Use the graph theory to optimise the road transportation network of a retail company

Build graphical representations of a road transportation network to support network optimisation studies.

For a retailer, road transportation between the distribution centre and stores accounts for a significant share of logistics costs.  Companies often conduct route-planning optimisation studies to reduce these costs and improve network efficiency.

It requires collaboration between continuous improvement engineers and the transportation teams that manage operations daily.

60+ case studies with source code, dummy data and mathematical concepts here 👉 Analytics Cheat Sheet

In this Article, we will use Graph Theory to design visual representations of a transportation network to support this collaboration and facilitate solution design.

Click on the image below to access a full tutorial video to understand the concept behind this solution

As a continuous improvement engineer of a retail company, you are in charge of reengineering warehousing and transportation operations. In your scope, you have a major distribution centre located in Shanghai (China) that delivers 54 hypermarkets.

Your objective is to reduce the total cost of transportation.

The objective is to design a new transportation plan to increase the average truck size by delivering more stores per route.

A graph is a structure that consists of nodes (vertices) and edges, where each edge connects two nodes.

With these graphs, you can challenge the current routing and discuss optimisation levers with the transportation teams.

In this repository, you will find all the code used to explain the concepts presented in the article.

• Network Graph.ipynb - Jupyter notebook with step-by-step analysis
• network_graph.py - Standalone Python script
• data/ - Folder containing input data (store province.xlsx, delivery records.xlsx)

pip install -r requirements.txt
python network_graph.py

• numpy
• pandas
• matplotlib
• networkx
• openpyxl

• The full write-up, with the reasoning behind the code: Transportation Network Analysis with Graph Theory
• The video: Transportation Network Analysis with Graph Theory
• Test what you learned: the Supply Science App has a quiz on road transportation and lessons on transportation management, free and in the browser.
• 100+ case studies with their source code: samirsaci.com

Samir Saci, supply chain engineer and data scientist with ten years in operations across Asia and Europe. Founder of LogiGreen, creator of Supply Science. For consulting on analytics and sustainable supply chain transformation: LogiGreen. More about me: samirsaci.com/about · LinkedIn