Improvement to Time-Horizon Supply Chain Planning Capacity
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With Gurobi, mining organizations can identify the optimal way to balance the utilization of precious natural resources with expectations for responsible use and safety. From the management of manufacturing systems, chemical processes, and mechanical systems, to the workforce that supports these operations—Gurobi makes it possible to make the right decisions, every step of the way.
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Dive deep into sample models, built with our Python API.
See how mathematical optimization can make your revenues and profits soar in this example, where we’ll show you how an airline can use the AI technology to devise an optimal seat pricing strategy. You’ll learn how to formulate this Yield Management Problem as a three-period stochastic programming problem using the Gurobi Python API and solve it with the Gurobi Optimizer. This model is example 24 from the fifth edition of Model Building in Mathematical Programming by H. Paul Williams on pages 282-284 and 337-340. This modeling example is at the advanced level, where we assume that you know Python and the Gurobi Python API and that you have advanced knowledge of building mathematical optimization models. Typically, the objective function and/or constraints of these examples are complex or require advanced features of the Gurobi Python API.
Learn MoreIn this example, you’ll discover how mathematical optimization can be used to address a macroeconomic planning problem that a country may face. We’ll show you how to model and solve an input-output problem encompassing the interrelationships between the different sectors of a country’s economy. This model is example 9 from the fifth edition of Model Building in Mathematical Programming by H. Paul Williams on pages 263-264 and 316-317. 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 should have some knowledge about building mathematical optimization models.
Learn MoreIn this example, we’ll demonstrate how you can use mathematical optimization to optimize the output of a refinery. You’ll learn how to generate an optimal production plan that maximizes total profit, while taking into account production capacity and other restrictions. More information on this type of model can be found in example # 6 of the fifth edition of Modeling Building in Mathematical Programming by H. P. Williams on pages 258 and 306 – 310. 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 should have some knowledge about building mathematical optimization models.
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Mathematical optimization uses the power of math to find the best possible solution to a complex, real-life problem. You input the details of your problem—the goals you want to achieve, the limitations you’re facing, and the variables you control—and the mathematical optimization solver will calculate your optimal set of decisions.
80% of the world’s leading companies use mathematical optimization to make optimal business decisions. For example, Air France uses it to build the most efficient schedule for its entire fleet, in order to save on fuel and operational costs, while reducing delay propagation.
Descriptive and predictive analytics show you what has happened in the past, why it happened, and what’s likely to happen next. But to decide what to do with that information, you need human input—which can introduce bias.
With mathematical optimization, you receive a decision recommendation based on your goals, constraints, and variables alone. You can, of course, involve human input when it comes to whether or not to act on that recommendation. Or you can bypass human input altogether and automate your decision-making.
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