Cookie Settings

We use cookies on our website to give you the most relevant experience by remembering your preferences and repeat visits. By clicking “Accept All”, you consent to the use of ALL the cookies. However, you may visit "Cookie Settings" to provide a controlled consent.

Free Trial
Supply Chain

Decisiveness During Uncertainty


Optimize your supply chain planning, decision making, and operations, to keep supply and demand in balance.

Read the Solution Sheet
 

Overview

Leading companies across numerous industries use Gurobi’s mathematical optimization solver – in a wide variety of applications – to optimize their supply chain planning, decision making, and operations and keep supply and demand in balance.

With mathematical optimization, you can:

  • Attain visibility and control over your end-to-end supply chain network.
  • React and respond rapidly and effectively to changing conditions and disruptions across your supply chain.
  • Make dynamic, data-driven decisions that optimize your company’s efficiency and profitability.
  • Achieve your business goals by balancing cost and service-level tradeoffs – simultaneously satisfying customer demand and spurring bottom-line growth.
  • Transform your supply chain from a source of costs into a source of competitive advantage.

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.
     

     Learn More  Learn More
  • Gurobi Optimizer Delivers Continuous Innovation
  • Gurobi Optimizer Delivers Responsive, Expert Support
Improvement in Supply Chain Planning Accuracy
1 %
Reduction in Planning Time
1 %

Peek Under the Hood

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

  • Market Sharing

    Market Sharing

    In this example, we’ll show you how to solve a goal programming problem that involves allocating the retailers to two divisions of a company in order to optimize the trade-offs of several market sharing goals. You’ll learn how to create a mixed integer linear programming model of the problem using the Gurobi Python API and how to find an optimal solution to the problem using the Gurobi Optimizer. This model is example 13 from the fifth edition of Model Building in Mathematical Programming by H. Paul Williams on pages 267-268 and 322-324. 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. You may also want to check out the documentation of the Gurobi Python API.

     Learn More
  • Supply Network Design
  • Traveling Salesman

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

No results found.

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.
Learn More

Always Free for Academics

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