Development of a Model for Generation of Examination Timetable Using Genetic Algorithm

Abstract

Examination time table scheduling problem is one of the complexes, NP-complete and typical combinatorial optimization problem faced by the university community across the globe. Many researchers have studied the problem due to its NP-complete nature and highly- multi-constrained problem which seeks to find possible scheduling for courses. Creating an examination timetable for university is a very difficult, time-consuming and the wider complex problem of scheduling, especially when the number of students and courses are high. Several factors are responsible for the problem: increases number of students, the aggregation of schools, changes in educational paradigms, among others. In most universities, the examination time table schedule is usually ended up with various courses clashing with one another. In order to solve this problem of time table scheduling for University examination and effective utilization of resources, this research proposed a model for examination time table generation using Genetic Algorithm (GA) probabilistic operators. GA has been successful in solving many optimization problems, including University time table. This is based on the fact that GA is accurate, precise, free from human error and robust for complex space problem. GA theory was also covered with emphasis on the use of fitness function and time to evaluate the result. The effects of altered mutation rate and population size are tested. By using Genetic algorithm, we are able to reduce the time required to generate a timetable which is more accurate, precise and free of human errors. The implication of this research is a solution, minimizing the time taken in timetable allocation and the clashing that usually characterize time table schedule.