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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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.
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.
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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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