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The importance of Mathematical Optimization

Mathematical optimization ((also called mathematical programming or prescriptive analytics) is often applied in taking optimal decisions on how to deploy resources while maximizing efficiency and unlock new opportunities for growth. However, there are many more ways to apply mathematical optimization. One essential characteristic of a mathematical optimization problem is that it involves a set of decisions that interact in complex ways, which makes it impossible to keep track of all the interactions an all the potential outcomes through for example a spreadsheet.

Optimization and Modeling

Mathematical optimization and specifically the modeling of mathematical programs is about describing a business problem in a set of formulas, or so-called equations. 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.

Complements Machine Learning

Mathematical optimization can be used to quickly address a wide variety of complex problems, and complements other AI techniques like machine learning to enrich the analytics toolbox. Operations Research groups, have been applying mathematical optimization for decades to support complex challenges in the field of supply chain planning, manufacturing, energy modeling and logistics. Nowadays, a growing group of analytics teams and data scientists are adopting mathematical optimization to support business decision-making across a wide range of industries.

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 build, deploy and maintain models and applications the low-code way. AIMMS is considered to be a prescriptive analytics technology.

Adding mathematical optimization to your AI toolbox

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.