Evolutionary Allocation with Dynamic Guidance for Pre-Scheduled Timetables

The University Course Timetabling Problem (UCTP) is a well-known combinatorial optimization challenge that involves allocating courses to timeslots and rooms while satisfying various institutional constraints. At other institutions, general courses (e.g., language subjects) are prioritised during timetable allocation due to their high enrolment numbers from multiple faculties. However, at the MARA University of Technology, Sarawak Branch, Mathematics and Statistics (MAT/STA) courses are shared across several programs, with timeslot availability limited by pre-scheduled major courses in each program. This study presents a tailored evolutionary algorithm for a real institutional scenario, incorporating dynamic local search and guided variation operators to improve feasibility and solution quality. A case study was conducted using real datasets from the Department of Mathematical Sciences at MARA University of Technology, Sarawak Branch. Benchmark datasets from ITC2002 and ITC2007 (Track 2) are employed for comparative evaluation against existing methods. The algorithm successfully produced a feasible timetable and outperformed manually prepared schedules in terms of soft-constraint penalties. Results indicate strong performance on real datasets, producing a high-quality, balanced timetable with reduced preparation time, particularly in handling repeating students, lecturers' availability, and limited timeslots. Nevertheless, lower performance on benchmark datasets suggests the need for hybridization or additional operators to handle larger, more complex problems. Overall, the results demonstrate the effectiveness of the proposed approach in real-world timetabling while maintaining acceptable quality on benchmark instances.

@article{Hamdan2026,
  title     = {Evolutionary Allocation with Dynamic Guidance for Pre-Scheduled Timetables},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {17},
  number    = {6},
  year      = {2026},
  publisher = {The Science and Information Organization},
  author    = {Anniza Hamdan and Sze San Nah and Goh Say Leng and Emily Sing Kiang Siew},
  doi       = {10.14569/IJACSA.2026.0170627},
  url       = {https://doi.org/10.14569/IJACSA.2026.0170627}
}