With mathematical optimization technologies, healthcare providers can ensure the timely resolution of healthcare issues for patients, improve resource utilization and employee satisfaction, and reduce operating costs—to safeguard the health of society as well as the sustainability of the healthcare system.
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Dive deep into sample models, built with our Python API.
This COVID-19 Healthcare Facility Capacity Optimization problem shows you how to determine the optimal location and capacity of healthcare facilities in order to:
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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