🧠 OptiShift: Workforce Shift Optimization Tool

OptiShift is a Python-based scheduling solution that leverages linear programming to generate optimized shift assignments. It uses a point-based logic system to assign the most suitable employees to shifts, balancing fairness, availability, and operational requirements.

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🎯 Project Objective

Develop an automated shift scheduling tool that:

• Assigns employees to roles based on their availability.
• Optimizes coverage using constraint-based logic.
• Ensures balanced workload distribution across all team members.

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🛠️ Technologies Used

• Python
• Pandas, NumPy
• PuLP (Linear Programming)
• Matplotlib (for visualization)

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🧮 How It Works

🔄 Workflow Overview

1. Data Input

   • Employees provide availability for each day of the week.
   • Each shift or time slot has required headcount and skill criteria.
   • A CSV matrix defines the availability of each employee.

2. Point-Based Assignment Logic

   • Each employee-shift combination is assigned a score based on:

     • Availability (binary: available/unavailable).
     • Cumulative hours already assigned (penalizes overloading).
     • Fairness constraint (promotes balanced distribution).
     • Optional preferences or past assignments (to avoid bias or repetition).

   • The optimizer maximizes the total points across the schedule while satisfying:

     • Minimum shift coverage.
     • Maximum hours per employee.
     • One shift per employee per day.

3. Linear Programming (LP) Optimization

   • The problem is modeled using the PuLP library.
   • LP constraints ensure feasibility (e.g., availability, max shifts).
   • Objective: maximize total points across all assignments.

4. Output

   • Optimized shift schedule in table format.
   • Summary visualizations (e.g., per-employee assignments, shift coverage).

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📁 Repository Structure

├── OptiShift.ipynb           # Main notebook with end-to-end implementation
├── data/                     # Input files (employee availability, requirements)
├── output/                   # Resulting schedules and plots
└── README.md                 # Project documentation

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📈 Results

• Efficient coverage of all required shifts.
• Balanced workload distribution.
• High flexibility for adapting to different teams or work cycles.

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🚀 Future Features

• Web-based GUI (Streamlit) for easy interaction.
• Employee preferences with scoring weights.
• Multi-role and multi-location support.