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Preparing course timetables for universities is a search problem with many constraints. Exhaustive search techniques in theory can be used to develop course timetables for academic departments, but unfortunately these techniques are computation intensive, since the search space is very large and therefore are impractical. In this paper, Genetic Algorithms (GA’s) are utilized to build an automated course timetable system. The system is designed for any academic department. The proposed timetabling system requires minimal effort from the administration staff to prepare the course timetable. Moreover, the prepared course timetable considers faculties’ desires, students' needs and available resources, such as classrooms and laboratories with optimal utilization. The proposed timetabling process was divided into three stages. The first stage is the data collection stage. In this stage, the administrative staff; usually the head of the department, is responsible for preparing the required data, such as the names of the faculty personnel and their desires of courses and laboratories ordered with some priority scheme. Number and type of theoretical and practical courses are also fed to the system based on some statistics about student numbers and previous course timetable history. The system is also fed with number of lecture rooms allocated for the department and number of labs with information about theoretical courses they are able to serve. In the second stage, the program generates an initial set of suggested schedules (chromosomes). Each chromosome represents a solution to the problem, but usually is not satisfactory. Finally, the proposed timetabling system starts the search for a good solution that satisfies best interests of the department according to a cost function. GA is applied in search for a satisfactory course timetable based on a pre-defined criterion. The system has been developed and tested utilizing benchmarked datasets developed by an international timetabling competition (ITC2007) and for the Computer Engineering Department at Yarmouk University. In both cases, the algorithm showed very satisfactory results.

Generating university course timetables is a complex problem, especially in large environments such as institutions. Currently, some universities in Saudi Arabia manually generate timetables for classes because they use Vendor Management Systems (VMS) for registration and management. Manually generating course timetables is time-consuming and laborious for the academic staff. Although various methods have been proposed to generate timetables, they address specific environments or systems that can be extended to or work as separate components of the university management system. In this paper, we propose a service-based system with a decentralized architecture that can fully automate the process of course timetable generation and can be easily integrated into VMS. The proposed service-based system employs a genetic algorithm to optimize the process of scheduling courses and generating timetables. The system was implemented using JAVA RESTful web services, and the algorithm was tested by generating various course timetables with various constraints. The results showed that the proposed decentralized architecture is applicable to and can be fully integrated with any VMS. Furthermore, the use of genetic algorithm set up to 200 generations and iterate 1000 times produces acceptable timetables without violating any of the defined constraints.

Track 2 of the international timetabling competition 2007 was a post enrolment course timetabling problem. A set of events has to be assigned to a timeslot and to a room such that all students are able to attend their requested events while not violating the hard constraints. There are also soft constraints that make the timetable “nicer”. We present a deterministic heuristic that assigns events to timeslots based on an LP-solution constructed with column generation. We get an integer solution by fixing columns one at a time. This heuristic finds a solution that obeys all the hard constraint for 23 of the 24 instances of the competition. The generated solution is improved by selecting a set of events that are reassigned by solving an integer program. This IP minimizes the number of soft constraint violations under the restriction that no hard constraints are violated. Comparing the results of our heuristic with the results of the five finalists of the competition, shows that our approach is competitive.