OptiRoute is an AI-driven platform designed to intelligently allocate critical resources across healthcare, disaster relief, food distribution, and housing. Using predictive analytics and optimization algorithms, the system ensures resources reach the right people, at the right time, with minimal waste and maximum impact.
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Healthcare – Hospital Bed & Doctor Allocation
Patients died during COVID-19 due to poor bed allocation, while doctors were overworked in some hospitals and idle in others. Ambulances were misrouted, and inequity in access worsened outcomes. -
Humanitarian Aid – Disaster Relief Resource Allocation
Relief distribution often suffers from uneven supply, half-empty trucks, duplicated aid, and neglected remote communities. -
Food & Supply Chain – Hunger and Waste Crisis
Millions of tons of food go to waste due to misaligned supply chains, hurting farmers and increasing environmental damage. -
Public Housing & Shelter Allocation Crisis
Government houses remain vacant while families live on streets due to mismanagement, poor planning, and inequality.
OptiRoute uses machine learning, optimization algorithms, and real-time updates to transform chaotic manual allocation into intelligent, fair, and efficient ecosystems.
- Predictive AI: Forecasts patient inflow, disaster needs, food demand, and housing vacancies.
- Smart Matching: Dynamically pairs patients, families, food, and supplies with the best-fit resource.
- Optimization at Scale: Efficient use of trucks, staff, beds, and units.
- Equity & Prioritization: Vulnerable populations are flagged and prioritized.
- Dynamic Adaptation: Real-time updates continuously adjust allocation.
- Impact Simulation: Tests multiple allocation scenarios for maximum lives saved and waste reduced.
- Predictive bed & ICU availability
- Smart patient routing
- Dynamic staff scheduling
- Risk & equity alerts
- Demand forecasting for shelters
- Optimal delivery routing
- Duplication avoidance
- Community needs detection
- Demand forecasting for food
- Surplus-to-need smart distribution
- Perishables optimization
- Impact maximization
- Forecasting housing demand
- Dynamic reallocation of units
- Needs-based prioritization
- Occupancy & satisfaction optimization
- Framework: FastAPI
- Microservices:
- Forecasting: XGBoost, Prophet, Scikit-learn
- Optimization: OR-Tools, PuLP, NetworkX
- Explainability: SHAP, LIME
- Background Jobs: Celery + Redis
- Realtime Updates: FastAPI WebSockets
- Data Validation: Pydantic
- Framework: Next.js
- Styling: Tailwind CSS
- UI Components: Shadcn/UI, Radix UI
- Charts & Metrics: Recharts, Chart.js
- State Management: Zustand + React Query / SWR
- Realtime: WebSockets
- Auth: Firebase Authentication
- File Storage: Firebase Storage
- MongoDB – Primary NoSQL database (flexible schemas)
- Firebase Firestore – Live dashboards & session data
- Redis – Caching & pub-sub
- Scikit-learn
- PyTorch
- TensorFlow
- Pandas, NumPy
- Frontend: Vercel
- Backend: Lightning-AI
- Models: Hugging Face
- Frontend: Deployed on Vercel
- Backend: Hosted on Lightning-AI with microservices
- ML Models: Hosted on Hugging Face
- Database: MongoDB Atlas + Firebase Firestore
- Realtime Communication: WebSockets + Redis
- Efficient Allocation: Critical resources reach those who need them most.
- Data-Driven Decisions: ML reduces human error and improves fairness.
- Real-Time Monitoring: Dashboards provide live visibility.
- Social Benefit: Improves community trust and resilience during crises.
- Improved ML models with larger datasets
- Multi-resource allocation (combined optimization across domains)
- Geographic scaling across regions
- Smarter prioritization (age, health, urgency factors)
- Advanced analytics dashboards