Authors : Mohammad Kaif; Riyaz Ahmed Ruheena Tabasum; S. B. Nusrath Fathima; Pushpa Mohan

Volume/Issue : Volume 11 - 2026, Issue 8 - August

Google Scholar : https://tinyurl.com/e3xd6xsj

Scribd : https://tinyurl.com/bddy8pvh

DOI : https://doi.org/10.38124/ijisrt/26aug357

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Abstract : Timetable scheduling in educational institu-tions is a complex and critical task that involves allocating subjects, teachers, classrooms, and time slots while satisfy-ing multiple constraints. Traditional manual methods often lead to conflicts such as overlapping classes, improper class-room allocation, and uneven workload distribution among faculty members. Over the years, several approaches have been proposed to solve this problem, including optimiza-tion techniques, constraintbased scheduling, genetic algo-rithms, and artificial intelligence-based systems. This paper presents a comprehensive literature sur-vey of existing methods used for solving the university course timetabling problem. Various techniques such as Mixed Integer Linear Programming (MILP), optimization models, graph coloring, particle swarm optimization, and hybrid metaheuristic approaches are analyzed in detail. Each method is evaluated based on efficiency, scalability, flexibility, and ability to handle real-time changes. The analysis reveals that while optimization-based ap-proaches provide highly accurate and conflict-free sched-ules, they lack adaptability to dynamic environments.On the other hand, AI-based techniques offer flexibility but require higher computational resources. Most of the exist-ing systems also fail to provide features such as real-time updates, automatic teacher substitution, and intelligent notification systems. To overcome these limitations, a smart staff schedul-ing and timetable management system is proposed. The proposed system integrates artificial intelligence with opti-mization techniques to generate efficient timetables, detect conflicts, allocate classrooms dynamically, and provide real-time notifications to users. This approach aims to improve scheduling efficiency, reduce manual effort, and enhance the overall academic management process.

Keywords : Artificial Intelligence, Staff Scheduling, University Course Timetabling, Genetic Algorithm, Ex-Plainable Artificial Intelligence, Educational Analytics, Recommendation System, Smart Campus, Academic Man-Agement.

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