Logistics Fleet Intelligence System

How It Works

1. 1
   Fetch pending orders

   Query Supabase for all orders with status 'pending' and delivery_date = today, including customer addresses, priority levels, and delivery windows

   Supabase Python client
2. 2
   Geocode addresses

   Convert customer addresses to GPS coordinates using Google Maps Geocoding API, caching results in Redis to avoid duplicate API calls

   Google Maps API
3. 3
   Calculate distance matrix

   Generate travel time matrix between all delivery points using current traffic conditions from Google Maps Distance Matrix API

   Google Maps API
4. 4
   Optimize routes

   Apply Traveling Salesman Problem solver with constraints for vehicle capacity, delivery windows, and driver shift limits using OR-Tools optimization library

   Google OR-Tools
5. 5
   Store optimized routes

   Save route assignments, stop sequences, and estimated delivery times to Supabase routes and route_stops tables, updating order status to 'routed'

   Supabase Python client

Implementation

# Route Optimizer Agent Implementation

## File Structure
```
route_optimizer/
├── main.py              # Entry point and cron handler
├── optimizer.py         # Core optimization logic
├── geocoding.py         # Address geocoding with caching
├── database.py          # Supabase database operations
├── config.py           # Configuration and environment variables
└── requirements.txt    # Python dependencies
```

## Environment Variables
```bash
SUPABASE_URL=your_supabase_url
SUPABASE_KEY=your_supabase_anon_key
GOOGLE_MAPS_API_KEY=your_google_maps_key
REDIS_URL=redis://localhost:6379
MAX_STOPS_PER_ROUTE=25
DEFAULT_VEHICLE_CAPACITY=50
```

## Key Functions

### main.py
```python
def optimize_daily_routes():
    """Main entry point for route optimization"""
    orders = fetch_pending_orders()
    coordinates = geocode_addresses(orders)
    distance_matrix = calculate_distances(coordinates)
    routes = optimize_with_constraints(orders, distance_matrix)
    store_optimized_routes(routes)
    log_optimization_summary(routes)
```

### optimizer.py
```python
def optimize_with_constraints(orders, distance_matrix):
    """Apply OR-Tools VRP solver with business constraints"""
    manager = pywrapcp.RoutingIndexManager(len(orders), num_vehicles, depot)
    routing = pywrapcp.RoutingModel(manager)
    
    # Add capacity constraint
    routing.AddDimensionWithVehicleCapacity(
        capacity_callback, 0, vehicle_capacities, True, 'Capacity')
    
    # Add time window constraints
    routing.AddDimension(time_callback, 30, 480, False, 'Time')
    
    return solve_and_extract_routes(routing, manager)
```

## Cron Setup
```bash
# Add to crontab for daily 4 AM execution
0 4 * * * cd /path/to/route_optimizer && python main.py
```

## Database Schema
```sql
CREATE TABLE routes (
    id UUID PRIMARY KEY,
    vehicle_id TEXT,
    driver_id TEXT,
    created_at TIMESTAMP,
    total_distance FLOAT,
    estimated_duration INTEGER
);

CREATE TABLE route_stops (
    id UUID PRIMARY KEY,
    route_id UUID REFERENCES routes(id),
    order_id UUID,
    sequence INTEGER,
    estimated_arrival TIMESTAMP
);
```

Data Flow

Prerequisites

• -Google OR-Tools library installed
• -Active Google Maps API key with sufficient quota
• -Supabase tables created with proper indexes
• -Redis server running and accessible
• -Vehicle and driver data populated in database

Error Handling