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Sports

Make an Optimal Impact


Identify the most exciting—and most fair—matchup schedule possible, while taking all of your constraints into account.

 

Overview

Optimization in sports scheduling can be considered as much art as science in combining a fascinatingly varied amount of data into a schedule that impacts players and audiences from around the world each season. Gurobi provides planners with the decision intelligence insights they need to evaluate any given possible schedule, identify what they want to change, and see the resulting effects until the optimal end result is achieved.

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.
     

  • 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.

  • Manpower Planning

    Manpower Planning

    Staffing problems – which require difficult decisions about the recruitment, training, layoffs, and scheduling of workers – are common across a broad range of manufacturing and service industries. In this example, you’ll learn how to model and solve a complex staffing problem by creating an optimal multi-period operation plan that minimizes the total number of layoffs and costs. More information on this type of model can be found in example #5 of the fifth edition of Model Building in Mathematical Programming by H. Paul Williams on pages 256 – 257 and 303 – 304. 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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  • Marketing Campaign Optimization
  • Workforce Scheduling

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

German First Division Basketball League Scheduling

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