Understanding Mathematical Optimization and Modeling
The benefits of using a low code platform for app creation and deployment
What is mathematical optimization and how does it compare to other types of analytics? Which tool should you use to develop optimization applications and scale them across the business? What are best practices for successful adoption? Find out in this guide.
For decades, operations research professionals have been applying mathematical optimization to address challenges in the field of supply chain planning, manufacturing, energy modeling and logistics. Today, a growing group of analytics teams and data scientists are adopting mathematical optimization to support business decision-making in a wide range of industries. Learn all about this AI technique and how it can help your organization.
This Guide is ideal if you:
- Want to understand the concept of mathematical optimization
- Want to find out where optimization falls in the broader AI and business analytics spectrum
- Want a high level understanding of typical use cases for mathematical optimization
- Are evaluating tools and implementation approaches
- Are curious about a low code approach for optimization app development and deployment
Mathematical optimization, applied
Optimization complements other AI techniques like machine learning. It enriches the analytics toolbox and is a highly adaptable tool that helps you improve decision making in a broad range of areas and across industries.
The benefits of a low code approach
AIMMS offers a mature and flexible toolset that empowers you to optimize almost any business problem. Our low code platform enables you to develop and deploy mathematical optimization apps at speed. For over 30 years, decision makers in leading organizations have used AIMMS to analyze and compare different scenarios and make optimal decisions with confidence.