Vehicle Routing Problem using Ant Colony Optimization

This project implements an Ant Colony Optimization (ACO) algorithm to solve the Vehicle Routing Problem (VRP), which is a combinatorial optimization problem. The VRP involves finding the optimal routes for a fleet of vehicles to deliver goods to a set of customers, subject to various constraints.

Overview

The project is implemented in juyter notebook by python and consists of the following components:

1. hant_vehivle_routing.ipynb :The main script that performs the ACO algorithm and mutation and other relative method and classes for solving the VRP.
2. dataset/: c106c6.txt solomon dataset

Usage

To run the project, make sure you have Python and jupyter notebook installed on your system. Then, follow these steps:

1. Clone the repository:

git clone https://github.com/your-username/vehicle-routing-aco.git](https://github.com/nimagarshasebi/A-hybrid-ant-colony-algorithm.git)https://github.com/nimagarshasebi/A-hybrid-ant-colony-algorithm.git
Install the required dependencies:
pip install -r requirements.txt

Dataset
The project uses datasets provided in the dataset/ directory. These datasets contain information about customers such as coordinates, demands, and time windows.
Results
After running the main script, the optimized routes for the vehicles are computed using the hybrid-ACO algorithm. Results are displayed and compared with other optimization methods.



Acknowledgements
This project is inspired by hybrid Ant Colony Optimization for Vehicle Routing Problem algorithms.
The dataset used in this project is adapted from the VRP-REP repository.