This crate solves the problem of "fitting smaller boxes inside of a larger box" using a three dimensional fitting algorithm.
The algorithm leverages a First Fit Decreasing greedy strategy, which some rotational optimizations.
use bin_packer_3d::bin::Bin; use bin_packer_3d::item::Item; use bin_packer_3d::packing_algorithm::packing_algorithm; let deck = Item::new("deck", [2.0, 8.0, 12.0]); let die = Item::new("die", [8.0, 8.0, 8.0]); let items = vec![deck.clone(), deck.clone(), die, deck.clone(), deck]; let packed_items = packing_algorithm(Bin::new([8.0, 8.0, 12.0]), &items); assert_eq!(packed_items, Ok(vec![vec!["deck", "deck", "deck", "deck"], vec!["die"]]));
This algorithm solves a constrained version of the 3D bin packing problem. As such, we have the following limitations:
The items we are packing, and the bins that we are packing them into, are limited to cuboid shapes
As an NP-Hard problem, this algorithm does not attempt to find the optimal solution
The algorithm leverages a rotational optimization when packing items which are less than half the length of a bin's side, as proposed in the paper titled "The Three-Dimensional Bin Packing Problem" (Martello, 1997): https://www.jstor.org/stable/pdf/223143.pdf, page 257
A struct representing the dimensions of the bin, which will be used for packing.
Defines an Error type and a Result type, which can be raised from the packing algorithm.
A struct representing the items we'll be packing into the bin.
Defines the function that will be used for our packing algorithm.