International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | AutoSchedule AI: Intelligent Academic Timetable Generation System Using Genetic Algorithm and Constraint-Based Optimization |
|---|---|
| ABSTRACT | Scheduling academic timetables is one of the most challenging problems that schools and universities deal with on a regular basis. Doing it manually takes a great deal of time, often leads to mistakes, and simply doesn't hold up as institutions grow more complex. This paper introduces AutoSchedule AI — a web-based scheduling platform that takes the heavy lifting out of timetable creation by automatically generating conflict-free, well-optimized academic schedules. It does this by combining a Genetic Algorithm (GA) with constraint satisfaction techniques, striking a balance between computational intelligence and practical usability. The system works by taking in essential inputs — things like subject details, teacher availability, classroom capacities, lab requirements, and any institutional rules that need to be respected — and then runs an evolutionary optimization process to build schedules that avoid conflicts while making the best possible use of available resources. Rather than requiring any server infrastructure, the platform runs entirely in the browser using HTML, CSS, and JavaScript, making it lightweight, easy to deploy, and accessible across different devices and operating systems. Among its standout features are a guided step-by-step input setup, flexible management of labs and subjects, built-in conflict detection, support for exporting schedules in JSON or CSV formats, and a real-time timetable display that lets users see results as they're generated. When tested across a range of constraint scenarios, the system consistently produced valid, balanced timetables with zero teacher or classroom conflicts — and did so in under a second for typical institutional use cases. At its core, AutoSchedule AI is about making a genuinely difficult administrative task more manageable. Efficient timetable generation matters enormously for how colleges and universities function day to day, and this system addresses that need by automating the process intelligently. It cuts down on manual effort, eliminates the usual scheduling headaches around faculty clashes and room overlaps, and ensures that subjects are distributed evenly. Its browser-based design means there's virtually no barrier to getting started — it's fast, accessible, and flexible enough to work for institutions of varying sizes. |
| AUTHOR | PROF. MANJULA P, SHILPA, SHOBHITHA B N, TEJASHWINI K, SHILPA C JAVALI Assistant Professor, Dept. of CSE, Jain Institute of Technology, Davangere, Karnataka, India UG Students, Dept. of CSE, Jain Institute of Technology, Davangere, Karnataka, India |
| VOLUME | 183 |
| DOI | DOI: 10.15680/IJIRCCE.2026.1404092 |
| pdf/92_AutoSchedule AI Intelligent Academic Timetable Generation System Using Genetic Algorithm and Constraint-Based Optimization.pdf | |
| KEYWORDS | |
| References | [1] Srinivasan, D., Seow, T. H., and Xu, J. X., "Automated Time Table Generation Using Multiple Context Reasoning for University Modules," IEEE Transactions on Evolutionary Computation, 2025. [2] Bagul, M. R., Chaudhari, S. C., Nagare, S. N., Patil, P. R., and Kumavat, K. S., "A Novel Approach for Automatic Timetable Generation," International Journal of Advanced Research in Computer Science, vol. 15, no. 2, 2024. [3] Markal, S., Ghorpade, S., and Chalke, D., "Timetable Generator Using Genetic Algorithm," IRJMETS, April 2023. [4] Wankhede, S., Sahare, A., Korde, M., Raut, N., and Ladke, U., "Automatic College Timetable Generation," International Journal of Engineering Research and Technology, vol. 11, no. 3, 2022. [5] Samah, K. A. F. A., Thusree, S. Q., Fadzil, A. F. A., Riza, L. S., Ibrahim, S., and Hasan, N., "A Greedy-based Algorithm in Optimizing Student's Recommended Timetable Generator with Semester Planner," IJACSA, Vol. 13, No. 1, 2022. [6] Rane, M. V., Apte, V. M., Nerkar, V. N., Edinburgh, M. R., and Rajput, K. Y., "Automated Timetabling System for University Course," IEEE, 2021. [7] Techie-Menson, H. and Nyagorme, P., "Design and Implementation of a Web-Based Timetable System for Higher Education Institutions," ResearchGate, March 2021. [8] Abulaziz, A., Caesarendra, W., Haruna, U. S., Sani, A., Sa'id, M., Pamungkas, D. S., Kurniawan, S. R., Kurniawan, E., "Design and Implementation of an Automatic Examination Timetable Generation and Invigilation Scheduling System Using Genetic Algorithm," IEEE, 2024. [9] Mhaise, G. D., Kurhade, C. S., Devre, M. Y., Sonawane, P., and Parasram, T. B., "Automatic Time Table Generator," IRJMETS, April 2023. [10] Holland, J. H., "Adaptation in Natural and Artificial Systems," University of Michigan Press, 1975. [11] Goldberg, D. E., "Genetic Algorithms in Search, Optimization and Machine Learning," Addison-Wesley, 1989. [12] Burke, E. K. and Petrovic, S., "Recent research directions in automated timetabling," European Journal of Operational Research, vol. 140, no. 2, pp. 266-280, 2002. [13] de Werra, D., "An introduction to timetabling," European Journal of Operational Research, vol. 19, no. 2, pp. 151-162, 1985. [14] Schaerf, A., "A survey of automated timetabling," Artificial Intelligence Review, vol. 13, no. 2, pp. 87-127, 1999. [15] Colorni, A., Dorigo, M., and Maniezzo, V., "A genetic algorithm to solve the timetable problem," Technical Report, Politecnico di Milano, 1992. |
