Automated Timetable Scheduler
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Date
2025
Journal Title
Journal ISSN
Volume Title
Publisher
UMT, Lahore
Abstract
Timetable scheduling in educational institutions is time-consuming and error-prone when done manually. The need to accommodate various constraints—room capacity, instructor availability, course prerequisites, and even non-teaching staff shifts—further increases complexity. Our research aimed to build an AI-based Automatic Schedule Maker capable of generating optimized, conflict-free timetables. In Phase 1, we began by experimenting with large language models (LLMs) via prompt engineering, to determine if they could autonomously generate timetables. While this approach showed promise, accuracy issues and limited adaptability led us to explore rule-based bots (like Edobot) and web-based solutions (like Unitime). Finally, we developed a Python-based scheduling algorithm that integrates AI-driven constraint-solving to automatically assign courses, rooms, labs, teachers, and staff shifts, all while preventing clashes. We validated this approach using Excel-based data input (courses, rooms, student counts), ensuring the solution is scalable to wider institutional needs. Our results show that the system substantially reduces manual workload, minimizes scheduling conflicts, and lays the groundwork for broader, AI-enhanced resource allocation in academic settings. Phase 2 expanded our research with a comprehensive dual-algorithmic framework, implementing both neural network-guided scheduling and genetic algorithm-based evolutionary optimization. Our genetic algorithm achieved optimal solutions with zero conflicts of test cases, demonstrating superior performance in complex constraint scenarios compared to traditional heuristic methods