A New Hybrid Intelligent Strategy for University Course Timetabling Problem for Real Applications

The university course timetabling problem (UCTP) is a hard combinatorial optimization problem. Because the search space of this problem is very large، human timetabling is a sophisticated job; as a result، there have been many attentions over the recent years from computer researchers to present an automated timetabling approach for UCTP. In a general point of view، UCTP is a problem in which some courses must be assigned to valid timeslots. There are some constrains and preferences in this problem. Most constraints and preferences come from curriculum of teachers and students. If a timetable satisfies all constraints، it is called a feasible timetable. In UCTP، the outcome timetable must be a feasible timetable. The less violation preferences have، the better timetable would be created. Since UCTP is a constraint satisfaction problem (CSP)، in this paper in order to create feasible timetables، a novel CSP-based algorithm is presented. In addition، a genetic algorithm (GA) combined with local search methods is used to optimize the feasible timetables. The experimental results show that the suggested method produces appropriate results in a reasonable time considering a real data with large state space as input data.