Quality recovering of university timetables

Abstract

At universities, the timetable plays a large role in the daily life of students and staff, showing when and where lectures are given. But whenever a schedule is executed in a dynamic environment, disruptions will occur. It is then desirable to find a new timetable similar to the old one, so only a few people will be affected. This leads to a minimum perturbation problem, where the goal is to find a feasible timetable by changing as few assignments as possible. This solution will, however, often lead to timetables of low quality as it can have many undesired features that will cause much inconvenience for effected parties.In this paper we show that minimum perturbation solutions often have low quality and how using additional perturbations results in timetables with significantly higher quality while still keeping the number of perturbations low, so the solutions can be practically implemented.We formulate a bi-objective model and propose a method to solve it by using mixed integer programming. We test the method on standard instances of the Curriculum-based Course Timetabling Problem with four different types of disruptions. The use of bi-objective optimization enables us to generate multiple solutions whereby we provide the decision makers with multiple choices for handling the disruption. This allows the decision makers to determine the best trade-off between the number of perturbations and the quality, ultimately leading to better timetables for students and staff when disruptions occur.