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Healthcare

Real-World Optimization Impact


Resolve patient healthcare issues faster, while improving resource utilization and employee satisfaction, and reducing operating costs.

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Overview

With mathematical optimization technologies, healthcare providers can ensure the timely resolution of healthcare issues for patients, improve resource utilization and employee satisfaction, and reduce operating costs—to safeguard the health of society as well as the sustainability of the healthcare system.

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.

  • COVID-19 Facility Capacity Optimization

    COVID-19 Facility Capacity

    This COVID-19 Healthcare Facility Capacity Optimization problem shows you how to determine the optimal location and capacity of healthcare facilities in order to:

    • Satisfy demand from COVID-19 patients for treatment,
    • Minimize the cost of opening temporary facilities for healthcare providers, and
    • Predict the allocation of COVID-19 patients from a specific county to a specific healthcare facility.
    This modeling example is at the beginner level, where we assume that you know Python and that you have some knowledge of how to build mathematical optimization models.

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  • Customer Assignment
  • Efficiency Analysis

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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