A novel timetabling and course allocation methodology

In academics, course allocation and timetabling remain as a critical challenge due to its NP- hard complexity. With several constraints, designing an efficient course scheduling is a non- trivial task. In this project proposes a novel workload model by incorporating various constraints to dynamically quantify faculty satisfaction metric. We then employ our proposed model and apply Simulated Annealing based optimization algorithm to generate course allocation with balance distribution of workload and improved satisfaction metric. Furthermore, we use Genetic algorithm to schedule the courses so as to benefit both teaching faculties and students. Finally, we have performed extensive experiments to validate the efficiency of our proposed algorithms in producing effective course allocation and scheduling. The evaluation focus on the faculty’s workload variation and timetable friendliness between faculty and students in an institution.Bachelor of Engineering (Computer Science