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.
Gurobi delivers blazing speeds and advanced features—backed by brilliant innovators and expert support.
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.
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.
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.Learn More
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.Learn More
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.
Although there are countless ways to use prescriptive analytics, here are some real-world examples, with links to their stories:
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.
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:
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:
With this information, the prescriptive analytics tool can generate a detailed action plan for achieving your goals, given your limitations.
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