Agent Systems Approach to Solve University Course Timetabling Problem,” ACSIJ Advances in Computer Science: an International Journal, vol. 3, no. 5, 2014.

H. Babaei, J. Karimpour, and A. Hadidi, “A survey of approaches for university course timetabling problem,” Comput Ind Eng, vol. 86, 2015, doi: 10.1016/j.cie.2014.11.010.

I. R. Cassar, N. D. Titus, and W. M. Grill, “An improved genetic algorithm for designing optimal temporal patterns of neural stimulation,” J Neural Eng, vol. 14, no. 6, 2017, doi: 10.1088/1741-2552/aa8270.

A. Hussain, Y. Shad Muhammad, and A. Nawaz, “Optimization through Genetic Algorithm with a New and Efficient Crossover Operator,” 2018.

F. Ben Amor, T. Loukil, and I. Boujelben, “The new formulation for the Integrated Dial-a-Ride Problem with Timetabled fixed route service,” in International Colloquium on Logistics and Supply Chain Management, LOGISTIQUA 2019, 2019. doi: 10.1109/LOGISTIQUA.2019.8907255.

M. Andrey, V. Voronkin, P. Svetlana, and S. Alexey, “Software and hardware infrastructure for timetables scheduling in university,” in CEUR Workshop Proceedings, 2018.

O. Abayomi-Alli, A. Abayomi-Alli, S. Misra, R. Damasevicius, and R. Maskeliunas, “Automatic Examination Timetable Scheduling Using Particle Swarm Optimization and Local Search Algorithm,” in Data, Engineering and Applications: Volume 1, vol. 1, 2019. doi: 10.1007/978-981-13-6347-4_11.

J. H. Obit and D. Landa-Silva, “Computational study of non-linear great deluge for university course timetabling,” Studies in Computational Intelligence, vol. 299, 2010, doi: 10.1007/978-3-642-13428-9_14.

Z. Lixi and L. SimKim, “Constructing university timetable using constraint satisfaction programming approach,” in Proceedings - International Conference on Computational Intelligence for Modelling, Control and Automation, CIMCA 2005 and International Conference on Intelligent Agents, Web Technologies and Internet, 2005. doi: 10.1109/cimca.2005.1631445.

A. Agnar and E. Plaza, “Case-Based reasoning: Foundational issues, methodological variations, and system approaches,” AI Communications, vol. 7, no. 1, 1994, doi: 10.3233/AIC-1994-7104.

N. Pillay and R. Qu, “Hyper-Heuristics: Theory and applications,” in Natural Computing Series, 2018.

E. K. Burke, S. Petrovic, and R. Qu, “Case-based heuristic selection for timetabling problems,” Journal of Scheduling, vol. 9, no. 2, 2006, doi: 10.1007/s10951-006-6775-y.

A. Agnar and E. Plaza, “Case-Based reasoning: Foundational issues, methodological variations, and system approaches,” AI Communications, vol. 7, no. 1, 1994, doi: 10.3233/AIC-1994-7104.

N. Pillay, “A review of hyper-heuristics for educational timetabling,” Ann Oper Res, vol. 239, no. 1, 2016, doi: 10.1007/s10479-014-1688-1.

Published
2024-03-01
How to Cite
Musa, U. B., & Oyelakin, A. M. (2024). A Survey of Approaches for Designing Course Timetable Scheduling Systems in Tertiary Institutions. Journal of Systems Engineering and Information Technology (JOSEIT), 3(1), 1-6. https://doi.org/10.29207/joseit.v3i1.5609
Section
Articles