Gurobi provides companies with the ability to reduce operational costs, speed up delivery of goods and services, improve quality, and maximize profit to ensure competitiveness in local, national, and global economies. Optimization gives organizations the ability to plan, schedule, and execute amid a challenging collection of variables that must be factored into the supply chain, including operations management, production, fulfillment, and distribution.

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.

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Time Savings

Peek Under the Hood

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

  • Workforce Scheduling
  • Customer Assignment
  • Marketing Campaign Optimization
  • Workforce Scheduling
  • Customer Assignment
  • Marketing Campaign Optimization
  • Workforce Scheduling

    Workforce Scheduling

    In this example, you’ll learn how to solve a critical, central problem in the services industry: workforce scheduling. We’ll demonstrate how you can use mathematical optimization to generate an optimal workforce schedule that meets your business requirements, maximizes employee fairness and satisfaction, and minimizes the number of temporary workers your company needs to hire. 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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  • 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.

     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?

    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.

  • 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.

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