A Genetic Algorithm Approach for a Real-World University Examination Timetabling Problem

Abstract

the introduction of modularity, increasing student numbers and the continued expansion of university departments, space in Nigerian Universities is becoming an increasingly precious commodity. To address this, some institutions have tried to ensure efficient space utilization by employing different proposed solutions to space allocation problems especially during examination period. A number of approaches have been explored in the casting of examination timetables for academic institutions. The approach to be discussed here applies genetic algorithm using hierarchy of constraints. This hierarchy can incorporate individual requests or organizational requirements by weighing them according to some criteria. In this paper, we present a new real-world examination timetabling dataset at the University of Agriculture, Abeokuta Nigeria that will hopefully be used as a future benchmark problem. In addition, a new objective function that attempts to spread exams throughout the examination period is also introduced. This objective function that taking into account both timeslots and days assigned to each exam, is different from the often used objective function from the literature that only considers timeslot adjacency. Also room capacity for each room is included in the examination datasets specification. This approach has been tested with real data from the university and numerical results is presented and discussed. General Terms: Scheduling, Algorithms, NP hard