“This used to be a massive manual planning effort. Now, it’s just the press of a button away.”
Philipp MünchData Scientist, Audi
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Your business faces extremely complex challenges. You need to achieve multiple, conflicting objectives simultaneously—amid an ever-changing business landscape and global disruption. That’s why 80% of the world’s leading enterprises turn to Gurobi’s decision intelligence technology.
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Learn MoreGurobi applauds the open-sourcing of NVIDIA’s cuOpt PDLP to help explore faster, more efficient solutions for real-world challenges.
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Mathematical optimization uses the power of math to find the best possible solution to a complex, real-life problem. It can be thought of as a way to make the smartest (and most optimal) decision despite having a multitude of variables and challenges.
Mathematical optimization models contain three components:
1. Objective Function: This is the end goal that you want to achieve.
2. Decision Variables: These represent the items involved that you can control and change in order to reach your objective.
3. Constraints: These are the rules and/or limitations that you must follow.