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.

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

    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.

    • Public benchmarks consistently show that Gurobi finds proven-optimal solutions faster than competing solvers.
    • The performance gap grows as model size and difficulty increase.
    • Gurobi has a history of making continual improvements across a range of problem types.
    • 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

    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.

  • 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.

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

Prescriptive analytics tools like Gurobi equip you to make optimal business decisions in the midst of extreme complexity. For example, you can decide exactly which products to produce, in which quantities, in which order, and in which facilities, while taking into consideration production minimums, manufacturing time and costs, raw material inventory, and inventory capacity, in order to minimize total product costs.

Prescriptive analytics is one of the three main types of data analytics, as show below:

Three Primary Types of Analytics

Predictive analytics tools—like machine learning, statistical models, and simulations—seek to find patterns in data, in order to predict what might happen in the future. For example, you can estimate industry growth, raw material pricing, revenue or profit growth, or changes in demand by product line.

Predictive analytics looks for patterns in historical data and uses those patterns to make predictions about the future. Prescriptive analytics can help you find the optimal way to achieve your business goals, given those predictions.

For example, if you were planning a trip, machine learning can predict what you may encounter along your journey (weather, traffic, engine trouble). But with mathematical optimization, you can take those predictions into consideration, as well as your goals (fastest, cheapest, safest route) and constraints (time, budget, speed limits), and identify the single best road you should take.

 

One popular prescriptive analytics use case is marketing campaign optimization—enabling you to offer the right product to the right person at the right time, so you can maximize your marketing ROI while satisfying your business constraints.

Another example is production planning:

Your goals/objectives:

  • Minimize product costs

Your constraints:

  • Minimum production of a given product
  • Required manufacturing time and cost of a particular machine
  • Raw material inventory
  • Finished goods inventory capacity

Your decision variables (the questions you need answered):

  • In which order should we produce which products?
  • In which manufacturing facilities?
  • On what product lines?
  • In what quantities?

Other examples include inventory optimization, location planning, portfolio management, vehicle routing, workforce scheduling, and more.

Additional Insight

Guidance for Your Journey

Gurobi: Always Free for Academics

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

Trusted Partners, at Your Service

When you face complex optimization challenges, you can trust our Gurobi Alliance partners for expert services.

We’ve Got Your Back

Our global team of helpful, PhD-level experts are here to support you—with responses in hours, not days.

What's
New at Gurobi

News
Gurobi 10.0 Delivers Blazing-Fast Speed, Innovative Data Science Integration, and an Enterprise Development and Deployment Experience
Latest release enables data professionals to easily integrate machine learning models into optimization models to solve new types of problems.
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Event
Webinar: What’s New in Gurobi 10.0
In this webinar, attendees will get a first look at our upcoming product release, Gurobi 10.0. We will summarize the performance improvements and highlight some of the underlying algorithmic advances, such as the network simplex algorithm, enhancements in concurrent LP, and optimization based bound tightening.
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new content
Cost Savings & Business Benefits for Gurobi Customers
2022 Total Economic Impact™ Study Reveals A 518% ROI with Gurobi
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