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.
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.
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 MoreTry 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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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:
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:
Your constraints:
Your decision variables (the questions you need answered):
Other examples include inventory optimization, location planning, portfolio management, vehicle routing, workforce scheduling, and more.
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