Design and Implementation of a Web-Based Timetable System for Higher Education Institutions
Timetabling concerns sets of activities geared towards the production of a timetable that must be open to various constraints. A timetable for higher education institution timetable refers to a temporary structure of a lecture series and lecture halls or classrooms where all presented constraints are met or satisfied. Currently, several administrative activities and services for higher educational institutions have been automated with the neglect of timetable for lecturing because of the challenges associated with its automation. Over the years, consultations with stakeholders such as students and staff in departments, faculty lasting several weeks coupled with adjustments due to feedback has characterized the timetable preparation process. The processes and procedure involved in creating timetables of such nature in a manual way is regarded as demanding, complex and time-consuming process. A computer assisted timetable generator is a time saver for administrators tasked with the job of course and timetable creation and management. Because every organization has its own timetabling dilemma, the software programs that are available on the market may not meet the needs of every organization. Therefore, a realistic approach to building a timetabling program for lecture courses must be created, which can be tailored to suit any problem with higher education timetabling. Due to the need for flexibility, adaptability to future requirements, and the possibility of producing the deliverables within a limited time frame to address the stated challenges, the Rapid Application Development (RAD) software development model was used. This project aims to produce a practically oriented timetable algorithm capable of addressing the challenges posed by weak and strong constraints in an automated timetable package.
Academic Timetable, Academic Scheduling, Web-based Timetable
Ahmed, K., & Keedwell, E. (2017). "A Hidden Markov Model Approach to the Problem of Heuristic Selection in Hyper Heuristics with a Case Study in High School Timetabling Problems," Massachusetts Institute of Technology, 2017.
Burke & Carter (1998). Practice and Theory of Automated Timetabling: Selected Papers from the 2nd International Conference. Lecture Notes in Computer Science, 14 (08), 115 129.
Esraa, A., & Ghada, E., K. (2016). "A Utilization-based Genetic Algorithm for Solving the University Timetabling Problem (UGA)," Alexandria Engineering Journal, vol. 55, no. 2, pp. 1395-1409, 2016.
Fatih, C. & Merve, K. (2015). "A Fuzzy Logic and Binary-Goal Programming-Based Approach for Solving the Exam Timetabling Problem to Create a Balanced-Exam Schedule," International Journal of Fuzzy Systems, pp. 119-129.
Hamed, B., Jaber, K. & Amin, H. (2019). "Generating an optimal timetabling for multi-departments common lecturers using hybrid fuzzy and clustering algorithms," Methodologies and Application, vol. 23, p. 4735–4747.
Iosif, K., Ioannis, T. & Grigorios, B. (2015). "A Comparative Study of Modern Heuristics on the School," Algorithms, vol. 8, no. 3, pp. 723-742.
Joselynn, H., F. (2019). "The Effect of the Gainful Employment Regulatory Uncertainty on Student Enrollment at For Profit Institutions of Higher Education," Research in Higher Education, p. 1 25.
Kheiri, A., & Ed, K. (2017) "A Hidden Markov Model Approach to the Problem of Heuristic Selection in Hyper Heuristics with a Case Study in High School Timetabling Problems," Evolutionary Computation, vol. 25, no. 3, pp. 473-501.
Mahmoud, S., Mahmoud, N., & Mohammad, M., M. (2020). "Optimal Localization of Shopping Centers Using Metaheuristic Genetic Algorithm," Journal of Optimization in Industrial Engineering, vol. 13, no. 1, pp. 167 176.
Nelishia, P., & Ender, O. (2019). "Automated generation of constructive ordering heuristics for educational timetabling," Annals of Operations Research, vol. 275, no. 1, p. 181–208.
Rakesh, B., Gupta & Pallav, I., (2014). "A new hybrid algorithm for university course timetabling problem using events based on groupings of students," Computers & Industrial Engineering, vol. 78, pp. 12-25.
Schaerf A. (1999). A survey of automated timetabling. Artificial Intelligence Review, 13 (2), 87-127.
Siau, K., & Tian, Y. (2009). A Semiotics Analysis of UML Graphical Notations. Requirements Engineering, 14 (1), 15 26.
Suliadi, S., Ahmad, I., Maselan, A., & Siti, R. (2018). "A Heuristics Approach for Classroom Scheduling Using Genetic Algorithm Technique," Journal of Physics: Conference Series, vol. 995.
Suresh, L., Ajinkya, K., Akash, T., Manasi, M., & Yogesh, C. (2014). "Genetic Algorithm: Paradigm Shift over a Traditional Approach of Timetable Scheduling," in Proceedings of the 3rd International Conference on Frontiers of Intelligent Computing: Theory and Applications (FICTA) 2014, Switzerland, 2014.
Tan, L., Joe, O., Yu-Beng, L. and Jetol, B. (2018). "Implementation of Constraint Programming and Simulated Annealing for Examination Timetabling Problem," Computational Science and Technology, vol. 481, pp. 175 184.
Zekang, L., Shiwei, H., Rui S., & Sijia, H. (2019). "Optimizing Vehicle Scheduling Based on Variable Timetable by Benders-and-Price Approach," Journal of Advanced Transportation, vol. 2019, pp. 1 13.
Glover, F. (1986) “Future Paths for Integer Programming and Links to Artificial Intelligence,” Computers and Operations Research, Vol. 13, pp. 533-549.
E. K. Burke, M. Hyde, G. Kendall, G. Ochoa, E. Ozcan, and J. Woodward. Exploring hyperheuristic methodologies with genetic programming. In C. Mumford and L. Jain, editors, Collaborative Computational Intelligence. Springer, 2009.
Kirkpatrick, S., Gelatt Jr., C. D. and Vecchi, M. P. (1983) Optimization by Simulated Annealing. Science, 220, 671-680. https://doi.org/10.1126/science.220.4598.671