https://github.com/richardgschmidt/genetic-dash
Real-time delivery route optimization dashboard using a genetic algorithm and Dash. Deployed live on a Raspberry Pi.
https://github.com/richardgschmidt/genetic-dash
containerization dash dashboards genetic-algorithm logistics python raspberry-pi visualization
Last synced: about 1 month ago
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Real-time delivery route optimization dashboard using a genetic algorithm and Dash. Deployed live on a Raspberry Pi.
- Host: GitHub
- URL: https://github.com/richardgschmidt/genetic-dash
- Owner: RichardGSchmidt
- Created: 2025-06-10T08:42:51.000Z (about 1 year ago)
- Default Branch: master
- Last Pushed: 2025-07-06T02:04:05.000Z (12 months ago)
- Last Synced: 2025-07-06T03:20:57.149Z (12 months ago)
- Topics: containerization, dash, dashboards, genetic-algorithm, logistics, python, raspberry-pi, visualization
- Language: Python
- Homepage:
- Size: 1.42 MB
- Stars: 1
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Genetic Dash – Delivery Route Optimization using a Genetic Algorithm
[](https://genetic-dash.com)
[](LICENSE)
[]()
[]()
[](https://github.com/RichardGSchmidt/genetic-dash/actions)
---
## Overview
Genetic Dash is a web-based interactive dashboard that visualizes the evolution of optimized delivery routes using a genetic algorithm.
- Real-time package delivery routing simulation
- Genetic algorithm with crossover, mutation, and elite preservation
- Fully interactive map and control panel built with Dash (Plotly)
- Hosted at [genetic-dash.com](https://genetic-dash.com)
---
## Features
- Dynamic routing with time, deadlines, and delivery constraints
- Live visualization of each generation’s performance
- Custom package editor and random scenario generation
- Truck-level stats, mileage, and deadline analytics
- GA tuning (population size, mutation rate, etc.)
---
## Tech Stack
| Component | Technology |
|------------------|------------------|
| Frontend | Dash (Plotly), Bootstrap components |
| Backend | Python 3.10, Flask |
| Optimization | Custom Genetic Algorithm |
| Hosting | **Raspberry Pi 4** + Docker + Cloudflared Tunnel |
| Deployment | Live @ [genetic-dash.com](https://genetic-dash.com) |
> Running on a Raspberry Pi demonstrates efficient resource use and practical deployment skills on low-power hardware.
---
## Getting Started
```bash
git clone https://github.com/YourUser/genetic-dash.git
cd genetic-dash
pip install -r requirements.txt
python genetic-dash.py
```
Or via Docker:
```bash
docker build -t genetic-dash .
docker run -p 8050:8050 genetic-dash
```
---
## Genetic Algorithm Highlights
- Genome encodes truck-package assignments
- Fitness function balances deadline adherence and mileage
- Cascade mutations intelligently shuffle truck loads
- Uses elitism and diversity-preserving strategies
---
## Screenshot
Running at enterprise scale on a raspberry pi.
---
## Purpose
This project demonstrates:
- Real-world AI in logistics
- Proficiency in Dash, Python, and optimization algorithms
- Full-stack skills including deployment and live hosting
- Raspberry Pi-based hosting as a demonstration of lean deployment
- Effective visualization of complex backend processes
---
## About the Author
Created by **Richard Schmidt**, U.S. Veteran and WGU Computer Science graduate, pursuing an M.S. in AI/ML.
Focused on practical AI for logistics, simulation, and optimization.
[LinkedIn](https://www.linkedin.com/in/richard-schmidt-328860138/) | [GitHub](https://github.com/RichardGSchmidt)
---
## License
This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.
---
##️ Usage Guide
Once the app is running (either locally or at [genetic-dash.com](https://genetic-dash.com)):
- **Generate or Edit Packages**:
Use the controls in the top-right to add/edit delivery packages, or randomly generate them with control over time windows and deadlines.
- **Adjust Algorithm Parameters**:
Use sliders and input fields to change:
- Population size
- Number of generations
- Mutation rate
- Truck count and capacity
- **Run the Algorithm**:
Click “Run” to visualize delivery routes optimized across generations.
- **Interpret the Map**:
Each truck’s route is shown with waypoints and delivery locations. Hover for details.
- **Analyze Results**:
Below the map:
- Tables display truck mileage, deliveries, and deadlines met/missed.
- Generation-by-generation cost comparisons show GA progress.
- **Reset or Modify**:
Use the “Clear” or “Regenerate” buttons to start over or load a new scenario.