Timetable Scheduling Project

📚 Overview

Welcome to the Timetable Scheduling Project! This repository documents research, findings, discussions, and progress on creating an efficient timetable scheduling system that addresses real-world challenges in academic scheduling. Our goal is to develop a scheduling model that optimally assigns rooms, teachers, and time slots while accommodating various constraints.

🔍 Project Goals

• Optimal Assignment: Effectively allocate rooms, teachers, and time slots.
• Constraint Management: Consider teacher preferences, student load, room capacities, and subject assignments.
• Dynamic Adaptability: Adjust to changes, such as adding new rooms or teachers, without complete model retraining.
• Simplicity and Flexibility: Simplify complex constraints while remaining adaptable to real-world needs.

🧬 Genetic Algorithm

The project employs a Genetic Algorithm (GA) to solve the scheduling problem, inspired by the principles of natural selection. Key modules in our GA include:

1. Data Encoding and Decoding: Converting solutions into chromosomes for efficiency.
2. Initial Population: Generating a diverse set of potential solutions under specified constraints.
3. Evaluation of Population: Using a fitness function to assess solution quality.
4. Crossover Evolution: Creating new solutions by combining existing ones.
5. Mutation: Introducing random changes to explore new potential solutions.

Advantages

• Flexibility compared to manual systems
• Reduced processing power requirements
• Quick generation of accurate timetables
• User-friendly data entry and revision
• Improved productivity and minimal paperwork

Disadvantages

• Requires initial raw content input
• High memory consumption
• Internet connectivity needed

📋 Applications

The genetic algorithm can efficiently create various timetables, including:

• University Timetable
• Exam Timetable
• School Timetable
• College Timetable
• Teacher Timetable

📊 Results

The system generates distinct timetables for each class, faculty member, and lab, preventing slot conflicts and allowing customization. The program meets both hard and soft scheduling constraints.

⚙️ Fitness Function

The fitness function is a critical component of our Genetic Algorithm, as it evaluates the quality of each timetable solution. It incorporates both hard and soft constraints to ensure a balanced and effective scheduling outcome.

Hard Constraints

These are strict requirements that must be satisfied for a solution to be considered valid:

1. No Overlap:

   • Teachers cannot be assigned to multiple classes at the same time.
   • Time slots must not overlap for the same section.
   • The same classroom cannot be allocated to multiple sections during the same time slot.

2. Room Capacity:

   • Each classroom's capacity must accommodate the number of students assigned to that class.

3. Time Slot Allocation:

   • Classes must be scheduled within designated time slots.

4. Teacher Load Distribution:

   • The total hours assigned to a teacher should not exceed their maximum allowable teaching hours.

Soft Constraints

These are preferences that enhance the quality of the solution but are not mandatory:

1. Teacher Preferences:

   • Assignments should consider teacher preferences for certain time slots or days.

2. Gaps Between Classes:

   • Minimizing the number of gaps between classes for teachers is preferred.

3. Balanced Workload:

   • Aim for a balanced distribution of classes among teachers to prevent overloading any single teacher.

4. Half Days for Sections

   • Every section should be provided half days so that resources can be utilised properly and also labs and calsses should be balanced.

Fitness Score Calculation

The fitness score is calculated based on the following criteria:

• Each hard constraint violation deducts a significant penalty from the score (e.g., -100 points for each violation).
• Soft constraint preferences add to the score (e.g., +10 points for each preference met).
• The final score is a reflection of both the number of constraints satisfied and the overall quality of the timetable.

A higher fitness score indicates a better solution, leading to more optimal timetables. The Genetic Algorithm iteratively evolves the population of solutions to maximize this fitness score over generations.

🚀 Future Scope

This project aims to simplify faculty management by automatically assigning courses, considering workload balance, and tracking expertise. Future enhancements may include a master schedule for departments, integration with university websites, and personalized time slot assignments.

🛠️ Installation

To run this project, clone the repository, set up a virtual environment, and install the required dependencies:

git clone https://github.com/yourusername/timetable-scheduling-project.git
cd timetable-scheduling-project

# Set up a virtual environment
python -m venv venv

# Activate the virtual environment
# For Linux/macOS:
source venv/bin/activate
# For Windows:
.\venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

# Optionally, set up environment variables in a .env file (e.g., DATABASE_URL, SECRET_KEY)
# Create and edit .env file with necessary variables
touch .env

echo "Setup complete. You can now run the application."