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The development of efficient and conflict-free timetables is a challenging task for educational institutions. The traditional manual process of timetable development consumes a considerable amount of time and may result in errors, which may cause an imbalance in the workload and resource underutilization. This research work proposes the development of an AI-based academic timetable development and analysis system that can automate the process of timetable development, document parsing, and performance analysis to overcome the aforementioned limitations. The proposed system will develop constraint-satisfied timetables from structured data using a heuristic algorithm for timetable development, ensuring uninterrupted lab sessions, balanced daily theoretical classes, and faculty and room allocations without any conflicts. Additionally, a transformer-based semantic information extraction module will be utilized to extract heterogeneous information from unstructured Portable Document Format and document files into structured data using the process of prompt engineering and domain rule validation. The machine learning analytics module further improves the system by carrying out faculty workload prediction, clustering, conflict identification, substitute suggestion, and timetable quality assessment. The experimental result on the institutional dataset showed the accuracy of the timetable generation procedure with respect to hard constraints to be 98.7 percent, conflict identification precision to be 97.9 percent, and overall workload balance improvement of 23 percent compared to the manual method. The proposed system greatly minimizes the administrative burden and maximizes efficiency in the scheduling procedure.
Keywords:
Workload Optimization, Rule-Based Information Modeling, Transformer-Based Document Extraction, Heuristic Scheduling Algorithms, Academic Timetable Creation and Machine Learning Analytics.
Cite Article:
"Smart Academic TimeTable Generator Using Heuristic Scheduling Algorithm", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2456-3315, Vol.11, Issue 4, page no.c542-c549, April-2026, Available :http://www.ijrti.org/papers/IJRTI2604337.pdf
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ISSN:
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator