Genetic Algorithm and Tabu Search Memory with Course Sandwiching (GATS_CS) for University Examination Timetabling

Abstract

University timetable scheduling is a complicated constraint problem because educational institutions use timetables to maximize and optimize scarce resources, such as tine and space. In this paper, an examination timetable system using Genetic Algorithm and Tabu Search memory with course sandwiching (GAT_CS), was developed fora lame public University. The concept of Genetic Algorithm with Selection and Evaluation was implemented while the memory properties of Tabu Search and course sandwiching replaced Crossover and Mutation. The result showed that GAT_CS had hall allocation accuracies of 96.07% and 99.02%, unallocated score of 3.93% and 0.98% for first and second semesters, respectively. It also automatically sandwiched (scheduled) multiple examinations into single halls with a simulation time in the range of 20-29.5 seconds. The GAT_CS outperformed previous related works on the same timetable dataset. It could, however, be improved to reduce clashes, duplications, multiple examinations and to accommodate more system-defined constraints.