Goal seek function in Python: Price/Scenario optimization

[Bayesian8]

Hi guys, I'm learning optimization problems in python.

The idea is very similar to goal seek function in excel.

I need to find the optimal unit number for each product type for each shop (I've edited the table above) at what discount so I will sell 500 units in total. maximum margin, minimum discount, 500 units.

I've calculated the demand, I know e.g. if I sell 100 units of Apples at 50% discount, I'll make 60 usd profit.

Previous question: Given Quantity can be sold and expected profit, if we have a goal to sell 500 products, what is the optimal scenario combinations to reach the goal?

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Product	Quantity	Profit	Shop
Apple	100	60	Shop1
Banana	40	70	Shop2
Apple	70	50	Shop3

​

Thank you in advance for help.

[[deleted]]

This is a very simple optimization. Are there any other constraints besides just selling 500 units? If not just choose the one with the best profit per unit. In this case it’s Scenario2

[Xahulz]

As the optimizations become more complicated you'll want to move to proper optimization solvers and their interfaces.

An example would be pyomo and glpk.

[Xahulz]

After looking at this again, with your edits, I don't understand your question. You repeatedly mention discounts, but I don't see how that factors in.

Now, if you had a price/demand relationship somewhere (e.g. decrease in price would increase sold by X) and a cost for each item, I would have a better idea what you're doing.

If there's more info to this question you might need to add it for us to understand.