A Systematic Literature Review: Optimization Timetable in Education to Support Work-Life Balance (WLB)

Noorrezam Yusop

doi:10.24191/jcrinn.v7i2.324

A Systematic Literature Review: Optimization Timetable in Education to Support Work-Life Balance (WLB)

Authors

Noorrezam Yusop Pusat Tamhidi, Universiti Sains Islam Malaysia, Bandar Baru Nilai, 71800 Nilai, Negeri Sembilan, Malaysia

DOI:

https://doi.org/10.24191/jcrinn.v7i2.324

Keywords:

SLR, Systematic Literature Review, Timetable Optimization, Work Life Balance, Optimization

Abstract

Scheduling academic staff timetables is crucial and necessary to avoid redundancy and clash of class between teacher and student timetables. A good timetable allows students and teachers to manage their time and support a good and healthy lifestyle. However, with the scheduling, academic staff timetable may use many procedures to get efficient results. Therefore, this paper provides a gap of study for existing work on Optimization Timetable to support Work-Life Balance (WLB) regarding their market commercial and research purposes. The methodology of this study was conducted using a Systematic Literature Review (SLR). Result: two findings investigate 1) relevant optimization timetable scheduling used and 2) the method for timetable optimization to support WLB. The strengths and weaknesses of the features and utilities behind each study are also presented to provide a further understanding of the gaps and weaknesses of each body of research. We conclude that these studies are still insufficient and require further evaluation and improvement.

Downloads

Download data is not yet available.

References

Aminu, Abdulaziz and Caesarendra, Wahyu and Haruna, Umar S and Sani, Abubakar and Sa’id, Mansur and Pamungkas, Daniel S and Kurniawan, Sumantri R and Kurniawan, E. (2019). Design and Implementation of An Automatic Examination Timetable Generation and Invigilation Scheduling System Using Genetic Algorithm. 2019 2nd International Conference on Applied Engineering (ICAE)}, 1–5. https://doi.org/10.1109/ICAE47758.2019.9221700

Barbara Kitchenham, S. C. (2007). Guidelines for performing Systematic Literature Reviews in Software Engineering. Technical Report, EBSE Technical Report EBSE-2007-01.

Bonabeau, E., Dorigo, M., & Theraulaz, G. (1999). Swarm Intelligence - From Natural to Artificial Systems. Santa Fe Institute Studies in the Sciences of Complexity.

Caro, G. Di. (2022). An Introduction to Swarm Intelligence Issues. The University of Washington. Retrieved August 10, 2022, from http://staff.washington.edu/paymana/swarm/dicaro_lecture1.pdf

Duxbury, L. (2004). Dealing with Work-Life Issues in the Workplace: Standing Still is Not an Option. The 2004 Don Wood Lecture in Industrial Relations, 1–25.

Guerriero, F., & Guido, R. (2022). Modeling a flexible staff scheduling problem in the Era of Covid-19. Optimization Letters, 16(4), 1259–1279. https://doi.org/10.1007/s11590-021-01776-3

Hill, E. J., J. Hawkins, A., Ferris, M., & Weitzma, M. (2001). Finding an Extra Day a Week: The Positive Influence of Perceived Job Flexibility on Work and Family Life Balance. In Family Relations (Vol. 50, Issue 1). http://www.jstor.org/stable/585774

Jalal, M., & K Mukhopadhyay, Anal Grasley, Z. (2019). Design, manufacturing, and structural optimization of a composite float using particle swarm optimization and genetic algorithm. Proceedings of the Institution of Mechanical Engineers, Part L: Journal of Materials: Design and Applications, 233(7), 1404–1418. https://doi.org/10.1177/1464420718755546

Kitchenham, B., & Charters, S. M. (2007). Guidelines for performing Systematic Literature Reviews in Software Engineering Guidelines for performing Systematic Literature Reviews in Software Engineering EBSE Technical Report EBSE-2007-01 Software Engineering Group School of Computer Science and Ma. EBSE Technical Report, October 2021.

Knutsater, L., & Sandh, D. (2019). University Course Scheduling Optimization under Uncertainty based on a Probability Model.

Larsen, R. (2012). Optimization Methods for Real Life Scheduling Problems.

Liu, H., Tang, T., Guo, X., & Xia, X. (2018). A timetable optimization model and an improved artificial bee colony algorithm for maximizing regenerative energy utilization in a subway system. Advances in Mechanical Engineering, 10(9), 1–13. https://doi.org/10.1177/1687814018797034

Omar, M. F., Bakeri, N. M., Nawi, M. N. M., Hairani, N., & Khalid, K. (2020). Methodology for Modified Whale Optimization Algorithm for Solving Appliances Scheduling Problem. Journal of Advanced Research in Fluid Mechanics and Thermal Sciences, 76(2), 132–143. https://doi.org/10.37934/arfmts.76.2.132143

Ramos, D. S., & Galleto, P. G. (2020). The Interplay between Work-Life Balance Practices and Productivity among Public Secondary School Teachers: Basis for Guidance and Counseling Program. American Journal of Multidisciplinary Research & Development (AJMRD, 2(3), 45–55. www.ajmrd.com

Rashmi, K. R., & Abhishek, M. B. (2021). Automated University Timetable Generation using Prediction Algorithm. June, 2345–2350.

Sandhu, K. (2001). Automating Class Schedule Generation in the Context of a University Timetabling Information System. 319995, 201.

Tucker, P., & Folkard, S. (1968). Reducible axisymmetric magnetogasdynamic flows. In ZAMM ‐ Journal of Applied Mathematics and Mechanics / Zeitschrift für Angewandte Mathematik und Mechanik (Vol. 48, Issue 6). https://doi.org/10.1002/zamm.19680480604

Uslu, M. F., Uslu, S., & Bulut, F. (2018). An adaptive hybrid approach: Combining genetic algorithm and ant colony optimization for integrated process planning and scheduling. Applied Computing and Informatics, 18(1–2), 101–112. https://doi.org/10.1016/j.aci.2018.12.002

Wang, M., Wang, L., Xu, X., Qin, Y., & Qin, L. (2019). Genetic Algorithm-Based Particle Swarm Optimization Approach to Reschedule High-Speed Railway Timetables: A Case Study in China. Journal of Advanced Transportation, 2019, 13–16. https://doi.org/10.1155/2019/6090742

Wren, A. (1995). Scheduling, timetabling and rostering—a special relationship? In L. N. in C. Science (Ed.), International conference on the practice and theory of automated timetabling (pp. 46–75). Springer Berlin Heidelberg.

Yusop, N. (2022). A Comparison Analysis Study to Analysis Optimization Timetable to Support Work-Life Balance. International Journal of Computer Applications, 184(10), 28–31.

Zandavi, S. M., Chung, V., & Anaissi, A. (2021). Multi-user Remote Lab: Timetable Scheduling Using Simplex Nondominated Sorting Genetic Algorithm. ACM/IMS Transactions on Data Science, 2(2), 1–13. https://doi.org/10.1145/3437260