Hi,
I'm trying to model my first optimization use case.
The idea is to maximize a company's sales.
To put it simply, I have different sources of data:
* My product list
* My available stock per product
* A list of 3 possible prices per product
* A list of 3 possible quantities per product to match the previous list.
This allows me to simply model that the price fluctuates according to the quantity sold.
I then thought I'd introduce a 2-dimensional binary variable that would allow me to know, for each product, whether the price that has been selected (and therefore indirectly the associated quantity).
My objective is therefore to determine the quantity to be sold per product to maximize my sales.
I have 2 minimum constraints:
* I can't sell more per product than I have in stock.
* I have to make sure that, per product, the proposed solution offers me only one price.
I began by trying to model this last constraint.
But I'm having trouble understanding how to formulate it in a context where there are two dimensions and not just one, as in the example provided on the site?
Any help or advice would be most welcome to unblock these first steps.
Thank you
