A Two-Phase Local Search Approach for Optimizing University Course Scheduling under Academic Constraints

Abstract

Efficiently scheduling university courses while accommodating a plethora of academic constraints poses a substantial challenge for educational institutions. This paper addresses the intricacies of course scheduling, acknowledging the presence of multiple restrictions, encompassing class and faculty constraints. Existing methodologies have proposed solutions to this complex optimization problem. In this research, we introduce a novel two-phase local search approach designed to tackle the university course scheduling problem. Our approach unfolds in two distinct phases. Initially, we generate a feasible solution to the scheduling problem. Subsequently, in the second phase, we enhance the solution's quality through the application of refined optimization techniques. We have implemented this method across diverse datasets and meticulously scrutinized the outcomes. Our empirical results underscore the efficacy of the proposed two-step approach in delivering high-quality solutions to the intricate problem of university course scheduling.