15.4 Integer Optimization Summary

In this lecture we’ve covered how to use linear optimisation to solved integer-valued optimisation problems, where some of the decision variables are constrained to be integers, or even constrained to be binary variables (which can be used to model yes/no decisions).

We’ve also seen how to transform logical constraints into linear constraints using binary variables. And we’ve also seen worked examples of problems with 10+ decision variables – these problems scale up very fast!

The output of these problems can be very useful in helping managers make optimal decisions based on the data, and is the whole objective behind prescriptive analytics.