This paper formulates the college timetable scheduling (CTTS) as a constraint satisfaction problem (CSP) in a manner that is easy to implement. Timetable scheduling is a process that is revised in every academic session and requires a lot of constraint checking, so it is inefficient to check the same type of constraints repeatedly to create a proper schedule manually. CSP is a natural choice for automating this process. The prototype presented in this paper is aimed to offer such assistance in scheduling classes for different courses running in an academic session keeping given different constraint checking like the limit check of the maximum number of lectures that can be assigned to a faculty, limit check of the maximum number of lectures that can be assigned to a course, detecting conflicts between courses scheduled in the same time slots, preventing overlapping assignments for faculty members in the same time slots. Here, first, the different aspects of the timetable scheduling problem are addressed, and then a technique is devised to help map the problem as a CSP.
- Carter M. W., Laporte G. Recent developments in practical course timetabling. Practice and Theory of Automated Timetabling II. 3–19 (1997).
- Valouxis C., Housos E. Constraint programming approach for school timetabling. Computers & Operations Research. 30 (10), 1555–1572 (2003).
- Petrovic S., Burke E. K. University timetabling. Handbook of scheduling (2004).
- MirHassani S. A., Habibi F. Solution approaches to the course timetabling problem. Artificial Intelligence Review. 39 (2), 133–149 (2013).
- Bettinelli A., Cacchiani V., Roberti R., Toth P. An overview of curriculum-based course timetabling. TOP. 23 (2), 313–349 (2015).
- Babaei H., Karimpour J., Hadidi A. A survey of approaches for university course timetabling problem. Computers & Industrial Engineering. 86, 43–59 (2015).
- Kadam V., Yadav S. Academic timetable scheduling: revisited. International Journal of Research in Science & Engineering. 417–423 (2016).
- Jat S. N., Yang S. A hybrid genetic algorithm and tabu search approach for post enrolment course timetabling. Journal of Scheduling. 14 (6), 617–637 (2011).
- Andersson H. School timetabling in theory and practice a comparative study of simulated annealing and tabu search. UME{\AA} Universitet (2015).
- Oude Vrielink R. A., Jansen E. A., Hans E. W., van Hillegersberg J. Practices in timetabling in higher education institutions: a systematic review. Annals of Operations Research. 275 (1), 145–160 (2019).
- Al-Jarrah M. A., Al-Sawalqah A. A., Al-Hamdan S. F. Developing acourse timetable system for academic departments using genetic algorithm. Jordanian Journal of Computers and Information Technology (JJCIT). 3 (1), 25–36 (2017).
- Birbas T., Daskalaki S., Housos E. School timetabling for quality student and teacher schedules. Journal of Scheduling. 12 (2), 177–197 (2009).
- Soria-Alcaraz J. A., Ochoa G., Swan J., Carpio M., Puga H., Burke E. K. Effective learning hyper-heuristics for the course timetabling problem. European Journal of Operational Research. 238 (1), 77–86 (2014).
- Golabpour A., Shirazi H. M., Farahi A., Kootiani A. Z. M., Beigi H. A Fuzzy Solution Based on Memetic Algorithms for Timetabling. 2008 International Conference on MultiMedia and Information Technology, Three Gorges. 108–110 (2008).