Into the Logisticverse: Improving Efficiency in Transportation Networks using Python

In this event, Uzoma Nicholas (Nick) Muoh re-presented the talk he gave at PyCon US 2023, offering the OpenTeams community the opportunity to engage in more extensive Q&A on the work he’s doing with Python at Trimble Transportation.

When we think about what Python is for, we often think of things like analytics, machine learning, and web apps—but Python is a general workhorse that plays a tremendous and often invisible role in our day-to-day lives, from medicine to finance, and even the transportation of goods from manufacturers to the shelves of our neighborhood stores.

Transportation networks are incredibly complex and highly dynamic. Goods are always moving from point A to point B, and every minute money is being gained or lost depending on how optimally those networks operate. Improving efficiency in a transportation network is critical to the survival of businesses in transportation that are concerned with timely delivery of goods to customers.

This talk examines three real-world examples of how Trimble Transportation uses Python to improve the efficiency of transportation networks. In particular, we explore:

• Finding the optimal match between a driver and a load at the lowest possible cost using Google’s or-tools.
• Generating recommendations for macro-level optimizations to a transportation network using networkX.
• Helping the decision-making process by answering the question “Should I accept this work?” using skfuzzy.

In particular, we explore graph analytics and other data science concepts that facilitate getting goods from manufacturers to stores more efficiently and at a lower cost to businesses. More broadly, we also highlight the complexity of the logistics industry and the role Python can play in making the lives of drivers better.

Nick @ GitHub: https://github.com/OdinTech3
Nick @ LinkedIn: https://www.linkedin.com/in/uzoma-nicholas-muoh-58284960/

Code examples: https://github.com/OdinTech3/pyconus2023-talk

Fuzzy Inference Process: https://www.mathworks.com/help/fuzzy/fuzzy-inference-process.html
A Very Brief Introduction to Fuzzy Logic and Fuzzy Systems: https://towardsdatascience.com/a-very-brief-introduction-to-fuzzy-logic-and-fuzzy-systems-d68d14b3a3b8

Overview of Assignment Problem: https://developers.google.com/optimization/assignment
Assignment as a Min Cost Flow Problem: https://developers.google.com/optimization/flow/assignment_min_cost_flow

Eric Ma: Network Analysis Made Simple: https://www.youtube.com/watch?v=5vU5nRjaac4&t=1999s
Graph Theory: Measures and Indices: https://transportgeography.org/contents/methods/graph-theory-measures-indices/

August 10, 2023