Which Kind of Machine Learning should I use for an Optimization Problem? I have a problem where I would like to apply machine learning. For this purpose I have been learning about the topic and have come across supervised, unsupervised, semi-supervised machine learning techniques.

I know the problem that I want to solve, so unsupervised machine learning doesn't seem to be a fit, because from how I understood, it is used when we don't know what we are looking for.

But I don't have a pre-labelled data set either, because we ourselves don't know if we are operating at the optimum level.

Basically we want to optimize profit, and we have the option of tweaking certain parameters. As an example let's say we want to the ML Algorithm to find the price of a product that would optimize the profit. 

What kind of ML can I apply here? Do you have any recommend frameworks? Any book that I can read to understand this (the books that I have referred to so far have all given examples of classification)?

Adding more context:- The actual problem is to balance a logistics network. There are multiple warehouses and delivery service providers(DSP) (DHL, GLS etc). We have some parameters that define to which warehouse/DSP we want to send the orders(to be packed and delivered). We want to find the optimum parameters that will yield profit. We have already processed data where we can train and validate the algorithm. But we don't know if the current parameters are correct, so I would assume that training the algorithm with the data that we have would not give us an optimum solution (because the labelled targets, i.e. the achieved profit might not be optimum)

With this specific algorithm we are trying to figure out the best warehouse/DSP combination for a given order. While we want to send a parcel to a cheapest warehouse/DSP option, it might be the case that the cheapest warehouse/DSP is out of capacity (limited by manpower at the warehouse or limited by the number of ordered trucks of a given DSP), in which case we might choose to send the order to a non cheapest warehouse, or to send the order the next day. With this solution we don't plan to optimize anything else (that is optimizing how stock is stored/storage costs etc.). The goal is to utilize the warehouse/truck capacity the best way we can. I don't need a full blown solution, but a few keywords/concepts/places to which I can refer would be enough.

PS:- Sorry for the half blown example in the original question. I just wanted to give a simple/similar example so that I can get some keywords, frameworks, books to further study.
You have no existing data where the parameters you mentioned have been tweaked and you then have the resulting profits?

If you don't have any pre-labeled data then you will find it very difficult because the NN will have nothing to learn from.  NNs learn from either the information you give; the inputs and corresponding outputs, or by experimenting with values and then using the result of using those outputs as labels / targets to learn from.