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

    Public benchmarks consistently show that Gurobi finds both feasible and proven optimal solutions faster than competing solvers. With our powerful MIP algorithm, 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

    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

  • What is Prescriptive vs. Predictive Analytics?

    Prescriptive analytics tools help you make decisions based on your real-world 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).

    Predictive analytics tools seek to find patterns in data, in order to predict what might happen in the future. For example, predictive analytics can predict who will launch which cyberattack, which experiments are more likely to prove the hypothesis, imminent machine failure, supply chain issues, infrastructure maintenance needs, and price movements—all before they happen.

  • What Is an Example of Prescriptive Analysis?

    Although there are countless ways to use prescriptive analytics, here are some real-world examples, with links to their stories:

    • Transportation providers, such as Air France, Swissport, and Uber, use prescriptive analytics to create optimal routing, staffing, and maintenance plans.
    • Professional sports leagues, including the National Football League and Beko BBL, plan their game schedules using prescriptive analytics.
    • Manufacturers use prescriptive analytics to plan and manage the procurement, production, and distribution of their products.
  • What Is the Goal of Prescriptive Analytics?

    Prescriptive analytics tools provide a detailed set of recommendations for how you can best achieve your goals, given your limitations. Although you can use it to automate decision-making, you can use it to inform your traditional decision-making processes. Its ability to explore what-if scenarios can be particularly helpful.

  • How Can Prescriptive Analytics Be Used with Predictive Analytics?

    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.

     

  • What Is an Advantage of Prescriptive Analytics?

    Prescriptive analytics doesn’t rely on historical data—which means you can make decisions for the future, even when it doesn’t look like your past. To use prescriptive analytics, you need to know three things:

    • The goals you need to achieve (“objectives”)
      • Such as minimizing product costs
    • The limitations you’re facing (“constraints”)
      • Such as minimum production of a given product, required manufacturing time and cost of a particular machine, raw material inventory, and finished goods inventory capacity
    • The questions you’re asking (“decision variables”)
      • Such as, “In which order should we produce which products?”, “In which manufacturing facilities?”, “On what product lines?”, and “In what quantities?”

    With this information, the prescriptive analytics tool can generate a detailed action plan for achieving your goals, given your limitations.

Additional Insight

Advanced Microgrid Solutions: Reducing Customer Electric Bills

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