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Inspiration
Inefficient delivery routing leads to wasted fuel, higher costs, and delays. We wanted to solve this real-world problem using algorithms and AI to make logistics smarter and greener.
What it does
SmartRoute optimizes delivery routes in real time using graph algorithms, reinforcement learning, and genetic optimization. It adapts instantly to traffic, weather, or demand changes, reducing costs and carbon emissions.
How we built it
We combined classical algorithms (Dijkstra, A*) with machine learning models for dynamic rerouting. A backend API processes delivery points and external data feeds, while a simple web app dashboard displays optimized routes on an interactive map.
Challenges we ran into
- Integrating live traffic and weather data smoothly into the optimization engine.
- Balancing multiple objectives: shortest time, least cost, and fair workload distribution across vehicles.
- Ensuring scalability for both small fleets and large logistics companies.
Accomplishments that we're proud of
- Built a working prototype that generates optimized routes in seconds.
- Combined classical algorithms with AI for a real-world scalable solution.
- Designed a system that supports both business efficiency and sustainability goals.
What we learned
- How reinforcement learning can enhance classical optimization.
- The importance of balancing algorithm efficiency with real-world constraints.
- How impactful algorithm-driven solutions can be for sustainability.
What's next for SmartRoute AI
- Deploy as a full SaaS platform for logistics companies.
- Add predictive demand modeling to anticipate future delivery spikes.
- Extend the solution to humanitarian aid and disaster relief logistics.
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