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.
Gurobi delivers blazing speeds and advanced features—backed by brilliant innovators and expert support.
Gurobi’s powerful MIP algorithm allows you to add complexity to your model to better represent the real world, and still solve your model within the available time.
Our development team includes the brightest minds in decision-intelligence technology--and they're continually raising the bar in terms of solver speed and functionality.
Our PhD-level experts are here when you need them—ready to provide comprehensive guidance and technical support. They bring deep expertise in working with commercial models and are there to assist you throughout the process of implementing and using Gurobi.
Dive deep into sample models, built with our Python API.
This COVID-19 Healthcare Facility Capacity Optimization problem shows you how to determine the optimal location and capacity of healthcare facilities in order to:
Sharpen your mathematical optimization modeling skills with this example, in which you will learn how to select the location of facilities based on their proximity to customers. We’ll demonstrate how you can construct a mixed-integer programming (MIP) model of this facility location problem, implement this model in the Gurobi Python API, and generate an optimal solution using the Gurobi Optimizer. This modeling example is at the intermediate level, where we assume that you know Python and are familiar with the Gurobi Python API. In addition, you should have some knowledge about building mathematical optimization models.
Learn MoreHow can mathematical optimization be used to measure the efficiency of an organization? Find out in this example, where you’ll learn how to formulate an Efficiency Analysis model as a linear programming problem using the Gurobi Python API and then generate an optimal solution with the Gurobi Optimizer. This model is example 22 from the fifth edition of Model Building in Mathematical Programming by H. Paul Williams on pages 278-280 and 335-336. This example is at the intermediate level, where we assume that you know Python and the Gurobi Python API and that you have some knowledge of building mathematical optimization models.
Learn MoreMathematical 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.
85% of the world’s leading companies use mathematical optimization to make optimal business decisions. For example, Air France uses it to build the most efficient schedule for its entire fleet, in order to save on fuel and operational costs, while reducing delay propagation.
Descriptive and predictive analytics show you what has happened in the past, why it happened, and what’s likely to happen next. But to decide what to do with that information, you need human input—which can introduce bias.
With mathematical optimization, you receive a decision recommendation based on your goals, constraints, and variables alone. You can, of course, involve human input when it comes to whether or not to act on that recommendation. Or you can bypass human input altogether and automate your decision-making.
We make it easy for students, faculty, and researchers to work with mathematical optimization.
When you face complex optimization challenges, you can trust our Gurobi Alliance partners for expert services.
Our global team of helpful, PhD-level experts are here to support you—with responses in hours, not days.
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