COMPARISON USING PARTICLE SWARM OPTIMIZATION AND GENETIC ALGORITHM FOR TIMETABLE SCHEDULING

Abstract

Lecturer timetable scheduling is an important part in the resource allocation planning. Due to the large amount of transactions and various related constraints have to be taken into account in timetable scheduling process, resource manager team shall need a lot of time to the solve the problem. This research is aimed to discuss the application of Particle Swarm Optimization (PSO) that can be used to automatically generate optimal lecturer timetable scheduling. Using Software Laboratory Center (SLC) data, some hard constraints are taken into account such as the assistant should teach according to their qualifications, teaching in their work shift and doesn’t teach any course that are being taken. Some soft constraints are also considered and the associated cost function is built based on these hard and soft constraints. Based on the computational results, the amount of penalty obtained by the PSO is much smaller than the GA on 500th iteration. The calculation is performed by comparing the amount of penalty that earned each time a hard constraint or soft constraint is violated by the implementation of PSO or GA to the total penalty obtained when all constraints are violated.