Student Schedule Optimization

This project aims to efficiently assign students to work shifts while ensuring fairness, minimizing uncovered time slots, and balancing workloads. Using linear programming (PuLP), the system optimizes shift allocations based on availability and constraints.

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📌 Project Overview

Scheduling students for work shifts can be a complex problem due to conflicting availability, minimum/maximum work hours, and the need for balanced distribution. This project builds an optimization model that:

• Merges multiple schedule files into a unified dataset.
• Assigns students to time blocks based on availability and constraints.
• Minimizes uncovered shifts to maximize schedule efficiency.
• Generates final schedules in Excel format for easy review.

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📁 Project Structure

Main Components

• merger.py – Merges multiple Excel schedule files into a single dataset.
• main.py – Implements the optimization algorithm to assign shifts.
• schedules.xlsx – Input dataset containing student availability.
• solution_assigned.xlsx – Optimized work schedule output.
• solution_uncovered.xlsx – Unassigned time slots due to constraints.
• LICENSE – Project licensing information.

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⚙️ Installation & Setup

Requirements

Ensure you have Python 3 installed along with the necessary dependencies:

pip install pandas pulp openpyxl

Execution Steps

1️⃣ Merge schedule files (if applicable)
If multiple Excel files contain schedule data, merge them using:

python merger.py

This creates a single schedules.xlsx file from all .xlsx files in the schedules/ directory.

2️⃣ Run the scheduling optimization
Once the data is prepared, execute the optimization algorithm:

python main.py

This generates two output files:

• solution_assigned.xlsx – Optimized schedule with assigned shifts.
• solution_uncovered.xlsx – Blocks that remain unfilled.

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📊 Optimization Constraints

This project enforces several constraints to create an effective schedule:

• Work Hour Limits – Ensures students work within defined minimum (minh) and maximum (maxh) hours.
• Balanced Workload – Controls the overall average work hours per student (avg_low, avg_high).
• Shift Structure – Assigns 2–4 consecutive blocks per student for efficiency.
• Fair Distribution – Limits the number of overlapping workers per time block (maxovl).
• Penalty for Excess Days – Adds a small penalty (day_cost) to discourage excessive working days.

If no feasible solution is found, these parameters can be adjusted in main.py to allow flexibility.

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🛠 Customization

The scheduling constraints can be adjusted in main.py:

Parameter	Description	Default Value
minh	Minimum hours per student	6
maxh	Maximum hours per student	20
avg_low	Lower bound for average hours	8
avg_high	Upper bound for average hours	12
maxovl	Maximum students per block	3
day_cost	Penalty for working on additional days	1

These values ensure fair scheduling while maintaining flexibility.

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📜 License

This project is open-source and follows the terms outlined in the LICENSE file. It is designed for educational and professional use, allowing modifications and adaptations as needed.