Journal of Ubiquitous Computing and Communication Technologies
IRO Journals
Volume 7 — Issue 3 — September 2025

AI-Driven Dynamic Scheduling and Real-Time Notification System for Staff Optimization and Conflict Resolution

https://doi.org/10.36548/jucct.2025.3.005
Monesh S.
Department of Artificial Intelligence and Data Science, Velammal Institute of Technology, Chennai, India
Sheninth JR
Department of Artificial Intelligence and Data Science, Velammal Institute of Technology, Chennai, India
Sriram S.
Department of Artificial Intelligence and Data Science, Velammal Institute of Technology, Chennai, India
Abirami
Assistant Professor, Department of Artificial Intelligence and Data Science, Velammal Institute of Technology, Chennai, India
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How to Cite

S., Monesh, Sheninth JR, Sriram S., and Abirami. 2025. “AI-Driven Dynamic Scheduling and Real-Time Notification System for Staff Optimization and Conflict Resolution”. Journal of Ubiquitous Computing and Communication Technologies 7 (3): 313-26. https://doi.org/10.36548/jucct.2025.3.005.
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Keywords

— Timetable Generation
— Genetic Algorithm
— CSP
— Automation
— Scheduling System
— Conflict Resolution
— Faculty Allocation
Published: 14-10-2025

Abstract

In institutions, developing a timetable is a challenging and problematic process, particularly when creating it manually. When managing multiple departments, some common challenges arise like overlapping schedules, unequal staff allocation for classes and insufficient classroom usage. This proposed work presents an AI-driven solution to automate and optimize timetable scheduling in colleges by using intelligent algorithms. The research involves proposed methodologies that include Constraint Satisfaction Problem (CSP) solutions and Genetic Algorithms, allowing the system to allocate courses, faculty members, classrooms and time slots without any conflicts. This model provides an equal and optimal distribution by considering several limitations like faculty availability, workload balance and institution regulations. The proposed research uses the Genetic Algorithm that represents timetables as “chromosomes” evaluated using a fitness function based on moderate or severe limitations producing solutions through crossover and mutation. The system increases the accuracy of scheduling by reducing human effort. It also handles faculty or time alterations in real-time. This smart automation method improves scalability across institutions and creates a path for a smart educational management system. The proposed system provides a significant shift from manual techniques to data-driven scheduling, increasing educational planning and utilizing resources effectively.

References

  1. Sonawane, Ms Premlata A., and Leena Ragha. "Hybrid genetic algorithm and TABU search algorithm to solve class time table scheduling problem." International Journal of Research Studies in Computer Science and Engineering 1, no. 4 (2014): 19-26.
  2. Devi, M. Uma, J. Jayapradha, Bhaavya Naharas, and Saniya Sharma. "Automated timetable generation for academic institutions." In AIP Conference Proceedings, vol. 3075, no. 1, p. 020094. AIP Publishing LLC, 2024.
  3. Renggli, Fabienne Josefine, Maisa Gerlach, Jannic Stefan Bieri, Christoph Golz, and Murat Sariyar. "Integrating Nurse Preferences Into AI-Based Scheduling Systems: Qualitative Study." JMIR Formative Research 9, no. 1 (2025): e67747.
  4. Deris, Safaai, Sigeru Omatu, and Hiroshi Ohta. "Timetable planning using the constraint-based reasoning." Computers & Operations Research 27, no. 9 (2000): 819-840.
  5. Li, Yuanyuan, Stefano Carabelli, Edoardo Fadda, Daniele Manerba, Roberto Tadei, and Olivier Terzo. "Machine learning and optimization for production rescheduling in Industry 4.0." The International Journal of Advanced Manufacturing Technology 110, no. 9 (2020): 2445-2463.
  6. Skobelev, Petr. "Multi-agent systems for real-time adaptive resource management." In Industrial Agents, pp. 207-229. Morgan Kaufmann, 2015.
  7. Perera, K. M. P. B. N. "LECTURE HALL SCHEDULING AND TIMETABLE MANAGEMENT SYSTEM." PhD diss., 2021.
  8. Jeong, Ji-Seong, Mihye Kim, and Kwan-Hee Yoo. "A content oriented smart education system based on cloud computing." International Journal of Multimedia and Ubiquitous Engineering 8, no. 6 (2013): 313-328.
  9. Rane, Nitin. "Integrating building information modelling (BIM) and artificial intelligence (AI) for smart construction schedule, cost, quality, and safety management: challenges and opportunities." Cost, Quality, and Safety Management: Challenges and Opportunities (September 16, 2023) (2023).
  10. Gupta, Poonam, Omkar Prasad Ijardar, Ashish Jadhav, and Vansh Saheb. "AI-Based Solution To Enable Ease of Grievance Lodging and Tracking for Citizens Across Multiple Departments." In International Conference on Advances and Applications in Artificial Intelligence (ICAAAI 2025), Atlantis Press, 2025, 1002-1022.
  11. Ajayi, Rhoda. "Integrating IoT and cloud computing for continuous process optimization in real-time systems." Int J Res Publ Rev 6, no. 1 (2025): 2540-2558.
  12. Heggond, Shreeshail. "Artificial Intelligence and Machine Learning for Smart Construction: Enhancing Real-Time Monitoring and Decision Making." Available at SSRN 5233045 (2025).
  13. Taye, Gemechu, Sonal Sharma, Pratishtha Shah, and Yohanes Getinet Nuriye. "Exploring the role of artificial intelligence in class scheduling and management: a comprehensive survey and review." In 2023 International Conference on Computer Science and Emerging Technologies (CSET), IEEE, 2023, 1-11.