Gurobi solvers help tackle some of the most demanding scenarios where food and beverage companies must make decisions about hundreds or thousands of products in order to stay competitive. These organizations rely on optimization to help maximize production and distribution efficiency while anticipating ever-changing consumer demand to reach the right level of product variety at market-sustainable prices.
Gurobi delivers blazing speeds and advanced features—backed by brilliant innovators and expert support.
With our powerful algorithms, you can add complexity to your model to better represent the real world, and still solve your model within the available time.
Our development team includes the brightest minds in decision-intelligence technology--and they're continually raising the bar in terms of solver speed and functionality.
Our PhD-level experts are here when you need them—ready to provide comprehensive guidance and technical support. They bring deep expertise in working with commercial models and are there to assist you throughout the process of implementing and using Gurobi.
Dive deep into sample models, built with our Python API.
In this example, you’ll discover how mathematical optimization can be used to address a macroeconomic planning problem that a country may face. We’ll show you how to model and solve an input-output problem encompassing the interrelationships between the different sectors of a country’s economy. This model is example 9 from the fifth edition of Model Building in Mathematical Programming by H. Paul Williams on pages 263-264 and 316-317. This modeling example is at the intermediate level, where we assume that you know Python and are familiar with the Gurobi Python API. In addition, you should have some knowledge about building mathematical optimization models.
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New customers regularly tell us that migrating was easier than they expected, and that they are happy they made the switch to Gurobi. Find out all of the details about why you should switch and how to migrate.
Gurobi is a special kind of software called a “solver.” But Gurobi doesn’t have a graphical interface the way your familiar consumer apps do. You interface with it through programming languages like Python, AIMMS, and R—so you have to know how to code. And you need to know how to create a mathematical model.
Don’t have those skills in-house? We have a network of trusted service partners who are ready to help.
And at any point along the way, the Gurobi Expert team is here to help with troubleshooting and tuning your mathematical models. We also offer customized training for groups that need help with modeling techniques, model tuning, etc.
Machine learning looks for patterns in historical data and uses those patterns to make predictions about the future. But what happens when your future no longer looks like your past?
With Gurobi, you can make decisions that don’t rely on your past data. You input what you want to achieve, and Gurobi identifies your best set of decisions. And if something changes along the way, no problem! Just adjust your inputs and run it through Gurobi again.
You’ll also need to know how to create a mathematical model. People who know how to code (like data scientists, for example) can pretty easily pick up this skill. Check out our examplecode and basic training to get started.
We don’t currently offer that specific service. But we have trusted partners who do. And the Gurobi Experts team can help customers troubleshoot and tune their models anytime, at no cost. We also offer customized training for groups that need help with modeling techniques, model tuning, etc.
Other decision models—like decision rules or heuristics—can result in sub-optimal decisions because they explore only a tiny percentage of possible solutions. Gurobi, by contrast, can provide provable optimality. And for a business, the difference between “sub-optimal” and “optimal” decisions can be millions in revenue.
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