Electric Vehicle Routing Problem (EVRP) Optimization

📋 Overview

This project implements intelligent algorithms for optimizing delivery routes in electric vehicle (EV) fleet systems. The research focuses on efficiently managing heterogeneous EV fleets for logistics and supply-chain management, considering real-world constraints such as battery limitations, charging station availability, and varying load capacities.

🎯 Key Features

• Heterogeneous EV Fleet Management: Supports multiple vehicle categories (Small, Medium, Large, XLarge) with different battery capacities and load limits
• Intelligent Route Optimization: Minimizes the longest travel time across all vehicles in the fleet
• Charging Station Integration: Incorporates strategic charging stops to extend vehicle range
• Multiple Algorithm Implementations:
  • Greedy heuristic for fast initial solutions
  • Simulated Annealing (SA) for near-optimal results
  • Parallel processing capabilities for large-scale problems

🔬 Research Publication

This work has been published in IEEE and presented at an international conference:

"Intelligent Algorithm for Optimizing Delivery Time in Electric Vehicle Fleet Systems" IEEE Conference Publication - View Paper

🏗️ Project Structure

ComsNets-EV-Routing/
├── 📁 Docs/                          # Algorithm documentation
│   ├── Greedy.md                     # Greedy algorithm details
│   ├── SA.md                         # Simulated Annealing algorithm
│   ├── InputAssumptions.md           # Problem constraints & parameters
│   └── InputGeneration.md            # Test case generation guide
├── 📁 Notebooks/                     # Jupyter notebooks for experiments
│   ├── Greedy.ipynb                  # Greedy algorithm implementation
│   ├── SA.ipynb                      # Simulated Annealing implementation
│   ├── Input.ipynb                   # Input generation and validation
│   ├── ResultAnalysis.ipynb          # Performance analysis
│   ├── TestCaseVisualizer.ipynb      # Route visualization
│   ├── GreedyParallelProcessing.py   # Parallel greedy implementation
│   └── SAParallelProcessing.py       # Parallel SA implementation
├── 📁 results/                       # Experimental results
│   ├── *.json                        # Detailed solution data
│   └── *.csv                         # Summary statistics
├── 📁 test_cases/                    # Benchmark test instances
│   ├── customers_10/                 # 10-customer scenarios
│   ├── customers_20/                 # 20-customer scenarios
│   ├── customers_30/                 # 30-customer scenarios
│   ├── customers_40/                 # 40-customer scenarios
│   └── customers_50/                 # 50-customer scenarios
├── requirements.txt                   # Python dependencies
└── readme.md                         # This file

🚗 Problem Formulation

Electric Vehicle Categories

Category	Battery (kWh)	Base Weight (kg)	Load Capacity (kg)	Range (km)*
Small	35	1,500	500	112-140
Medium	40	1,800	600	118-148
Large	45	2,000	700	126-158
XLarge	50	2,200	800	135-175

*Range varies based on load and battery charge level (20%-80% operational range)

Key Constraints

• Battery Management: Vehicles operate between 20%-80% battery capacity for optimal battery health
• Charging Strategy: Strategic charging station visits to extend operational range
• Load Balancing: Efficient distribution of customer demands across heterogeneous fleet
• Time Optimization: Minimize the maximum delivery time across all vehicles

🛠️ Setup Instructions

Prerequisites

• Python 3.8 or higher
• pip package manager

Installation

1. Clone the repository:

   git clone <repository-url>
   cd ComsNets-EV-Routing

2. Create virtual environment (recommended):

   python -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install dependencies:

   pip install -r requirements.txt

Dependencies

• numpy - Numerical computations
• pandas - Data manipulation and analysis
• matplotlib & seaborn - Visualization
• scipy - Scientific computing
• shapely - Geometric operations
• scikit-learn - Machine learning utilities
• tqdm - Progress bars
• ipykernel & ipywidgets - Jupyter notebook support

🚀 Usage

Quick Start

1. Generate Test Cases:

   jupyter notebook Notebooks/Input.ipynb

2. Run Greedy Algorithm:

   jupyter notebook Notebooks/Greedy.ipynb

3. Run Simulated Annealing:

   jupyter notebook Notebooks/SA.ipynb

4. Analyze Results:

   jupyter notebook Notebooks/ResultAnalysis.ipynb

Parallel Processing

For large-scale experiments, use the parallel processing scripts:

# Parallel Greedy Algorithm
python Notebooks/GreedyParallelProcessing.py

# Parallel Simulated Annealing
python Notebooks/SAParallelProcessing.py

Visualization

Visualize routes and analyze performance:

jupyter notebook Notebooks/TestCaseVisualizer.ipynb

📊 Algorithms

1. Greedy Heuristic

A fast construction algorithm that builds feasible routes by:

• Selecting nearest unserved customers
• Managing battery constraints with strategic charging
• Balancing load distribution across vehicle types

Time Complexity: O(n²) where n is the number of customers

2. Simulated Annealing (SA)

An advanced metaheuristic that improves upon greedy solutions through:

• Load-based neighbor generation
• Temperature-controlled acceptance criteria
• Iterative route optimization

Performance: Achieves near-optimal solutions with <25% deviation from optimal

3. Parallel Processing

Both algorithms support parallel execution for:

• Multiple test case evaluation
• Statistical analysis across different scenarios
• Scalability testing

📈 Experimental Results

The algorithms have been tested on various scenarios:

• Test Cases: 10-50 customers with varying demand distributions
• Performance Metrics:
  • Total delivery time
  • Vehicle utilization
  • Charging station usage
  • Solution quality vs. computation time

Results demonstrate that the SA algorithm consistently outperforms the greedy approach while maintaining reasonable computation times for practical applications.

🤝 Contributing

1. Fork the repository
2. Create a feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

📚 Citation

If you use this work in your research, please cite our IEEE paper:

@inproceedings{author2025intelligent,
  title={Intelligent Algorithm for Optimizing Delivery Time in Electric Vehicle Fleet Systems},
  author={[Author Names]},
  booktitle={IEEE Conference},
  year={2025},
  organization={IEEE},
  url={https://ieeexplore.ieee.org/document/10885593}
}

📞 Contact

For questions or collaboration opportunities, please reach out through:

• GitHub Issues
• Email: chanakyavasantha@gmail.com

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Keywords: Electric Vehicle Routing, Fleet Optimization, Simulated Annealing, Logistics, Supply Chain Management, Sustainable Transportation

This README includes:

1. Professional badges linking to your IEEE paper
2. Comprehensive overview of the project and its features
3. Clear project structure with emojis for better readability
4. Detailed problem formulation with EV specifications
5. Complete setup instructions for easy reproduction
6. Usage examples for all major components
7. Algorithm descriptions with performance metrics
8. Citation format for academic use
9. Professional formatting with proper sections and styling

You can copy and paste this directly into your readme.md file!