Time Table Scheduler

This project leverages capabilities of Genetic Algorithms (GAs) and Artificial Intelligence (AI) to address the complexities of university timetable scheduling, a challenging NP-hard optimization problem. Targeting academic institutions facing intricate scheduling demands, the system provides an adaptive solution that optimizes schedules for a single batch, accommodating diverse hard and soft constraints.

Features

• Secure login functionality for administrators
• Intuitive interface for administrators to input course requirements and faculty assignment.
• Strategic optimization with GA by assigning varied weights to manage constraint violations of varying significance and introducing mutations to minimize losses.
• Presents a visually appealing and clear timetable for effective comprehension.

Prerequisites

• Python programming language

• Front-end technologies - HTML, CSS, and JavaScript for creating a responsive and user-friendly interface.

• Firebase provides a NoSQL cloud database that allows developers to store and sync data in real-time across connected clients and offers tools for user authentication.

• Flask for web application development.

• Genetic Algorithm and Constraint Satisfaction Problem Algorithm

Installation

git clone https://github.com/thanujaashok/Timetablescheduler.git

Test Run

Attention: Ensure the corresponding faculty details are uploaded

References

Research Papers:

• Colorni, Alberto & Dorigo, Marco & Maniezzo, Vittorio. (1994). A Genetic Algorithm To Solve The Timetable Problem
• C. H. Wong, S. L. Goh and J. Likoh, "A Genetic Algorithm for the Real-world University Course Timetabling Problem," 2022 IEEE 18th International Colloquium on Signal Processing & Applications (CSPA), Selangor, Malaysia, 2022.
• Asmaa Houar, Yassir Bensmain, Talib Hicham Betaouaf, "Scheduling pedagogical tasks for university timetables: A field study", 2022 14th International Colloquium of Logistics and Supply Chain Management (LOGISTIQUA)
• Deris, S., Omatu, S., Ohta, H. and Saad, P., 1999. Incorporating constraint propagation in genetic algorithm for university timetable planning. Engineering applications of artificial intelligence.

Genetic Algorithm:

• Smith, J. (Year). "Introduction to Genetic Algorithm and Python Implementation for Function Optimization." Towards Data Science.
• Brown, M. (Year). "Simple Genetic Algorithm from Scratch in Python." Machine Learning Mastery