Vehicle Routing Problem (VRP) Solver

Overview This project implements a Genetic Algorithm (GA) to solve the Vehicle Routing Problem (VRP). The goal is to optimize delivery routes for multiple vehicles, reducing travel distance and enhancing efficiency.

Features

• Problem Representation: Models VRP with multiple delivery locations and vehicles.
• Genetic Algorithm Implementation: Uses DEAP for evolutionary computation.
• Fitness Evaluation: Optimizes routes based on total travel distance.
• Mutation & Crossover: Ensures diversity in route solutions.
• Visualization: Displays optimized routes using Matplotlib.

Technologies Used

• Python
• DEAP (Distributed Evolutionary Algorithms in Python)
• NumPy
• Matplotlib

How to Run

1. Clone the repository:

   "git clone https://github.com/yourusername/vrp-solver.git"

2. Install dependencies:

   "pip install matplotlib deap numpy"

3. Run the Jupyter Notebook or execute the script:

   "python vrp_solver.py"

Results & Insights

• The genetic algorithm optimizes routes for efficient deliveries.
• Crossover and mutation improve route diversity and effectiveness.
• Visualization provides insights into route optimization strategies.