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Overview

Gurobi supports the complex strategic planning, supply chain management, and daily operations of chemical and petroleum manufacturers, marketers, and distributors. Optimization enables decision-makers to improve planning, scheduling, and production processes within refineries that impact day-to-day business operations and long-term asset investment strategies.

“The ability to add new facts as they become available is one of the most beneficial aspects of this model. ”

Levi DeLissa
Data Integration & Optimization Engineer, East Daley

“As we have collected more data over time, I have yet to encounter a dataset that couldn’t be turned into a set of constraints. ”

Levi DeLissa
Data Integration & Optimization Engineer, East Daley

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

    Public benchmarks consistently show that Gurobi finds both feasible and proven optimal solutions faster than competing solvers. With our powerful MIP algorithm, 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.
     

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  • 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.

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  • 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.

  • Decentralization Planning
  • Factory Planning
  • Refinery Planning
  • Standard Pooling
  • Decentralization Planning
  • Factory Planning
  • Refinery Planning
  • Standard Pooling
  • Decentralization Planning

    Decentralization Planning

    Ready for a mathematical optimization modeling challenge? Put your skills to the test with this example, where you’ll learn how to model and solve a decentralization planning problem. You’ll have to figure out – given a set of departments of a company, and potential cities where these departments can be located – the “best” location for each department in order to maximize gross margins. This model is example 10 from the fifth edition of Model Building in Mathematical Programming by H. Paul Williams on pages 265 and 317-319. 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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  • 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.

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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.

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  • Standard Pooling

    Standard Pooling

    Companies across numerous industries – including petrochemical refining, wastewater treatment, and mining – use mathematical optimization to solve the pooling problem. In this example, we’ll guide you through the process of building a mixed-integer quadratically-constrained programming (MIQCP) model of a pooling problem using the Gurobi Python API and show you how to generate an optimal solution to the problem with the Gurobi Optimizer. 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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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.

Additional Insight

East Daley: Using MIP to Model Midstream Energy Assets

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Guidance for Your Journey

Gurobi: Always Free for Academics

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

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Trusted Partners, at Your Service

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

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We’ve Got Your Back

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

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