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What is the optimal production schedule to maximize equipment utilization? The best stacking configuration to transport orders? The optimal schedule for our workforce? Which natural ingredient blend should be used to lower costs? How to design our supply chain network to lower carbon emissions? These are just a few examples that can be answered by applying mathematical optimization. One essential characteristic of a mathematical optimization problem is that it involves a set of variables that interact in complex ways, which makes it impossible to keep track of all the interactions and potential outcomes using tools like spreadsheets.

Adding mathematical optimization to your AI toolbox

Optimization and Modeling

Mathematical optimization describes a business problem in a set of formulas, or so-called equations, and then solving those equations to maximize business outcomes. Such equations are a generic representation and can therefore be optimized over and over again using various data sets (e.g. input demand scenarios) as well as objectives such as max revenue, lowest cost, and service level. Each result is a best case scenario that can be evaluated by the user and applied in the business to generate the impact.

Building Resilience

We live in an unpredictable environment and cannot control many of the events that affect our businesses. However, we can certainly equip ourselves with the right tools and technology to make ourselves more resilient. Evaluating multiple scenarios using the power of mathematical optimizations not only reduces the risks posed by unexpected events like a pandemic, but also helps businesses leverage the strategic competitive advantages that resilience and preparedness provide.

AIMMS low code platform

Driven by our purpose, we believe that every organization should have access to the power of mathematical optimization. That is why we empower regular business users (decision-makers) to analyze scenarios through a highly interactive user interface, while developers can securely build, deploy and maintain models and applications the low code way. AIMMS is considered to be a prescriptive analytics technology.

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Explore the benefits of mathematical optimization and discover a low code approach to develop and deploy optimization apps.

There’s no single solution for all the challenges in the world today, but AI offers an important arsenal of tools that can improve decision making and business outcomes. Mathematical optimization is one of these tools.

How Mathematical Optimization Complements Machine Learning

Machine Learning is good at mimicking the current (business) decisions.

Mathematical Optimization looks for better decisions.

Machine Learning needs real big data, not just 10.000 records in a database, but millions of records.

Optimization can work with much less data, because you have the option to specify logic mathematically.

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Machine Learning has difficulties in dealing with exceptions (think of Tesla’s not recognizing police cars at a traffic accident).

Optimization is specifically trained to deal with these exceptions as constraints are explicitly modeled.

Machine Learning can provide valuable business insights, predicting outcomes and forecasts.

Mathematical Optimization allows to make trade-off decisions through running what-if scenarios.