https://github.com/rui-exe/asae-routes-optimization

Using metaheuristics to optimize ASAE's Vehicle Routing Problem with Time Windows Problem
https://github.com/rui-exe/asae-routes-optimization

ai algorithms artificial-intelligence artificial-intelligence-algorithms genetic-algorithm hill-climbing metaheuristics simulated-annealing vehicle-routing-problem

Last synced: 7 months ago
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Using metaheuristics to optimize ASAE's Vehicle Routing Problem with Time Windows Problem

• Host: GitHub
• URL: https://github.com/rui-exe/asae-routes-optimization
• Owner: rui-exe
• Created: 2023-03-11T18:02:38.000Z (over 2 years ago)
• Default Branch: master
• Last Pushed: 2023-04-04T21:36:30.000Z (over 2 years ago)
• Last Synced: 2025-01-29T20:43:58.349Z (9 months ago)
• Topics: ai, algorithms, artificial-intelligence, artificial-intelligence-algorithms, genetic-algorithm, hill-climbing, metaheuristics, simulated-annealing, vehicle-routing-problem
• Language: Python
• Homepage:
• Size: 4.08 MB
• Stars: 2
• Watchers: 1
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# IA First Assignment

# Optimal Inspection Routes Problem

# Problem : Inspected establishments maximization

## Installing the dependencies

- To run the project you need to have the following modules installed:

- Pandas (https://pandas.pydata.org/)

```bash

pip install pandas

```

- Matplotlib (https://matplotlib.org/)

```bash

pip install matplotlib

```

- Numpy (https://numpy.org/)

```bash

pip install numpy

```

- Tkinter (https://docs.python.org/3/library/tkinter.html)

```bash	

pip install tk

```

- Folium (https://python-visualization.github.io/folium/)

```bash

pip install folium

```

## Running the project

**Keep in mind that everytime you run the program with a metaheuristic, a graph will be generated for that same metaheuristic, plotting the solution utility in function of time.

They will be saved in the "plots" folder, with the name of the metaheuristic, followed by the correct date and time which it was plotted, ex: sa_solution_utility20230404-223857.png**

- To run the project you need to run the following command:

```bash

python3 main.py

```

- After running the command, the following window will appear:

![alt text](images/gui.png)

- On the first input you need to insert the size of the dataset you want to use. The dataset is composed by 1000 establishments, so you can choose a number up to 1000.

- On the second input you need to choose the metaheuristic you want to use. You can choose between:

    - Hill Climbing

    - Simulated Annealing

    - Genetic Algorithm

![alt text](images/options.png)

- After choosing the metaheuristic, you need to click on the "Submit" button. After that a loading screen will appear and the program will run the metaheuristic.

![alt text](images/loading.png)

- In the terminal we can see the inicial solution evaluation, and every time the solution is updated, we can see the new solution evaluation.

- In the end it is also shown the solution itself, with the establishments assigned to each vehicle.

- When the program finishes running the metaheuristic, the following window will appear:

![alt text](images/solution.png)

- There we can see the number of establishments that were inspected, our final solution evaluation.

- We can also see each vehicle route, on the map, with the establishments assigned to it, by clicking on the ok button, after selecting the vehicle we want to see the route.

![alt text](images/vehicle_options.png)

- This is an example of a vehicle route, shown on the map, on your default browser.

![alt text](images/vehicle_map.png)