CFP last date
20 December 2024
Reseach Article

A Comparison Analysis Study to Analysis Optimization Timetable to Support Work-Life Balance

by Noorrezam Yusop
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 184 - Number 10
Year of Publication: 2022
Authors: Noorrezam Yusop
10.5120/ijca2022922058

Noorrezam Yusop . A Comparison Analysis Study to Analysis Optimization Timetable to Support Work-Life Balance. International Journal of Computer Applications. 184, 10 ( Apr 2022), 28-31. DOI=10.5120/ijca2022922058

@article{ 10.5120/ijca2022922058,
author = { Noorrezam Yusop },
title = { A Comparison Analysis Study to Analysis Optimization Timetable to Support Work-Life Balance },
journal = { International Journal of Computer Applications },
issue_date = { Apr 2022 },
volume = { 184 },
number = { 10 },
month = { Apr },
year = { 2022 },
issn = { 0975-8887 },
pages = { 28-31 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume184/number10/32364-2022922058/ },
doi = { 10.5120/ijca2022922058 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:21:07.979250+05:30
%A Noorrezam Yusop
%T A Comparison Analysis Study to Analysis Optimization Timetable to Support Work-Life Balance
%J International Journal of Computer Applications
%@ 0975-8887
%V 184
%N 10
%P 28-31
%D 2022
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Scheduling academic staff timetable is important to avoid the classes clash or redundancy between teacher and student timetable. A good timetable allow the student and teacher time management with a good healthy and work-life balance. However, with the scheduling academic staff timetable may use many procedure to get efficiency result. Therefore, this paper is provides a gap of study for existing work on optimization timetable regarding their research purpose. This research report our findings from review and analysis of different studies. The strengths and shortcomings of the features and utility are also discussed in order to offer a better understanding of the research's gaps and weaknesses. This study concludes that these studies are still in their infancy and require additional improvement.

References
  1. Wren,A.1995. Scheduling, timetabling and rostering—a special relationship?.International conference on the practice and theory of automated timetabling, pp. 46–75.
  2. Dorigo,M.2007.Swarm Intelligence. Springer New York.
  3. Kachitvichyanukul,V.2012. Comparison of Three Evolutionary Algorithms :GA, PSO, and DE.Industrial Engineering & Management Systems, vol. 11, no. 3, pp. 215–223.
  4. Aminu,A.2019. Design and implementation of an automatic examination timetable generation and invigilation scheduling system using genetic algorithm.In 2nd International Conference on Applied Engineering (ICAE).
  5. Jalal,M., Mukhopadhyay,A. K., Grasley,Z.2019. Design, manufacturing, and structural optimization of a composite float using particle swarm optimization and genetic algorithm.In Proceedings of the Institution of Mechanical Engineers, Part L: Journal of Materials: Design and Applications, vol. 233, no. 7, pp. 1404–1418, doi: https://doi.org/ 10.1177/1464420718755546.
  6. Duxbury,L.2004. Dealing with work-life issues in the workplace: Standing still is not an option.In The 2004 Don Wood Lecture in Industrial Relations.
  7. Hill,E. J., Hawkins,A. J., Ferris,M., Weitzman,M.2001. Finding an extra day a week: The positive effect of job flexibility on work and family life balance.Family Relations, vol. 50, no. 1, pp. 49–58.
  8. Rashmi,K. R., Abhishek,M. B.2021. Automated University Timetable Generation using Prediction Algorithm. June, pp. 2345–2350.
  9. 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. vol. 10, no. 9, pp. 1–13,doi: 10.1177/1687814018797034.
  10. 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, vol. 2019, pp. 13–16.https://doi.org/10.1155/2019/6090742
  11. Knutsater, L. Sandh,D.2019. University Course Scheduling Optimization under Uncertainty based on a Probability Model.
  12. Omar,M. F., Mohd Bakeri,N., Mohd Nawi,N., Hairani, M. 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, vol. 2, no. 2, pp. 132–143.
  13. 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. New England Journal of Entrepreneurship, doi: 10.1016/j.aci.2018.12.002.
  14. Zandavi,S. M. Chung,V., Anaissi,A. L. I.2020. Multi-User Remote lab: Timetable Scheduling Using Simplex Nondominated Sorting Genetic Algorithm. pp. 1–11.
  15. Sheau,H., Irene,F., Hashim,M., Zaiton,S. 2009. Solving University Course Timetable Problem Using Hybrid Particle Swarm Optimization. pp. 93–99.
Index Terms

Computer Science
Information Sciences

Keywords

Timetable Optimization Work-Life Balance