Overview

Gurobi helps companies identify how to deliver the right products to the right customers at the right time in order to create the best possible purchasing experience. From planning all the way through agile execution in the marketplace, Gurobi enabled optimal decision-making, including: deciding which locations have the greatest potential to support new stores, optimizing store layout to maximize space, analyzing which products to carry and how to merchandize them, inventory control, and determining which marketing elements will have the biggest impact on customer behavior.

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.

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Peek Under the Hood

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

  • Supply Network Design
  • Market Sharing
  • Customer Assignment
  • Marketing Campaign Optimization
  • Supply Network Design
  • Market Sharing
  • Customer Assignment
  • Marketing Campaign Optimization
  • 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.

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  • Market Sharing

    Market Sharing

    In this example, we’ll show you how to solve a goal programming problem that involves allocating the retailers to two divisions of a company in order to optimize the trade-offs of several market sharing goals. You’ll learn how to create a mixed integer linear programming model of the problem using the Gurobi Python API and how to find an optimal solution to the problem using the Gurobi Optimizer. This model is example 13 from the fifth edition of Model Building in Mathematical Programming by H. Paul Williams on pages 267-268 and 322-324. 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. You may also want to check out the documentation of the Gurobi Python API.

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  • Customer Assignment

    Customer Assignment

    Sharpen your mathematical optimization modeling skills with this example, in which you will learn how to select the location of facilities based on their proximity to customers. We’ll demonstrate how you can construct a mixed-integer programming (MIP) model of this facility location problem, implement this model in the Gurobi Python API, and generate an optimal solution using the Gurobi Optimizer. 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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  • Marketing Campaign Optimization

    Marketing Campaign Optimization

    Companies across almost every industry are looking to optimize their marketing campaigns. In this Jupyter Notebook, we’ll explore a marketing campaign optimization problem that is common in the banking and financial services industry, which involves determining which products to offer to individual customers in order to maximize total expected profit while satisfying various business constraints. You’ll learn how to formulate a mathematical optimization model of the problem (using machine learning predictive response models as parameters) and solve it using the Gurobi Optimizer. 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. The reader should also consult the documentation of the Gurobi Python API.

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Frequently Asked Questions

Mathematical optimization uses the power of math to find the best possible solution to a complex, real-life problem. You input the details of your problem—the goals you want to achieve, the limitations you’re facing, and the variables you control—and the mathematical optimization solver will calculate your optimal set of decisions.

85% of the world’s leading companies use mathematical optimization to make optimal business decisions. For example, Air France uses it to build the most efficient schedule for its entire fleet, in order to save on fuel and operational costs, while reducing delay propagation.

Descriptive and predictive analytics show you what has happened in the past, why it happened, and what’s likely to happen next. But to decide what to do with that information, you need human input—which can introduce bias.

With mathematical optimization, you receive a decision recommendation based on your goals, constraints, and variables alone. You can, of course, involve human input when it comes to whether or not to act on that recommendation. Or you can bypass human input altogether and automate your decision-making.

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.
 Learn More
new content
Cost Savings & Business Benefits for Gurobi Customers
2022 Total Economic Impact™ Study Reveals A 518% ROI with Gurobi
 Learn More