A constraint programming approach for fair course planning in high school

Abstract: Inequality problems in course scheduling in American high schools did not exist before. However, in recent years, with the increase of student population and course diversity, students feel that the course scheduling method is unfair. Current integer programming (IP) methods for the high school scheduling problem (HSSP) fail to address these fairness issues. The aim of this research is to develop a solution methodology that generates feasible and fair course schedules using students’ preferences. Using the fairness principles, which have been well studied in market design, we define the fair high school scheduling problem (FHSSP), a new extension of the HSSP, and design a corresponding algorithm based on integer programming to solve the FHSSP. We test our approach on a dataset of real course demands from a high school in California, USA. The results show that our algorithm can generate schedules that are both feasible and fair. In this paper, we demonstrate that our IP algorithm not only solves the HSSP and FHSSP problems in the United States, but has the potential to be applied to various real-world scheduling problems. Furthermore, we show the feasibility of integrating human emotions into mathematical modeling.