Course Scheduling Solver

Overview

This project implements a Course Scheduling Solver using Constraint Programming (CP) techniques, specifically leveraging Google OR-Tools' CP-SAT solver. The goal is to assign courses to time slots and rooms while satisfying various constraints, such as avoiding schedule conflicts and optimizing resource usage.

Features

• Uses Constraint Programming (CP) to efficiently assign courses.
• Avoids scheduling conflicts between courses with common students or instructors.
• Ensures room capacity constraints are met.
• Supports time slot constraints (e.g., avoiding overlapping courses).
• Implements heuristic search and optimization techniques to improve scheduling efficiency.
• Uses CP-SAT (a state-of-the-art SAT-based constraint solver) to solve the problem effectively.

Technologies Used

• Python
• Google OR-Tools (CP-SAT Solver)
• Constraint Programming (CP)
• CSP (Constraint Satisfaction Problem) methodology
• Streamlit (for front-end interface)

Installation

Ensure you have Python installed (version 3.7+ recommended). Then, install the required dependencies:

pip install ortools streamlit plotly pandas

How to Run

1. Clone this repository:

   git clone https://github.com/kiya030/CS5100_Project

2. Install dependencies:

   pip install -r requirements.txt

3. Run the Streamlit front-end interface:

   streamlit run app.py

4. Upload a CSV file containing course, teacher, and room data.
5. Configure hard and soft constraints through the sidebar.
6. Click the "Run Optimization Model" button to generate the course schedule.

Constraints Considered

Hard Constraints

• No instructor can teach two courses at the same time.
• No student can attend two courses scheduled at the same time.
• Room capacity cannot be exceeded.
• Specific courses require specific time slots or rooms.
• A teacher can only teach a course during their available times.
• No two courses can be scheduled in the same room at the same time.

Soft Constraints

• Minimize gaps between classes (Allows setting a preference for compact schedules).

Example CSV Format

The uploaded CSV file should contain data for courses, teachers, rooms, and time slots in the following format, or you can just try the "data.csv" file already been given in the folder:

type	id	department	group	preferred_times	num_students	available_times	capacity
course	CS101	CS	GroupA	8,9	45
teacher	Dr. X	CS				8,9,10,11
room	Room101						50
time_slot	8
time_slot	9

Example Output

Course  Group Department Teacher  Time    Room  Students
 CS101 GroupA         CS   Dr. X     8   HallB        45
 CS201 GroupB         CS   Dr. X     9 Room101        50
...

Future Improvements

• Add soft constraints (e.g., preferred time slots for instructors).
• Support multi-campus scheduling.
• Optimize for minimum gaps between courses.

Authors

• Yuxin Qin
• Zihan Zhan
• (Kristen) Yang Wang

License

This project is licensed under the MIT License.