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Genetic Dash – Delivery Route Optimization using a Genetic Algorithm\n\n[![Live Demo](https://img.shields.io/badge/Live-Demo-brightgreen)](https://genetic-dash.com)\n[![License: MIT](https://img.shields.io/badge/license-MIT-blue.svg)](LICENSE)\n[![Built with Python](https://img.shields.io/badge/Python-3.10+-blue.svg)]()\n[![Dash Framework](https://img.shields.io/badge/Framework-Plotly%20Dash-lightgrey)]()\n[![CI](https://github.com/RichardGSchmidt/genetic-dash/actions/workflows/ci.yml/badge.svg)](https://github.com/RichardGSchmidt/genetic-dash/actions)\n\n\n![demo.png](screenshots/demo.png)\n\n\n---\n\n## Overview\n\nGenetic Dash is a web-based interactive dashboard that visualizes the evolution of optimized delivery routes using a genetic algorithm.\n\n- Real-time package delivery routing simulation\n- Genetic algorithm with crossover, mutation, and elite preservation\n- Fully interactive map and control panel built with Dash (Plotly)\n- Hosted at [genetic-dash.com](https://genetic-dash.com)\n\n---\n\n## Features\n\n- Dynamic routing with time, deadlines, and delivery constraints\n- Live visualization of each generation’s performance\n- Custom package editor and random scenario generation\n- Truck-level stats, mileage, and deadline analytics\n- GA tuning (population size, mutation rate, etc.)\n\n---\n\n## Tech Stack\n\n| Component        | Technology       |\n|------------------|------------------|\n| Frontend         | Dash (Plotly), Bootstrap components |\n| Backend          | Python 3.10, Flask |\n| Optimization     | Custom Genetic Algorithm |\n| Hosting          | **Raspberry Pi 4** + Docker + Cloudflared Tunnel |\n| Deployment       | Live @ [genetic-dash.com](https://genetic-dash.com) |\n\n\u003e Running on a Raspberry Pi demonstrates efficient resource use and practical deployment skills on low-power hardware.\n\n---\n\n## Getting Started\n\n```bash\ngit clone https://github.com/YourUser/genetic-dash.git\ncd genetic-dash\npip install -r requirements.txt\npython genetic-dash.py\n```\n\nOr via Docker:\n\n```bash\ndocker build -t genetic-dash .\ndocker run -p 8050:8050 genetic-dash\n```\n\n---\n\n## Genetic Algorithm Highlights\n\n- Genome encodes truck-package assignments\n- Fitness function balances deadline adherence and mileage\n- Cascade mutations intelligently shuffle truck loads\n- Uses elitism and diversity-preserving strategies\n\n---\n\n## Screenshot\n\n![scaled-up.png](screenshots/scaled-up.png)\nRunning at enterprise scale on a raspberry pi.\n---\n\n## Purpose\n\nThis project demonstrates:\n- Real-world AI in logistics\n- Proficiency in Dash, Python, and optimization algorithms\n- Full-stack skills including deployment and live hosting\n- Raspberry Pi-based hosting as a demonstration of lean deployment\n- Effective visualization of complex backend processes\n\n---\n\n## About the Author\n\nCreated by **Richard Schmidt**, U.S. Veteran and WGU Computer Science graduate, pursuing an M.S. in AI/ML.\n\nFocused on practical AI for logistics, simulation, and optimization.\n\n[LinkedIn](https://www.linkedin.com/in/richard-schmidt-328860138/) | [GitHub](https://github.com/RichardGSchmidt)\n\n---\n\n## License\n\nThis project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.\n\n---\n\n##️ Usage Guide\n\nOnce the app is running (either locally or at [genetic-dash.com](https://genetic-dash.com)):\n\n- **Generate or Edit Packages**:  \n  Use the controls in the top-right to add/edit delivery packages, or randomly generate them with control over time windows and deadlines.\n\n- **Adjust Algorithm Parameters**:  \n  Use sliders and input fields to change:\n  - Population size\n  - Number of generations\n  - Mutation rate\n  - Truck count and capacity\n\n- **Run the Algorithm**:  \n  Click “Run” to visualize delivery routes optimized across generations.\n\n- **Interpret the Map**:  \n  Each truck’s route is shown with waypoints and delivery locations. Hover for details.\n\n- **Analyze Results**:  \n  Below the map:\n  - Tables display truck mileage, deliveries, and deadlines met/missed.\n  - Generation-by-generation cost comparisons show GA progress.\n\n- **Reset or Modify**:  \n  Use the “Clear” or “Regenerate” buttons to start over or load a new scenario.","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Frichardgschmidt%2Fgenetic-dash","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Frichardgschmidt%2Fgenetic-dash","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Frichardgschmidt%2Fgenetic-dash/lists"}