An Ant Colony Optimisation Algorithm For Timetabling Problem

 ABSTRACT

The University Course Timetabling Problem (UCTP) is a combinatorial optimization problem which involves the placement of events into timeslots and assignment of venues to these events. Different institutions have their peculiar problems; therefore there is a need to get an adequate knowledge of the problem especially in the area of constraints before applying an efficient method that will get a feasible solution in a reasonable amount of time. Several methods have been applied to solve this problem; they include evolutionary algorithms, tabu search, local search and swarm optimization methods like the Ant Colony Optimisation (ACO) algorithm. A variant of ACO called the MAX-MIN Ant System (MMAS) is implemented with two local search procedures (one main and one auxiliary) to tackle the UCTP using Covenant University problem instance. The local search design proposed was tailored to suit the problem tackled and was compared with other designs to emphasise the effect of neighbourhood combination pattern on the algorithm performance. From the experimental procedures, it was observed that the local search design proposed significantly bettered the existing one used for the comparison. The results obtained by the implemented algorithm proved that metaheuristics are highly effective when tackling real-world cases of the UCTP and not just generated instances of the problem and can even be better if some tangible modifications are made to it to perfectly suit a problem domain.