College Department Timetable Generator

A web-based application for generating optimal college department timetables using Genetic Algorithm optimization, built with Python Streamlit.

Features

• CSV Data Upload: Easy upload interface for Faculty, Courses, Rooms, and Batch data
• Constraint Configuration: Adjustable parameters for working days, periods, and GA optimization
• Genetic Algorithm Optimization: Advanced GA implementation with fitness-based optimization
• Real-time Progress Tracking: Live visualization of fitness progression during generation
• Multiple View Options: Timetable display by Batch, Room, or Faculty
• Export Functionality: Download timetables as Excel or CSV files
• Data Validation: Comprehensive validation with detailed error reporting

Quick Start

1. Run the Application:

   streamlit run app.py --server.port 5000

2. Upload Sample Data:

   • Navigate to "Upload Data" section
   • Download sample CSV templates provided
   • Upload your own data or use the provided samples

3. Set Constraints:

   • Configure working days and periods per day
   • Adjust GA parameters (population size, generations, etc.)
   • Set faculty and room constraints

4. Generate Timetable:

   • Click "Start Generation" to run the optimization
   • Monitor real-time progress and fitness evolution
   • View results in the generated timetable

5. View and Export:

   • Switch between Batch, Room, and Faculty views
   • Export as Excel or CSV format

Data Format Requirements

Faculty.csv

• FacultyID: Unique identifier for faculty member
• Name: Faculty member's name
• PreferredDays: Comma-separated preferred working days (0-6, where 0=Monday)
• PreferredSlots: Comma-separated preferred time slots
• MaxLoadPerDay: Maximum classes per day

Courses.csv

• CourseID: Unique course identifier
• Name: Course name
• FacultyID: Assigned faculty member (must exist in Faculty.csv)
• HoursPerWeek: Number of hours per week for the course
• IsLab: Boolean indicating if course requires lab room

Rooms.csv

• RoomID: Unique room identifier
• Capacity: Room capacity (number of students)
• IsLabRoom: Boolean indicating if room is equipped as lab

Batches.csv

• BatchID: Unique batch identifier
• Name: Batch/class name
• CoursesOffered: Comma-separated list of CourseIDs for this batch

Genetic Algorithm Details

Chromosome Representation

Each chromosome represents a complete timetable with genes containing:

• BatchID, CourseID, FacultyID, RoomID, Day, Slot

Fitness Function

The fitness function evaluates solutions based on:

Hard Constraints (Heavy Penalties):

• Faculty conflicts (same faculty, same time)
• Room conflicts (same room, same time)
• Batch conflicts (same batch, same time)
• Lab room violations (lab courses not in lab rooms)

Soft Constraints (Light Penalties):

• Faculty preference violations
• Uneven workload distribution
• Excessive consecutive hours

GA Operators

• Selection: Tournament selection
• Crossover: Single-point crossover
• Mutation: Random day/slot/room changes
• Elitism: Preserve best solutions across generations

Configuration Parameters

Timetable Structure

• Working Days: 3-7 days per week
• Periods per Day: 4-10 time slots
• Max Consecutive Hours: Limit for faculty teaching hours

GA Parameters

• Population Size: 20-200 solutions per generation
• Generations: 10-500 evolution cycles
• Crossover Rate: 0.1-1.0 probability of parent combination
• Mutation Rate: 0.01-0.5 probability of random changes
• Elitism: 1-10 top solutions preserved per generation

File Structure