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Say I have some products and their unit price:

Product Name Product Amount Price per unit
A 10 2
B 20 6
... ... ...
Z 30 7

The products are financial instruments so they need not be sold in discrete amounts (i.e i can sell 50% of product A for a total price of 0.5102).

I am given a target total demand and a target average price. Say the total demand is 25 and the target price is 5.

There are several product combination that can satisfy the product demand. E.g, choosing all of Product_A and 75% of Product_B:

$10 + 0.75*20 = 25.$

This results in an average price of $(10*2+15*6)/25 = 4.4$

Or I could use $(5/6)$ of product Z:

$(5/6)*30 = 25$

And this gives me an average price of:

$(25*7)/25 = 7$

Since I can choose any positive amount of each product, I am having a hard time thinking of a way of solving this in python. If I had to use whole amounts of the products I could simply check if the weighted sum adds up to my target value. But with continuous amounts I am not sure how or even if this is possible.

Since I only have two constraints: that the sum of products must equal the demand and the average price must equal the target, I have too many variables for the number of unknowns. So my gut feeling is that this cannot be solved.

I thought that instead of checking for an exact match with the target, I could choose product amounts such that I get as close as possible to my target, but again, I feel like this won't be possible because of the number of unknowns.

Has anyone here worked with a similar problem and could provide some ideas?

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    $\begingroup$ To an optimizer (person), degrees of freedom are good, because that means there's something which can be optimized. $\endgroup$ Aug 1, 2022 at 16:17

1 Answer 1

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You can solve this as a linear program. The variables are how much of each instrument to sell. The sole constraint (other than lower and upper bounds on each variable) is the demand constraint (equality). The objective is to minimize the total price (which will automatically minimize the per unit price). You do not need a constraint involving the target price; either the optimal solution has unit price less than or equal to the target, or it cannot be done.

Update: If the objective is to get as close as possible to the target price $P$ while exactly meeting demand $D,$ you can introduce a variable $z \ge 0$ to represent the deviation in average price and minimize $z$ subject to the supply limits, the demand constraint (equation) and a pair of constraints $$z \ge (T - P)/D$$ and $$z \ge (P - T)/D,$$ where $T$ is a placeholder for the expression giving the total price in terms of the other decision variables. If you want to get as close as possible to the target price $P$ without exceeding it, you can replace the second constraint with $$T \le P\cdot D.$$

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  • $\begingroup$ That's great to hear! I'll try using Pyomo to solve this. Just one question about the use of a single constraint: say I had the same demand (25) but a target price of 10. Surely this should result in a different product mix? But if I just need to satisfy demand with equality, then using the product mix A and 75% of B, would work while having a lower average price. $\endgroup$
    – BenBernke
    Aug 1, 2022 at 16:35
  • $\begingroup$ Yes. I am assuming, based on the original post, that you want to either land on or below the price target. If you want to land as close to the price target as possible, then you would minimize the absolute difference between average price and target, which would entail one new variable and two new constraints. $\endgroup$
    – prubin
    Aug 1, 2022 at 18:17
  • $\begingroup$ Ah of course the objective function should be the difference between the average price and target price! So I want to minimise this difference subject to the demand constraint, upper and lower bound of the products and a positive value for the target price. $\endgroup$
    – BenBernke
    Aug 1, 2022 at 18:34

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