this post was submitted on 17 Apr 2024
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That bin packing is an NP-hard problem is more reason for using heuristics like ml, it means that calculating an exact answer quick explodes to unfeasible amounts of computation so using a far more efficient ml solution to give a probablistic answer makes complete sense.
Using ML to research potentially more efficient, in a BigO sense, packing algorithms makes sense.
But that isn't what they are doing.
They are using image recognition and text analysis to identify the product being packed, and then adjusting the packaging requirements, e.g. more protection = greater volume.
The point I'm making is that they already know what the item is, because inventory codes, so doing visual checks is pointless. They should already know the packing instructions for fragile, etc, items as these are provided by the manufacturer and have already been proven valid by virtue of the product leaving the factory and getting to the Amazon warehouse.
If amazon are ignoring those instructions - and it sounds like they are - then that is a problem they are creating for themselves.
Fitting the items in to the box is still the same problem domain as is taught to first year CompSci students, and is NP-complete. First Fit is extremely efficient when dealing with a relatively small number of items, while optimal solutions are NP-hard, the performance first fit is O(n log n) so not great but not terrible either. Given the myriad combination of item and box size, I'd expect there is a decent amount of triage which can be done and identify "easy to pack" orders (1 or 2 items, no special requirements) which would be essentially a table lookup with O(1) performance.
There are many better algorithms than first fit, I'm just using that as a single example because the point is the same across all of them.