Gurobi Tutorial Overview: What You Need to Get Started

Gurobi’s mathematical optimization solver makes it easy for businesses to make the best possible decisions—helping them to not only overcome their biggest challenges, but ensure optimal outcomes. 

Operations researchers, data scientists, and analysts alike leverage Gurobi to navigate the many variables, constraints, and tradeoffs inherent in their companies’ most impactful decisions. 

For example, food company Mondelēz uses Gurobi to maximize their supply chain’s cost-effectiveness. By ensuring that the right snacks are sorted in the fewest number of vehicles and on the lowest cost delivery routes, they ensure customers can buy their products on-demand while reducing overhead. In addition to improving Mondelēz’s supply chain decisions, Gurobi delivers optimal decisions faster than other solvers, allowing the company to quickly respond to changing demands and conditions. 

If your company wants to navigate complex decisions and pivot quickly when things change, Gurobi may be the right fit for your business. The tutorials in this article will help your OR and data teams get up to speed quickly. 

Gurobi Tutorial Guide 

New Gurobi users should start with these guides: 

• Starting with Gurobi: This compilation has all the high-level resources that you will need to get started with Gurobi, such as an installation guide, example tour, reference manual, and a very active discussion forum. The resources linked here will help you understand all of your options for using Gurobi, based on your technology and programming preferences. 

• Gurobi Python Tutorial: This guide will walk you through a simple mixed integer programming model to illustrate how you can program in Gurobi using Python, so you can translate optimization modeling concepts into Python language conventions. 

• Getting Started with Mathematical Optimization in Python: This library allows Python users to get even more in depth with data science-focused webinars and Jupyter Notebook examples based on real life optimization models that users can adapt to their company’s needs. 

• Programming Language & API Tutorials: While most Gurobi customers use Python, Gurobi supports all major programming languages (e.g., Java, MATLAB, R). The guides on this page will help you get started, regardless of which language and API you prefer. 

General Optimization Tutorials 

Users who want to grow their mathematical optimization skills may find these articles useful: 

• Introduction to Optimization Through the Lens of Data Science: More and more data scientists are discovering the power of mathematical optimization. Gurobi’s free Udemy course helps data scientists leverage their skills to ensure optimization success. The course includes the building blocks of optimization, connects optimization with data science concepts to build learners’ understanding, and introduces various modeling archetypes. While this course is most useful for Python users, the examples are all explained in code, math, and English in parallel, so that no students get left out. 

• Math Programming Modeling Basics: This guide helps readers think through the various aspects of optimization modeling, including case studies and sample models from various industries. Readers will learn the basics of how to convert business problems into math problems. 

• Linear Programming Tutorial: Linear programming is a foundational mathematical optimization model that new learners are sure to encounter. This 14-part video series walks users through all aspects of linear programming, including classic example problems (e.g., the Furniture Factory Problem) and basic solution algorithms (e.g., the Simplex Method). 

Ready for More? 

Gurobi is dedicated to making optimization accessible to as many people as possible. That’s why we’re constantly adding new tools to our resource library. Some of our favorites include: 

• The Burrito Optimization Game: This fun, interactive game introduces players to the basics of mathematical optimization. As the owner of a popular burrito chain (Guroble), you’ll face critical decisions about where to place your food trucks to maximize profits—while also considering things like ingredient availabililty, production levels, and pricing. 

• The Gurobipy Card Game: This offline game helps programmers and data scientists build their mathematical knowledge by matching math concepts with their corresponding Python code. 

• Jupyter Notebook: This is a compilation of example models drawn from real Gurobi users. In addition to reviewing beginner, intermediate, and advanced models, users can adapt models for their own needs, which allows them to jump start their modeling process. 

• OptiMods: This is an open-source collection of Python optimization models. Each model is based on a real-life example, contains background information, and includes an example dataset.