Mathematical Optimization VS Machine Learning: How to choose your AI problem-solving tool?

Mathematical Optimization and Machine Learning are two main technologies allowing on one side to generate solutions for complex decisions and on the other to predict actions to guide future decisions. In a recent article published by Forbes, Edward Rothberg (CEO and Co-founder of Gurobi) points out 4 main differences between Mathematical Optimization and Machine Learning related to the type of analytics*, the applications, the adaptability and the maturity.
SCM software for decision making optimization
Figure 1. Machine Learning VS Mathematical Optimization

*Check our previous article about Prescriptive Analytics to learn more!

How does Mathematical Optimization improve your problem resolution? Using Mathematical Optimization technologies, a tailored application solver is able to consider multiple levels of decisions to ensure a systemic operational performance. Optimizing several decision challenges simultaneously, a comprehensive solver increases profitability at a global scale, to make more while consuming less.

When Machine Learning technologies reach its limits handling highly complex business problems, Mathematical Optimization technologies take over in complementary ways to achieve the best business outcomes.

Discover more on the Forbes article!

Published on 25/10/2021.
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