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Pulp and Paper

Optimize Your Decision-Making

Balance productivity and profits with responsible environmental impact—from planting to consumption and recycling.
 

Overview

Gurobi enables pulp and paper manufacturers to make optimal decisions—from planting seeds to harvesting, processing, distribution, and consumption, all the way to post-consumer recycling. With Gurobi, you’ll have the insights you need, so you can balance productivity and profits with responsible environmental impact.

“Gurobi has it all: performance, price, and personnel. Why would you use anything else? ”

Chris Johnson
VP of Research & Development, LeanLogistics

“Gurobi has it all: performance, price, and personnel. Why would you use anything else? ”

Chris Johnson
VP of Research & Development, LeanLogistics

“Gurobi has it all: performance, price, and personnel. Why would you use anything else? ”

Chris Johnson
VP of Research & Development, LeanLogistics

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.

  • 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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  • Efficiency Analysis
  • Factory Planning
  • Economic Planning

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

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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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