AcadTime: Automatic Timetable Generator

Abstract

This paper presents an automated solution to the challenges faced by educational institutions during manual timetable creation through the development of an Automatic Timetable Generator using our custom algorithm based on Progressive Constraint Placement and Iterative Repair (PCP-IR).The main issue addressed is the complexity and high chance of conflicts when assigning teachers, rooms, and subjects by hand. Our approach begins by placing the hardest and most restrictive constraints first, progressively building a stable timetable structure before adding the remaining sessions. This ensures that most major conflicts area voided early in the process, making the initial timetable more accurate and reliable. This paper further highlights how the iterative repair mechanism refines the timetable by detecting left over clashes and resolving them using systematic adjustments or swaps. Extensive testing and evaluation we optimize the performance of the PCP-IR algorithm, ensuring consistent and conflict-free scheduling. The outcomes demonstrate that our method offers a user-friendly, efficient, and intelligent timetable generation system, significantly improving speed, reducing errors, and enhancing overall scheduling quality compared to traditional manual methods.

Introduction

The Automatic Timetable Generator is a software system designed to automate timetable creation for schools and colleges, eliminating the challenges of manual scheduling such as teacher overlaps, room conflicts, and time-consuming planning. The system uses a custom algorithm called Progressive Constraint Placement and Iterative Repair (PCP-IR), which first schedules the most difficult constraints and then intelligently resolves any remaining conflicts.

The system consists of four main components: a scheduling engine, constraint-processing module, conflict-detection unit, and PCP-IR repair module. Inputs such as teacher availability, subjects, rooms, and time slots are processed to create an optimized and conflict-free timetable.

The PCP (Progressive Constraint Placement) technique assigns the most restrictive scheduling elements first, such as teachers with limited availability and compulsory subjects. This reduces the likelihood of major conflicts. The IR (Iterative Repair) module then detects issues like overlapping classes, double-booked rooms, or teacher clashes and fixes them through targeted adjustments and time-slot swaps rather than rebuilding the entire schedule.

The methodology ensures efficient allocation of teachers, classrooms, and subjects while satisfying hard constraints and optimizing preferences. The modular design also allows easy updates when new teachers, subjects, or scheduling requirements are added.

Results showed that the PCP-IR algorithm successfully generated conflict-free timetables, significantly improved scheduling accuracy, and reduced the need for manual intervention. Resource utilization was optimized, scheduling time was greatly reduced compared to manual methods, and timetables could be generated within seconds even for large datasets. User feedback from faculty and administrators was highly positive, particularly regarding workload reduction and intelligent scheduling decisions.

Conclusion

This Automatic Timetable Generator was designed to help institutions overcome the challenges of manual timetable creation. Through the implementation of our PCP- IR algorithm, the system effectively handles complex constraints, reduces scheduling conflicts, and automates the timetable generation process. The combination of intelligent placement and iterative repair has proven to be a robust and reliable solution for academic scheduling. In conclusion, the developed system demonstrates strong potential in improving institutional productivity, ensuring conflict-free timetables, and supporting efficient academic planning.

References

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Copyright

Copyright © 2026 Prof. Shweta Shondawale, Anupam Modi, Priyanshu Raj, Priyanka Dhumal, Samyukta More. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.