Course Scheduling using Genetic Algorithm

Abstract

Traditional manual scheduling methods often fail to meet the diverse constraints and preferences involved, leading to inefficiencies and suboptimal outcomes. This project aims to revolutionize course scheduling by implementing an automated system powered by genetic algorithms. These algorithms, inspired by natural selection, provide a robust optimization technique for navigating complex scheduling challenges. They efficiently explore and identify optimal solutions tailored to the unique needs of educational institutions. Manual scheduling is hampered by conflicting preferences, limited room availability, and tight time constraints, which can result in conflicts, resource wastage, and stakeholder dissatisfaction. The proposed automated system addresses these issues by systematically generating schedules that consider factors such as room availability, faculty preferences, and student enrollment, thus optimizing resource allocation and minimizing conflicts. The system offers a user-friendly interface for administrators to define scheduling objectives and constraints, allowing for quick adaptation to changing requirements. This project promises to enhance scheduling efficiency, reduce conflicts, and improve the quality of education delivery through automation and optimization.