Year: 2024
Type: article
Abstract: The creation of academic timetables is a complex and time-consuming task for educational institutions, requiring adherence to various constraints such as university regulations, faculty workloads, and resource availability. Traditional manual methods are often inefficient and error-prone, making it essential to develop automated solutions. This paper proposes an intelligent timetable generation system that leverages advanced optimization algorithms, including Evolutionary Algorithms, Tabu Search, Simulated Annealing, and Scatter Search, to address the challenges associated with manual scheduling. The system takes inputs such as semester-wise subjects, faculty availability, and workload constraints to dynamically generate conflict-free and resource-efficient timetables. Designed to accommodate multiple courses and semesters, the proposed system ensures scalability, flexibility, and compliance with institutional policies. By integrating automation with customizable features, the system reduces administrative effort, enhances accuracy, and optimally utilizes available resources. This paper presents the design, implementation, and evaluation of the system, highlighting its ability to adapt to dynamic requirements and provide a robust solution for academic scheduling. Key Words: Time tabling, Scheduling, Dynamic Scheduling, Conflict-Free Scheduling, Academic Timetabling, Timetable Generator.
DOI: 10.55041/ijsrem40166
Language: English
Field: Decision Sciences
Domain: Social Sciences
SDG: Quality education
Open Access status: bronze
OpenAlex ID: W4405836853