Optimize E-Commerce Last-Mile Delivery with Python

Organize your routes to deliver parcels with a minimum number of drivers using optimization models with python

Optimize E-Commerce Last-Mile Delivery with Python
Photo by Sam Balye / Unsplash

Organize your routes to deliver parcels with a minimum number of drivers using optimization models with python

If you travel to first and second-tier cities of China, you will find on the street many delivery drivers (Chinese: 快递).

They take the parcels from small warehouses called customer service centres (Chinese:客户服务中心) located in each neighbourhood and deliver them to the final customers.

These centres are key elements of the Logistics Network of the major courier companies in China. They provide a large geographical coverage for last-mile delivery and a huge competitive advantage by offering the best service level and delivery lead time in the market.

Before arriving at your door, your parcel will be picked up from the vendor’s warehouse, transit through several regional distribution centres and will finally arrive at the service centre of your neighbourhood.

When your parcel arrives at the centre, you will receive a notification on your phone to inform you that a courier will deliver your parcel during the day.

This article will present a solution to optimize the last-mile delivery from these centres to reduce the costs and ensure a uniform distribution of the workload to each driver.

I. Scenario


Problem Statement

You are a manager in a local service centre with

  • 4 drivers in your team
  • 15 parcel capacity per vehicle
  • 16 destinations to cover in the neighbourhood named Dj with j in [1, 16]
  • D0 is the centre
  • 1 route per driver
Optimize E-Commerce Last-Mile Delivery with Python
Example with 0: your service centre | 1 … 16: customers’ destinations — (Image by Author)

Distance Matrix

To build your model, you need to provide a distance matrix M as inputs defined by

  • M(i, j) with i, j in [0, 16]
  • M(i, j) = distance between Di and Dj

This distance matrix will be loaded from an Excel file.

You can find an example of this scenario here: Link

Demand: number of parcels to deliver to each location

We will use here a python list with the first value at zero (because you don’t deliver anything in the centre)

  • Demand = [0, 1, 1, 2, 4, 2, 4, 8, 8, 1, 2, 1, 2, 4, 4, 8, 8]

Objective

  • Deliver all parcels with a minimum number of drivers
  • Optimize the routing to minimize the distance covered per route
Results

Route for driver 0
0 Parcels(0) -> 4 Parcels(4) -> 3 Parcels(6) -> 1 Parcels(7) -> 7 Parcels(15) -> 0 Parcels(15)
Distance of the route: 1552 (m)
Parcels Delivered: 15 (parcels)

Route for driver 1
0 Parcels(0) -> 14 Parcels(4) -> 16 Parcels(12) -> 10 Parcels(14) -> 9 Parcels(15) -> 0 Parcels(15)
Distance of the route: 1552 (m)
Parcels Delivered: 15 (parcels)

Route for driver 2
0 Parcels(0) -> 12 Parcels(2) -> 11 Parcels(3) -> 15 Parcels(11) -> 13 Parcels(15) -> 0 Parcels(15)
Distance of the route: 1552 (m)
Parcels Delivered: 15 (parcels)

Route for driver 3
0 Parcels(0) -> 8 Parcels(8) -> 2 Parcels(9) -> 6 Parcels(13) -> 5 Parcels(15) -> 0 Parcels(15)
Distance of the route: 1552 (m)
Parcels Delivered: 15 (parcels)

Total distance of all routes: 6,208 (m)
Parcels Delivered: 60/60

Based on these results, you can assign each of your four drivers a route that has the same total distance

  • 100% of parcels are delivered with a minimum distance covered
  • Drivers’ vehicles are loaded to their maximum capacity (15/15)

Using this model helps you to ensure that your drivers, who are paid a fixed amount by delivery, will be fairly assigned to a route. You will avoid the issues of having drivers complaining because they have longer routes than their colleagues.

Moreover, you are using your resources at their maximum capacity.

🔗
You can find the full code in this Github repository: Link

II. Build your Model

Capacitated vehicle routing problem (CVRP) with Google OR-Tools

OR-Tools is an open-source collection of Google with tools for combinatorial optimization. The objective is to find the best solution out of a very large set of possible solutions.

Let us try to use this library to build the optimal routes.

1. Import Distance Matrix and Init parameters

2. Create functions to calculate distances and order quantities

3. Build your model with constraints

4. Show the solution

III. Conclusion

This model can help the centre manager to

  • Optimize his fleet with full utilization of his drivers and vehicles
  • Ensure that the workload is equally distributed among each driver

Question:

  • What could be the results with higher capacity (boxes) per driver?
  • What could be the results if we have a weight or volume constraint?

I let you test it and share your results (or questions) in the comment area.

References

[1] Google AI, Google OR-Tools Library, Link