Overview

With Gurobi, mining organizations can identify the optimal way to balance the utilization of precious natural resources with expectations for responsible use and safety. From the management of manufacturing systems, chemical processes, and mechanical systems, to the workforce that supports these operations—Gurobi makes it possible to make the right decisions, every step of the way.

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

    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.

    • The performance gap grows as model size and difficulty increase.
    • Gurobi has a history of making continual improvements across a range of problem types, with a more than 75x speedup on MILP since version 1.1.
    • 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.

Improvement to Time-Horizon Supply Chain Planning Capacity
1 %

Peek Under the Hood

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

  • Yield Management
  • Economic Planning
  • Efficiency Analysis
  • Mining
  • Refinery Planning
  • Yield Management
  • Economic Planning
  • Efficiency Analysis
  • Mining
  • Refinery Planning
  • Yield Management

    Yield Management

    See how mathematical optimization can make your revenues and profits soar in this example, where we’ll show you how an airline can use the AI technology to devise an optimal seat pricing strategy. You’ll learn how to formulate this Yield Management Problem as a three-period stochastic programming problem using the Gurobi Python API and solve it with the Gurobi Optimizer. This model is example 24 from the fifth edition of Model Building in Mathematical Programming by H. Paul Williams on pages 282-284 and 337-340. This modeling example is at the advanced level, where we assume that you know Python and the Gurobi Python API and that you have advanced knowledge of building mathematical optimization models. Typically, the objective function and/or constraints of these examples are complex or require advanced features of the Gurobi Python API.

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  • 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.

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  • Efficiency Analysis

    Efficiency Analysis

    How can mathematical optimization be used to measure the efficiency of an organization? Find out in this example, where you’ll learn how to formulate an Efficiency Analysis model as a linear programming problem using the Gurobi Python API and then generate an optimal solution with the Gurobi Optimizer. This model is example 22 from the fifth edition of Model Building in Mathematical Programming by H. Paul Williams on pages 278-280 and 335-336. This example is at the intermediate level, where we assume that you know Python and the Gurobi Python API and that you have some knowledge of building mathematical optimization models.

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  • Mining

    Mining

    Put your planning skills to the test in this example, where you’ll learn how to model and solve a production planning problem that involves optimizing the operations of a group of mines over a five-year period. More information on this type of model can be found in example # 7 of the fifth edition of Modeling Building in Mathematical Programming by H. P. Williams on pages 261 – 262 and 310 – 312. 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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  • Refinery Planning

    Refinery Planning

    In this example, we’ll demonstrate how you can use mathematical optimization to optimize the output of a refinery. You’ll learn how to generate an optimal production plan that maximizes total profit, while taking into account production capacity and other restrictions. More information on this type of model can be found in example # 6 of the fifth edition of Modeling Building in Mathematical Programming by H. P. Williams on pages 258 and 306 – 310. 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

Frequently Asked Questions

  • What is mathematical optimization?

    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.

  • What’s a real-world example of mathematical optimization?

    80% 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.

  • What makes mathematical optimization “unbiased”?

    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

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