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Gurobi decision intelligence technology can help semiconductor manufacturers manage real-time scheduling and production needs in order to increase operational efficiency, reduce cycle time, and allow for new product introductions.
Gurobi delivers blazing speeds and advanced features—backed by brilliant innovators and expert support.
With our powerful algorithms, you can add complexity to your model to better represent the real world, and still solve your model within the available time.
Dive deep into sample models, built with our Python API.
In 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.
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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.
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