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Optimizing Student Course Preferences in School Timetabling

Conference paper
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Part of the Lecture Notes in Computer Science book series (LNCS, volume 12296)

Abstract

School timetabling is a complex problem in combinatorial optimization, requiring the best possible assignment of course sections to teachers, timeslots, and classrooms. There exist standard techniques for generating a school timetable, especially in cohort-based programs where students take the same set of required courses, along with several electives. However, in small interdisciplinary institutions where there are only one or two sections of each course, and there is much diversity in course preferences among individual students, it is very difficult to create an optimal timetable that enables each student to take their desired set of courses while satisfying all of the required constraints.

In this paper, we present a two-part school timetabling algorithm that was applied to generate the optimal Master Timetable for a Canadian all-girls high school, enrolling students in 100% of their core courses and 94% of their most desired electives. We conclude the paper by explaining how this algorithm, combining graph coloring with integer linear programming, can benefit other institutions that need to consider student course preferences in their timetabling.

Keywords

School timetabling Post Enrollment Course Timetabling Problem Integer Programming Graph coloring Optimization 

Notes

Acknowledgments

The authors thank the reviewers for their insightful comments that significantly improved the final version of this paper. We are grateful to Darlene DeMerchant and Megan Hedderick at St. Margaret’s School for making this collaboration possible. Finally, we acknowledge that the student author’s research was sponsored by a Student Project Grant awarded by the Research and Scholarly Works Committee at Quest University Canada.

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Quest University CanadaSquamishCanada

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