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Hello and welcome to this lecture, where I am going to present the first case study of the class,

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which is related to transportation of products, we can consider a real example of an e-commerce that

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needs to loads products on that truck should distribute them to the company branches, as you can see

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here, that are 14 products and one truck.

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For example, the refrigerator here uses zero point seven five one cubic meters and the price is nine

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nine nine point nine.

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And you can see this base and the price for each one of them.

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The refrigerator see here is the product that uses the most space in the truck.

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While this cell phone here is the product that uses the least space in the truck.

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Regarding price, this television here is the most expensive product.

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However, it also uses a large space in the truck.

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We can see that the truck has a maximum capacity.

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It is not possible to load all these products on this same trip.

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For example, here we have the information.

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That's the maximum capacity is only three cubic meters.

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Of course, these values are only for the purpose of this lesson, so it will be easier to test the

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algorithms.

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A truck has a much larger capacity, and the company has many more products.

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This some of the space occupied by each product is far behind seven to nine cubic meters, which indicates

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that it will not be possible to load the farting products on the truck since four point seventy nine

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is greater than three.

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Based on this, the purpose of this case is, thirdly, is to find the most expansive products are,

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in other words, the most profitable ones and at the same time occupy the maximum space of the truck.

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For example, the refrigerator can be less profitable than this cell phone, but it takes up a lot more

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space as we can see the values here and here.

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The optimal solution in this scenario may be the transportation of the most profitable products.

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We have a maximization problem.

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That's the goal is to return higher values.

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The higher the value, the more profit the company will earn.

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This is also a small problem, with only around 16000 solutions and a computational cost.

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Just past all of these solutions would not be so high.

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However, an e-commerce company has thousands of products and more than one units of each product.

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If you have a real data set, there would be easily trillions of combinations so we can apply genetic

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algorithms to find the best set of products that we are going to transport in the truck.

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Now that you know the problem that we are going to solve in the next lectures, we are going to start

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the implementations from scratch.

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See you there.