Hello and welcome to this lecture, where I am going to present the first case study of the class,
which is related to transportation of products, we can consider a real example of an e-commerce that
needs to loads products on that truck should distribute them to the company branches, as you can see
here, that are 14 products and one truck.
For example, the refrigerator here uses zero point seven five one cubic meters and the price is nine
nine nine point nine.
And you can see this base and the price for each one of them.
The refrigerator see here is the product that uses the most space in the truck.
While this cell phone here is the product that uses the least space in the truck.
Regarding price, this television here is the most expensive product.
However, it also uses a large space in the truck.
We can see that the truck has a maximum capacity.
It is not possible to load all these products on this same trip.
For example, here we have the information.
That's the maximum capacity is only three cubic meters.
Of course, these values are only for the purpose of this lesson, so it will be easier to test the
algorithms.
A truck has a much larger capacity, and the company has many more products.
This some of the space occupied by each product is far behind seven to nine cubic meters, which indicates
that it will not be possible to load the farting products on the truck since four point seventy nine
is greater than three.
Based on this, the purpose of this case is, thirdly, is to find the most expansive products are,
in other words, the most profitable ones and at the same time occupy the maximum space of the truck.
For example, the refrigerator can be less profitable than this cell phone, but it takes up a lot more
space as we can see the values here and here.
The optimal solution in this scenario may be the transportation of the most profitable products.
We have a maximization problem.
That's the goal is to return higher values.
The higher the value, the more profit the company will earn.
This is also a small problem, with only around 16000 solutions and a computational cost.
Just past all of these solutions would not be so high.
However, an e-commerce company has thousands of products and more than one units of each product.
If you have a real data set, there would be easily trillions of combinations so we can apply genetic
algorithms to find the best set of products that we are going to transport in the truck.
Now that you know the problem that we are going to solve in the next lectures, we are going to start
the implementations from scratch.
See you there.