Talks - Uzoma Nicholas Muoh: Improving Efficiency in Transportation Networks using Python

When we think about what Python is for, we often think of things like analytics, machine learning, and web apps, but python is a 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 highly dynamic, goods are always moving from point A to point B and money is being gained or lost every minute. Improving efficiency in a transportation network is critical to the survival of a business that provides transportation and distribution services as well as ensuring timely delivery of goods to customers. This talk examines three real-world examples of how Python is used to improve the efficiency of transportation networks, particularly we will explore: * Finding the optimal match between a driver and a load at the lowest possible cost using Google's ortools; * Generating recommendations for macro level optimizations to a transportation network using networkX; and * Helping the decision making process by answering the question "Should I accept this work?" using skfuzzy. Key Takeaways include: * Graph analytics and data science concepts that facilitate getting goods from manufacturers to stores more efficiently and at a lower cost to businesses; and * An appreciation of the complexity of the logistics industry and the role Python plays in making the life of drivers better.