In our enterprise, Mixed integer Programming (MIP) was one of the most widely used and powerful techniques, long before AI. But there are lots of tricks to make it really applicable. Its still very useful. Here a useful introduction with links to implementation systems. Also makes a very good point about understanding how the structure of the problem is important. And efficiency is crucial. Also this is one space where maturing quantum methods are likely to become very useful. By necessity is technical.
A comprehensive study of Mixed Integer Programming with JuMP on Julia (Part 1)
Some basics of Linear/Mixed Integer Programming & How to use a heuristic callback inside a MIP solver.
By Ouaguenouni Mohamed in TowardsDataScience
Introduction
One of the primary purposes of the computer sciences and operation research is to solve problems efficiently; problem-solving is a field where we often find very “ad-hoc” methods of resolution, they can be efficient, but they rely on some specific properties of the problem which are not necessarily easy to notice.
In this series of posts, we will introduce and discover a very versatile and generic way of thinking and of solving a wide variety of problems, and this introduction will occur on three sides:.... '
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