Routing optimization is everywhere — from delivery fleets to ride-sharing apps, from warehouse logistics to postal systems. Yet, behind the scenes, these problems are complex, dynamic, and computationally challenging.
In this course, you’ll learn how to model and solve Vehicle Routing Problems (VRP) step by step, using Python as the main implementation language. We’ll start with the fundamentals of routing and then move through several key VRP variants, including Capacitated VRP, VRP with Time Windows, Multi-Depot VRP, Heterogeneous Fleet VRP, and Pickup and Delivery Problems.
You’ll see how small changes in constraints can transform a simple route into a combinatorial puzzle — and you’ll learn practical ways to handle these challenges through exact algorithms, heuristics, and metaheuristics.
Each section is designed for hands-on learning. Instead of long theoretical lectures, you’ll walk through pre-tested Python scripts that demonstrate how to build, run, and interpret routing models effectively. Along the way, you’ll also get brief insights into how the same problems can be tackled in Julia and Java, so you can compare approaches across languages.
This course is ideal for students and professionals in industrial engineering, operations research, computer science, logistics, and supply chain management — or anyone curious about optimization and decision-making in real-world systems.
By the end of the course, you’ll be able to model your own routing problems, choose appropriate solvers and techniques, and confidently implement end-to-end VRP solutions in Python.
- Students and professionals in Industrial Engineering, Operations Research, Logistics, Supply Chain, or Computer Science.
- Anyone interested in optimization, route planning, or transportation analytics.
- Developers and data scientists who want to apply algorithmic thinking to real-world logistics problems.
