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Oil and Gas

Prepare for Optimal Impact


Strike the right balance between maximizing production and minimizing the resulting environmental impact.

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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.

“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 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 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

The Solver That Does More

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

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

Peek Under the Hood

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

  • 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
  • Refinery Planning
  • Standard Pooling

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?

  • What makes mathematical optimization “unbiased”?

Additional Insight

East Daley: Using MIP to Model Midstream Energy Assets

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Guidance for Your Journey

30 Day Free Trial for Commercial Users

Start solving your most complex challenges, with the world's fastest, most feature-rich solver.
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Always Free for Academics

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