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Don’t Go It Alone. Gurobi and Its Partners Provide the Continuum of Support You Need.

While the mathematical optimization field is more than 70 years old, many customers are still learning how to make the most of its capabilities. That’s why, at Gurobi, we have established the Gurobi Alliance partner network—a group of trusted partners who can support you in achieving your optimization goals.

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Unlock the power of optimized decision-making with this online course developed by Gurobi in partnership with Dr. Joel Sokol, professor at Georgia Tech.

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Lyft Optimizes Driver Incentives with Gurobi
By optimizing driver incentives, Lyft can encourage drivers to drive in areas with the highest demand for rides.
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Power and Utilities

Optimize Your Decision-Making

Delicately balance consumer utilization with responsible power generation and distribution.

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Overview

Gurobi allows energy and utility companies to respond to the growing demand for services each year. Optimization enables organizations to delicately balance consumer utilization with responsible management of power generation and distribution. Optimization allows companies to turn data into insight by combining economic, social, and environmental considerations into a single mathematical model. Optimization can also be used to help companies mitigate risk and uncertainty in an increasingly competitive market.

“Our Quant Analysis team looks to optimize our output in both the short and long term. We want to use the least amount of water to generate maximum efficiency and profits.”

Anne Tye
Senior Energy Analyst, Alpiq

“With Gurobi, we get the precision, flexibility, and speed we need to optimize the efficiency of our hydroelectric power plants. ”

Anne Tye
Senior Energy Analyst, Alpiq

“We can change our constraints at any time, to keep up with evolving demands of the electrical market and the characteristics of our complex Alpine power plants.”

Anne Tye
Senior Energy Analyst, Alpiq

“Our Quant Analysis team looks to optimize our output in both the short and long term. We want to use the least amount of water to generate maximum efficiency and profits.”

Anne Tye
Senior Energy Analyst, Alpiq

“With Gurobi, we get the precision, flexibility, and speed we need to optimize the efficiency of our hydroelectric power plants. ”

Anne Tye
Senior Energy Analyst, Alpiq

The Solver That Does More

Gurobi delivers blazing speeds and advanced features—backed by brilliant innovators and expert support.

Unmatched Performance
Continuous Innovation
Responsive, Expert Support

Unmatched Performance
Continuous Innovation
Responsive, 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
Continuous Innovation

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 code is fundamentally parallel—not sequential code that was parallelized—so you can make the most of parallelism and run sequentially.
- We go beyond cutting-edge MIP cutting planes, with new classes of cuts you can find only with Gurobi.
- Our advanced MIP heuristics identify feasible, good quality solutions, fast—where other solvers fall flat.
- Our barrier algorithms fully exploit the features of the latest computer architectures.
- Our APIs are lightweight, modern, and intuitive—to minimize your learning curve while maximizing your productivity.
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Gurobi Optimizer Delivers Responsive, Expert Support
Responsive, Expert Support

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.
- Tap into our team’s deep expertise—from implementation to tuning and more.
- We respond to customer inquiries in hours not days, helping to quickly resolve any issues you’re facing.
- We can help you fit and adapt your mathematical optimization application to your changing requirements.
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Peek Under the Hood

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

Facility Location
Electrical Power Generation
Offshore Wind Farming
Technician Routing & Scheduling

Facility Location
Electrical Power Generation
Offshore Wind Farming
Technician Routing & Scheduling

Facility Location Problem

Facility Location

Facility location problems can be commonly found in many industries, including logistics and telecommunications. In this example, we’ll show you how to tackle a facility location problem that involves determining the number and location of warehouses that are needed to supply a group of supermarkets. We’ll demonstrate how to construct a mixed-integer programming (MIP) model of this problem, implement this model in the Gurobi Python API, and then use the Gurobi Optimizer to find an optimal solution. 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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Electrical Power Generation

