University Course Timetabling Model in Joint Courses Program to Minimize the Number of Unserved Requests

This work proposes a novel course timetable model for the national joint courses program. In this model, the participants, both students and lecturers, come from different universities. It is different from most existing university course timetabling models where the environment is physical, and the system can dictate the timeslots and classrooms for the students and lecturers. The courses are delivered online in this model, so physical classrooms are no longer required, as was the case in most previous course timetabling studies. In this model, the matching process is conducted based on the assigned timeslots and the requested courses. The courses are elective rather than mandatory. Three metaheuristic methods are used to optimize this model: artificial bee colonies, cloud theory-based simulated annealing, and genetic algorithms. Due to the simulation process, the cloud theory-based simulated annealing performs best in minimizing the number of unserved requests. This method outperforms the two other metaheuristic methods, the genetic algorithm, and the artificial bee colony algorithm. According to the simulation results, when the number of students is low, the cloud theory-based simulated annealing has 91 percent fewer unserved requests than the genetic algorithm. When the number of students is large, this figure drops to 62%.

@article{Kusuma2021,
  title     = {University Course Timetabling Model in Joint Courses Program to Minimize the Number of Unserved Requests},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {12},
  number    = {10},
  year      = {2021},
  publisher = {The Science and Information Organization},
  author    = {Purba Daru Kusuma and Abduh Sayid Albana},
  doi       = {10.14569/IJACSA.2021.0121014},
  url       = {https://doi.org/10.14569/IJACSA.2021.0121014}
}