A Hybrid Genetic Algorithm for Course Scheduling and Teaching Workload Management

Course scheduling is a common problem of all higher educational institutions in several developing countries. Most of these institutions experience a shortage of resources such as teachers and infrastructures. Having small time to recruit, institutions likely to resort in employing beginners to avoid overloading and to fill vacancies immediately. However, directly reassigning classes from senior teacher to the new teacher may lead to unmatched skills required to teach a particular course and more constraints violations. This study presents a hybrid genetic algorithm for course scheduling and teaching workload management. The genetic algorithm is employed with four neighboring operators identified using self-adaptive mechanism during the search process. A data structure of unused resources is used to guide the operators to unused periods. Repair operator is applied to increase the optimality of the solutions. Results show that the proposed algorithm generates feasible and optimized workloads and timetables. The automated system can relieve the decision-makers from the burden of tedious and time-consuming scheduling task in every semester. Hiring additional teaching staff and managing workloads are now much more convenient.