Need advice on the best way to represent the below data to be fed into an ML algorithm (yet to decided on)
This is from the online order processing domain. An order consists of a set of variable number of items. Each item can be located in different warehouses, again this is a variable number. The entire order with multiple items and items with multiple warehouses per item, needs to be processed as one training sample. The goal is to learn a function that outputs the warehouses from which the items can be picked under some rules/conditions to minimize processing costs. The number of items can run in millions and stores in 1000's.
I've been looking at representing these as permutation invariant sets - is there a simpler way or is that the right way to go about it ?