An Innovative Two-Phase Local Search Approach for Efficient University Course Scheduling under Academic Constraints

Abstract

Scheduling university courses represents a formidable challenge, given the multifaceted academic constraints inherent to higher education institutions. This paper addresses the intricate task of optimizing university course scheduling, which involves accommodating numerous constraints, such as classroom and faculty availability. While numerous methodologies have been proposed to tackle this problem, our work introduces a novel approach employing a two-phase local search method. In the initial phase of our method, a viable solution is generated, laying the foundation for a well-structured course schedule. Subsequently, in the second phase, we refine the generated schedule using carefully crafted techniques to enhance its overall quality. Our approach has been meticulously implemented and rigorously evaluated across various datasets, yielding promising results. The outcomes of our study underscore the efficacy of our two-step methodology in resolving the complex challenge of university course scheduling. This research contributes a robust solution to aid academic institutions in streamlining their course scheduling processes while adhering to academic constraints.