If it seems like decision-making has become more difficult lately, you’re not alone. In fact, Gartner reports that 65% of decisions are more complex than they were two years ago.
You need to make decisions that lead to optimal business outcomes. That’s hard enough. But you have to do so in the midst of extreme complexity—such as global change, regulations, fluctuating energy costs, green energy targets, and supply chain disruptions.
But there’s good news. With Gurobi’s decision optimization technology, you can let the AI take every detail into consideration—something humans alone simply cannot do—and identify your best possible course of action.
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.
Deploy your model where and how you want. One user can solve a single model on one machine, or many users can solve multiple models using many machines. You can also solve models locally or on an internal or public cloud.
Model your problem the way that works best for you—including LP and MILP and convex and non-convex QP, MIQP, QCP, and MIQCP, as well as SOCP and bi-linear problems.
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.
85% 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.
We make it easy for students, faculty, and researchers to work with mathematical optimization.
When you face complex optimization challenges, you can trust our Gurobi Alliance partners for expert services.
Our global team of helpful, PhD-level experts are here to support you—with responses in hours, not days.
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