https://github.com/naveen88112/vehicle-routing-problem

Vehicle Routing Problem (VRP) Solver This project applies a Genetic Algorithm (GA) to optimize the Vehicle Routing Problem (VRP). This is done for more than one vehicle to ensure delivery routes that save travel distance and increase efficiency.
https://github.com/naveen88112/vehicle-routing-problem

deap genetic-algorithm matplotlib numpy python vehicle-routing-problem

Last synced: 2 months ago
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Vehicle Routing Problem (VRP) Solver This project applies a Genetic Algorithm (GA) to optimize the Vehicle Routing Problem (VRP). This is done for more than one vehicle to ensure delivery routes that save travel distance and increase efficiency.

• Host: GitHub
• URL: https://github.com/naveen88112/vehicle-routing-problem
• Owner: Naveen88112
• Created: 2025-01-09T10:00:56.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2025-03-11T09:28:20.000Z (over 1 year ago)
• Last Synced: 2025-10-09T23:39:38.344Z (8 months ago)
• Topics: deap, genetic-algorithm, matplotlib, numpy, python, vehicle-routing-problem
• Language: Jupyter Notebook
• Homepage:
• Size: 94.7 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
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.