Overview

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.

The Solver That Does More

Gurobi delivers blazing speeds and advanced features—backed by brilliant innovators and expert support.

  • Unmatched Performance
  • Continuous Innovation
  • Responsive, Expert Support
  • Unmatched Performance
  • Continuous Innovation
  • Responsive, Expert Support
  • Gurobi Optimizer Delivers Unmatched Performance

    Unmatched Performance

    Gurobi’s powerful MIP algorithm allows you to add complexity to your model to better represent the real world, and still solve your model within the available time.

    • Public benchmarks consistently show that Gurobi finds proven-optimal solutions faster than competing solvers.
    • The performance gap grows as model size and difficulty increase.
    • Gurobi has a history of making continual improvements across a range of problem types.
    • Gurobi is tuned to optimize performance over a wide range of instances.
    • Gurobi is tested thoroughly for numerical stability and correctness using an internal library of over 10,000 models from industry and academia.
     

  • Gurobi Optimizer Delivers Continuous Innovation

    Continuous Innovation

    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 code is fundamentally parallel—not sequential code that was parallelized—so you can make the most of parallelism and run sequentially.
    • We go beyond cutting-edge MIP cutting planes, with new classes of cuts you can find only with Gurobi.
    • Our advanced MIP heuristics identify feasible, good quality solutions, fast—where other solvers fall flat.
    • Our barrier algorithms fully exploit the features of the latest computer architectures.
    • Our APIs are lightweight, modern, and intuitive—to minimize your learning curve while maximizing your productivity.

  • Gurobi Optimizer Delivers Responsive, Expert Support

    Responsive, Expert Support

    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.

    • Tap into our team’s deep expertise—from implementation to tuning and more.
    • We respond to customer inquiries in hours not days, helping to quickly resolve any issues you’re facing.
    • We can help you fit and adapt your mathematical optimization application to your changing requirements.

Peek Under the Hood

Dive deep into sample models, built with our Python API.

  • Economic Planning
  • Factory Planning
  • Food Manufacturing
  • Supply Network Design
  • Economic Planning
  • Factory Planning
  • Food Manufacturing
  • Supply Network Design
  • Economic Planning

    Economic Planning

    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.

     Learn More
  • Factory Planning

    Factory Planning

    Factory Planning I

    Want to learn how to create an optimal production plan that will maximize your profits? In this example, we’ll teach you how to solve this classic production planning problem. More information on this type of model can be found in example # 3 of the fifth edition of Modeling Building in Mathematical Programming by H. P. Williams on pages 255 – 256 and 300 – 302. 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.  

    Factory Planning II

    Are you up for a major production planning challenge? Try this example where you will learn how to create an optimal production plan that will not only maximize profits, but also determine which month in which to perform maintenance operations on your machines. More information on this type of model can be found in example #4 of the fifth edition of Modeling Building in Mathematical Programming by H. P. Williams on pages 256 and 302 – 303. 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.

     Learn More
  • Food Manufacturing

    Food Manufacturing

    Food Manufacturing I

    If you’re hungry for a mathematical optimization challenge, then try this food manufacturing problem. You’ll learn how to create an optimal multi-period production plan for a product that requires a number of ingredients – each of which has different costs, restrictions, and features. More information on this type of model can be found in example #1 of the fifth edition of Modeling Building in Mathematical Programming by H. P. Williams on pages 253 – 254 and 296 – 298. 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.  

    Food Manufacturing II

    In this example, you’ll have to tackle the same problem that you did in “Food Manufacturing I,” but with additional constraints that change the problem type from a linear program (LP) problem to a mixed-integer program (MIP) problem, making it harder to solve. More information on this type of model can be found in example #2 of the fifth edition of Modeling Building in Mathematical Programming by H. P. Williams on pages 255 and 299 – 300. 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.

     Learn More
  • Supply Network Design

    Supply Network Design

    Supply Network Design I

    Try this Jupyter Notebook Modeling Example to learn how to solve a classic supply network design problem that involves finding the minimum cost flow through a network. We’ll show you how – given a set of factories, depots, and customers – you can use mathematical optimization to determine the best way to satisfy customer demand while minimizing shipping costs. This model is example 19 from the fifth edition of Model Building in Mathematical Programming by H. Paul Williams on pages 273-275 and 330-332. This modeling example is at the beginner level, where we assume that you know Python and that you have some knowledge about building mathematical optimization models.  

    Supply Network Design II

    Take your supply chain network design skills to the next level in this example. We’ll show you how – given a set of factories, depots, and customers – you can use mathematical optimization to determine which depots to open or close in order to minimize overall costs. This model is example 20 from the fifth edition of Model Building in Mathematical Programming by H. Paul Williams on pages 275-276 and 332-333 This modeling example is at the beginner level, where we assume that you know Python and that you have some knowledge about building mathematical optimization models.

     Learn More

Frequently Asked Questions

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.

Additional Insight

Guidance for Your Journey

Gurobi: Always Free for Academics

We make it easy for students, faculty, and researchers to work with mathematical optimization.

Trusted Partners, at Your Service

When you face complex optimization challenges, you can trust our Gurobi Alliance partners for expert services.

We’ve Got Your Back

Our global team of helpful, PhD-level experts are here to support you—with responses in hours, not days.

What's
New at Gurobi

News
Gurobi 10.0 Delivers Blazing-Fast Speed, Innovative Data Science Integration, and an Enterprise Development and Deployment Experience
Latest release enables data professionals to easily integrate machine learning models into optimization models to solve new types of problems.
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Event
Webinar: What’s New in Gurobi 10.0
In this webinar, attendees will get a first look at our upcoming product release, Gurobi 10.0. We will summarize the performance improvements and highlight some of the underlying algorithmic advances, such as the network simplex algorithm, enhancements in concurrent LP, and optimization based bound tightening.
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new content
Cost Savings & Business Benefits for Gurobi Customers
2022 Total Economic Impact™ Study Reveals A 518% ROI with Gurobi
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