Incorporating Teacher’s Preferences and Student Time Management in University Course Timetabling

Abstract

University course timetabling is a challenging task today due to the mushrooming of a variety of courses with flexible timings in different streams and disciplines in global universities. The challenge is to devise non-overlapping class schedules for students enrolled in different courses with the available and limited resources of teachers and classrooms. The task of automated timetable generation is a constrained optimization problem. In any optimization problem, the hard constraints are those that need to be mandatorily satisfied. The soft constraints are the penalties that are sought to be minimized in every iteration of the optimization algorithm. In this paper, we propose a novel set of soft constraints for university course timetabling that in addition to the conventional constraints, incorporates teacher’s preferences and student time management as well. The latter constraint takes the form of minimizing student mobility between classrooms and restricting time gaps between classes to ensure that student’s time on-campus is fully utilized. The evolutionary algorithms- Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Simulated Annealing (SA) are used for optimizing the course schedules. The timetables so generated, based on actual university data, are found to be more humanely optimized than the previous work of the authors due to the incorporation of human factor consideration both from the perspective of teachers and students.