In this program, you will learn why mathematical optimization (also known as mathematical programming) is the primary technology for doing prescriptive analytics, and how to use it to solve prescriptive analytics problems across a variety of industries.
Not sure what prescriptive analytics is about? Check out this blog post: https://www.mipwise.com/blogs/analytics-driven-decision-making
But don't think that mathematical optimization is only for optimization tasks. In many cases, we just need to find a solutions that meet a list of requirements (puzzles are a good example of this). There are also dedicated technologies for solving these types of problem. Constraint Programing is one of them as we will discuss later.
In fact, mathematical optimization can be used to model and solve an incredibly large range of decision-making problems as we will see in this program.
This technology is particularly useful for complex problems where lots of interconnected decisions must be made. The Digit Tracking puzzle illustrates this very well. If you place the number 3 in the cell with label 2, then the number 2 must be placed in three distinct cells, and that will imply that three other numbers will have to appear twice, and so on.
In some other cases, the decision-making problems is not so complex in the sense that decisions are highly dependent on each other, but decisions must be synchronized to achieve certain requirements. For example, in the Diet Problem, we must decide how much to buy of each food while accounting for the minimum amount of total calories in the diet.
In the next section you will learn about mathematical formulation.