Reduce delivery times and fuel costs by optimizing routes using Python!

The Challenge of Inefficient Delivery Routes

For logistics companies, e-commerce stores, and delivery services, route optimization is a major factor in reducing costs and improving customer satisfaction. Poorly planned routes lead to:

• Higher fuel expenses
• Increased delivery times
• Overworked drivers
• Dissatisfied customers

Manually planning routes using maps and spreadsheets is inefficient and does not scale as order volumes grow. A Python-based automation system can solve this by calculating the most efficient routes in real time, adjusting dynamically based on traffic, delivery windows, and vehicle capacities.

How Python Automates Route Optimization

Python, combined with powerful libraries like Google OR-Tools, NetworkX, and Geopy, enables businesses to automate route planning. These tools help businesses:

• Optimize multi-stop delivery routes
• Factor in real-time traffic data
• Assign deliveries based on vehicle availability and capacity
• Recalculate routes dynamically when new orders come in

Step 1: Install Required Libraries

pip install ortools geopy pandas

Step 2: Define Locations and Distances

Using Geopy, we can convert addresses into latitude and longitude and then calculate distances between points.

from geopy.geocoders import Nominatim
from geopy.distance import geodesic

geolocator = Nominatim(user_agent="route_optimizer")

# Example addresses
locations = {
    "Warehouse": "1600 Amphitheatre Parkway, Mountain View, CA",
    "Customer 1": "1 Infinite Loop, Cupertino, CA",
    "Customer 2": "345 Spear St, San Francisco, CA"
}

# Convert addresses to coordinates
coordinates = {name: geolocator.geocode(address) for name, address in locations.items()}

# Calculate distance between warehouse and Customer 1
distance = geodesic(
    (coordinates["Warehouse"].latitude, coordinates["Warehouse"].longitude),
    (coordinates["Customer 1"].latitude, coordinates["Customer 1"].longitude)
).km

print(f"Distance from warehouse to Customer 1: {distance:.2f} km")

Step 3: Optimize Route Using OR-Tools

Google OR-Tools can find the most efficient route for multiple deliveries.

from ortools.constraint_solver import routing_enums_pb2
from ortools.constraint_solver import pywrapcp

# Distance matrix example
distance_matrix = [
    [0, 10, 20],  # Warehouse to others
    [10, 0, 15],  # Customer 1 to others
    [20, 15, 0],  # Customer 2 to others
]

def create_data_model():
    data = {}
    data["distance_matrix"] = distance_matrix
    data["num_vehicles"] = 1
    data["depot"] = 0  # Warehouse as starting point
    return data

def optimize_route():
    data = create_data_model()
    manager = pywrapcp.RoutingIndexManager(len(data["distance_matrix"]), data["num_vehicles"], data["depot"])
    routing = pywrapcp.RoutingModel(manager)
    
    def distance_callback(from_index, to_index):
        return data["distance_matrix"][manager.IndexToNode(from_index)][manager.IndexToNode(to_index)]
    
    transit_callback_index = routing.RegisterTransitCallback(distance_callback)
    routing.SetArcCostEvaluatorOfAllVehicles(transit_callback_index)

    search_parameters = pywrapcp.DefaultRoutingSearchParameters()
    search_parameters.first_solution_strategy = routing_enums_pb2.FirstSolutionStrategy.PATH_CHEAPEST_ARC

    solution = routing.SolveWithParameters(search_parameters)

    if solution:
        print("Optimized Route:")
        index = routing.Start(0)
        while not routing.IsEnd(index):
            print(manager.IndexToNode(index), end=" -> ")
            index = solution.Value(routing.NextVar(index))
        print(manager.IndexToNode(index))

optimize_route()

Step 4: Real-Time Traffic Adjustments

By integrating APIs like Google Maps or OpenRouteService, real-time traffic data can be incorporated into routing calculations. This allows companies to avoid traffic jams and reroute deliveries dynamically.

import requests

API_KEY = "YOUR_GOOGLE_MAPS_API_KEY"
origin = "1600 Amphitheatre Parkway, Mountain View, CA"
destination = "1 Infinite Loop, Cupertino, CA"

url = f"https://maps.googleapis.com/maps/api/distancematrix/json?origins={origin}&destinations={destination}&key={API_KEY}"
response = requests.get(url)
data = response.json()

print(f"Real-time travel time: {data['rows'][0]['elements'][0]['duration']['text']}")

Business Impact of Automated Route Optimization

Using Python-based automation, businesses can:

✅ Reduce fuel costs by 15-30%
✅ Improve delivery speed by optimizing routes dynamically
✅ Enhance customer satisfaction with accurate ETAs
✅ Scale efficiently without increasing operational workload

Let’s Optimize Your Logistics!

Want to automate your delivery routes and cut down costs? My consulting services specialize in building Python-based logistics solutions tailored to your business needs.

Book a consultation today, and let’s optimize your operations!