Abstract:
Manual duty scheduling in organizations is a complex, time- consuming processprone to human error, scheduling conflicts, and inequitable workloaddistribution. This project presents the design and implementation of anintelligent system to automate and optimize duty roster generation. The systememploys a dual-stage approach. Initially, a rule-based algorithm generates afoundational timetable by processing predefined constraints, such as staffavailability, role requirements, and fixed holidays. This baseline schedule isthen enhanced by a predictive AI model. Leveraging machine learning, the AIcomponent analyzes historical data and employee patterns to predict and assignduties to the most suitable personnel, aiming to balance workloads andimprove operational efficiency. Key functionalities include a fully automatednotification system that informs staff of their assigned duties via email or SMS,and the generation of comprehensive analytical reports for management. Theproposed system seeks to significantly reduce administrative overhead,minimize scheduling errors, and ensure a fair, transparent, and efficientworkforce management solution.
Page(s):
131-131
DOI:
DOI not available
Published:
Journal: 4th International Conference of Sciences “Revamped Scientific Outlook of 21st Century, 2025” , November 12,2025, Volume: 1, Issue: 1, Year: 2025
Keywords:
IoT
,
Automated Duty Scheduling
,
AIPowered System
,
time consuming process