Electrical Power Generation

Model: Part 1
Major electric power companies around the world utilize mathematical optimization to manage the flow of energy across their electrical grids. In this example, you’ll discover the power of mathematical optimization in addressing a common energy industry problem: electrical power generation. We’ll show you how to figure out the optimal set of power stations to turn on in order to satisfy anticipated power demand over a 24-hour time horizon. This model is example 15 from the fifth edition of Model Building in Mathematical Programming by H. Paul Williams on pages 270 – 271 and 325 – 326. 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.
Model: Part 2
This example (which is an extension of the earlier ‘Electrical Power Generation 1 ‘ example) will teach you how to choose an optimal set of power stations to turn on in order to satisfy anticipated power demand over a 24-hour time horizon – but gives you the option of using hydroelectric power plants to satisfy that demand. This model is example 16 from the fifth edition of Model Building in Mathematical Programming by H. Paul Williams on pages 271-272 and 326-327. 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.
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Offshore Wind Farming

Offshore Wind Farming

In this example, you’ll learn how to solve an offshore wind power generation problem. The goal of the problem is to figure out which underwater cables should be laid to connect an offshore wind farm power network at a minimum cost. We’ll show you how to formulate a mixed-integer programming (MIP) model of this problem using the Gurobi Python API and then find an optimal solution to the problem using the Gurobi Optimizer. 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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Technician Routing and Scheduling Problem

Technician Routing & Scheduling

Try this modeling example to discover how mathematical optimization can help telecommunications firms automate and improve their technician assignment, scheduling, and routing decisions in order to ensure the highest levels of customer satisfaction. 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 have some knowledge about building mathematical optimization models. To fully understand the content of this notebook, you should be familiar with object-oriented-programming.
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Frequently Asked Questions

What is prescriptive analytics?

Prescriptive analytics tools like mathematical optimization help you make decisions based on your real-world business goals (“objectives”) and limitations (“constraints.”) This can be especially useful when you’re facing a business problem with multiple, conflicting goals (such as cutting spending while increasing production) and multiple constraints (such as time, distance, product availability).

Learn more about prescriptive analytics in our article, “What is Prescriptive Analytics?”
What is the difference between predictive analytics and prescriptive analytics?

Predictive analytics seeks to identify patterns in data to forecast future events, such as predicting cyberattacks or imminent machine failures. Prescriptive analytics, on the other hand, utilizes mathematical modeling to guide decisions based on real-world objectives and constraints, such as minimizing costs or managing raw material inventory.
While predictive analytics tells you what might happen, prescriptive analytics provides actionable recommendations on how to achieve specific goals, given certain limitations.

Learn more about the difference in our article, “Predictive Analytics vs. Prescriptive Analytics.”
What are some examples of prescriptive analytics in the real world?

In the real world, prescriptive analytics has diverse applications, including transportation providers like Air France and Uber using it to create optimal routing, staffing, and maintenance plans. Professional sports leagues, such as the National Football League, plan their game schedules using prescriptive analytics. Additionally, manufacturers utilize prescriptive analytics to plan and manage the procurement, production, and distribution of their products, aligning decisions with real-world goals and constraints.

Learn more about examples in our article, “Examples of Prescriptive Analytics.”
Can I improve my machine learning applications by applying optimization?

Yes! By using machine learning predictions as valuable input for mathematical optimization solutions, or conversely, using mathematical optimization to inform machine learning predictions, you can leverage the problem-solving power of mathematical optimization to enhance machine-learning applications.
Learn more in our article, “Improving Machine Learning Applications with Prescriptive Analytics.”
How can prescriptive and predictive analytics work together?
Say you were planning a trip. Predictive analytics can predict what you may encounter along your journey (weather, traffic, engine trouble), and prescriptive analytics can, given those predictions, identify the route that best helps you achieve your goals (fastest, cheapest, safest route), given your constraints (time, budget, speed limits).
Here are some additional examples:
- Use predictive analytics to predict supply chain issues, and use prescriptive analytics to identify the least costly way to reroute shipments.
- Use predictive analytics to predict cyberattacks before they happen, and use prescriptive analytics to identify the right investigators based on cost and skill.
- Use predictive analytics to predict imminent machine failure, and use prescriptive analytics to identify the best time to shut down the production line.
- Use predictive analytics to predict customer likelihood to buy more with targeted offers, and use prescriptive analytics to identify how many discount coupons to offer, in order to maximize revenue.
Learn more in our article, “How Can Prescriptive and Predictive Analytics Work Together?”

Additional Insight

Advanced Microgrid Solutions: Reducing Customer Electric Bills

Alpiq: Keeping Hydroelectric Power Flowing

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