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@article{176322,
        author = {Vinayak Khandelwal and Versha Dubey and Priya Goyal and Indra Kishor},
        title = {Automatic Time Table Generator: Revolutionizing Scheduling Efficiency},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {11},
        pages = {6910-6920},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=176322},
        abstract = {That traditional manual methods prove to be time-consuming, error-prone, and unable to deal with complex constraints makes efficient scheduling an essential need in the times of academic institutions and organizational setups. ATTGs are a transformative solution that automate scheduling processes, optimum resource allocation for better stakeholder satisfaction. This paper synthesizes insights from the existing methodologies to propose a novel Hybrid Constraint-Satisfaction Algorithm, based on rule-based systems, heuristic optimization techniques, and AI-driven models. By leveraging heuristic methods, genetic algorithms, and reinforcement learning, HCSA achieves high conflict resolution (98%) and scalability (90%), with average timetable generation time being 5 seconds. The proposed system shall provide scalability for large datasets, adaptability to real-time changes, and incorporation of stakeholder feedback for iterative improvements. The paper also discusses some aspects of ethical considerations, such as data privacy in cloud-based implementations and mitigation of biases in scheduling through AI. Experiments using synthetical datasets demonstrated that the solution is indeed viable as satisfaction rates of students, faculties, and administrators were scored 90%, 88%, and 92% respectively. This research now opens possibilities for ATTGs to change the way scheduling is done with embracing new technologies, good ethics, and usability-led design. More ahead, it encompasses federated learning towards Scheduling with collaboration, multi-objective optimization, and increased transparency of AI models for wide acceptance in multiple domains.},
        keywords = {Constraint-Satisfaction algorithm, Heuristic algorithms, Genetic optimization, Computational Efficiency, Artificial Intelligence},
        month = {April},
        }